reconciling event-based knowledge through rdf2vec
TRANSCRIPT
Reconciling Event-Based Knowledge through RDF2VEC
Mehwish Alam12 Diego Reforgiato Recupero23 Misael Mongiovi4Aldo Gangemi12 Petar Ristoski5
1 Universite Paris 13 Paris France2 ST-Lab National Research Council (CNR) Rome Italy
3 University of Cagliari Cagliari Italy4 National Research Council (CNR) Catania Italy5 University of Mannheim Mannheim Germany
21stOctober 2017Hybrid Statistical Semantic Understanding and Emerging Semantics ISWC 2017
1 18
Knowledge Reconciliation
Figure Overall Process of Knowledge Reconciliation [Mongiovı et al 2016]
Why Knowledge Reconciliation
Text Summarization
Document Similarity
Generating Textual Analytics
Existing Tool ndash MERGILO
Graph Compression
Graph Alignment
Uses String matching and Word Similarity
Our Goal
Introduce similarities effectively using event information represented as Frames andRoles
Use background knowledge concerning event information2 18
Method
Figure Pipeline for Event-Based Knowledge Reconciliation
3 18
Framester [Gangemi et al 2016]
Figure Framester Factual-Linguistic Linked Data Hub Blue green orange yellow and greycolors represent role-oriented lexical resources wordnet-like lexical resources fact-oriented dataontology schemas and topic models respectively
4 18
Example Framester Frame Graph
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Figure A fragment of FrameNet-OWL graph dotted lines represent subFrameOf relation andsolid lines represent the inheritsFrom relation as defined in FrameNet-OWL
5 18
RDF Graph Based Frame Embeddings ndash RDF2Vec[Ristoski and Paulheim 2016]
Method
Word2Vec converts raw text into vector representations
RDF2Vec converts a graph into a sequence of nodes and edges
Models
Continuous Bag of Words
Given a context of next and previous words as input the central word is predicted
Example Capital Austria Ntilde Vienna
Skip-Gram
Given a word its context is predicted
Example Vienna Ntilde Capital Austria
Representing RDF Graphs into a sequence of nodes and edges
Graph Walks
Weisfeiler-Lehman Subtree RDF Graph Kernels
6 18
Graph Walks
Method
Define a depth d eg d ldquo 3
Perform walks of specific depth d to generate sequences of nodes and edges
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Sequences Generated
Event Ntilde inheritsFrom Ntilde Objective Influence Ntilde inheritsFrom Ntilde Transitive Action
Intentionally affect Ntilde inheritsFrom Ntilde Invasion Scenario Ntilde subFrameOf Ntilde Conquering
7 18
Weisfeiler-Lehman Subtree RDF Graph Kernels
e a
b
c d
eb abcd
baedc
cab dab
abcd Ntilde f
eb Ntilde h
baedc Ntilde g
dab Ntilde i
cab Ntilde j
h f
g
j i
Generated Sequences
bNtildegNtildej bNtildegNtildei bNtildegNtildef bNtildegNtildeh bNtildegNtildejNtildef
aNtildefNtildeg aNtildefNtildej aNtildefNtildei aNtildefNtildegNtildeh
8 18
Continuous Bag of Words and Skip-gram Models
9 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Knowledge Reconciliation
Figure Overall Process of Knowledge Reconciliation [Mongiovı et al 2016]
Why Knowledge Reconciliation
Text Summarization
Document Similarity
Generating Textual Analytics
Existing Tool ndash MERGILO
Graph Compression
Graph Alignment
Uses String matching and Word Similarity
Our Goal
Introduce similarities effectively using event information represented as Frames andRoles
Use background knowledge concerning event information2 18
Method
Figure Pipeline for Event-Based Knowledge Reconciliation
3 18
Framester [Gangemi et al 2016]
Figure Framester Factual-Linguistic Linked Data Hub Blue green orange yellow and greycolors represent role-oriented lexical resources wordnet-like lexical resources fact-oriented dataontology schemas and topic models respectively
4 18
Example Framester Frame Graph
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Figure A fragment of FrameNet-OWL graph dotted lines represent subFrameOf relation andsolid lines represent the inheritsFrom relation as defined in FrameNet-OWL
5 18
RDF Graph Based Frame Embeddings ndash RDF2Vec[Ristoski and Paulheim 2016]
Method
Word2Vec converts raw text into vector representations
RDF2Vec converts a graph into a sequence of nodes and edges
Models
Continuous Bag of Words
Given a context of next and previous words as input the central word is predicted
Example Capital Austria Ntilde Vienna
Skip-Gram
Given a word its context is predicted
Example Vienna Ntilde Capital Austria
Representing RDF Graphs into a sequence of nodes and edges
Graph Walks
Weisfeiler-Lehman Subtree RDF Graph Kernels
6 18
Graph Walks
Method
Define a depth d eg d ldquo 3
Perform walks of specific depth d to generate sequences of nodes and edges
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Sequences Generated
Event Ntilde inheritsFrom Ntilde Objective Influence Ntilde inheritsFrom Ntilde Transitive Action
Intentionally affect Ntilde inheritsFrom Ntilde Invasion Scenario Ntilde subFrameOf Ntilde Conquering
7 18
Weisfeiler-Lehman Subtree RDF Graph Kernels
e a
b
c d
eb abcd
baedc
cab dab
abcd Ntilde f
eb Ntilde h
baedc Ntilde g
dab Ntilde i
cab Ntilde j
h f
g
j i
Generated Sequences
bNtildegNtildej bNtildegNtildei bNtildegNtildef bNtildegNtildeh bNtildegNtildejNtildef
aNtildefNtildeg aNtildefNtildej aNtildefNtildei aNtildefNtildegNtildeh
8 18
Continuous Bag of Words and Skip-gram Models
9 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Method
Figure Pipeline for Event-Based Knowledge Reconciliation
3 18
Framester [Gangemi et al 2016]
Figure Framester Factual-Linguistic Linked Data Hub Blue green orange yellow and greycolors represent role-oriented lexical resources wordnet-like lexical resources fact-oriented dataontology schemas and topic models respectively
4 18
Example Framester Frame Graph
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Figure A fragment of FrameNet-OWL graph dotted lines represent subFrameOf relation andsolid lines represent the inheritsFrom relation as defined in FrameNet-OWL
5 18
RDF Graph Based Frame Embeddings ndash RDF2Vec[Ristoski and Paulheim 2016]
Method
Word2Vec converts raw text into vector representations
RDF2Vec converts a graph into a sequence of nodes and edges
Models
Continuous Bag of Words
Given a context of next and previous words as input the central word is predicted
Example Capital Austria Ntilde Vienna
Skip-Gram
Given a word its context is predicted
Example Vienna Ntilde Capital Austria
Representing RDF Graphs into a sequence of nodes and edges
Graph Walks
Weisfeiler-Lehman Subtree RDF Graph Kernels
6 18
Graph Walks
Method
Define a depth d eg d ldquo 3
Perform walks of specific depth d to generate sequences of nodes and edges
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Sequences Generated
Event Ntilde inheritsFrom Ntilde Objective Influence Ntilde inheritsFrom Ntilde Transitive Action
Intentionally affect Ntilde inheritsFrom Ntilde Invasion Scenario Ntilde subFrameOf Ntilde Conquering
7 18
Weisfeiler-Lehman Subtree RDF Graph Kernels
e a
b
c d
eb abcd
baedc
cab dab
abcd Ntilde f
eb Ntilde h
baedc Ntilde g
dab Ntilde i
cab Ntilde j
h f
g
j i
Generated Sequences
bNtildegNtildej bNtildegNtildei bNtildegNtildef bNtildegNtildeh bNtildegNtildejNtildef
aNtildefNtildeg aNtildefNtildej aNtildefNtildei aNtildefNtildegNtildeh
8 18
Continuous Bag of Words and Skip-gram Models
9 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Framester [Gangemi et al 2016]
Figure Framester Factual-Linguistic Linked Data Hub Blue green orange yellow and greycolors represent role-oriented lexical resources wordnet-like lexical resources fact-oriented dataontology schemas and topic models respectively
4 18
Example Framester Frame Graph
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Figure A fragment of FrameNet-OWL graph dotted lines represent subFrameOf relation andsolid lines represent the inheritsFrom relation as defined in FrameNet-OWL
5 18
RDF Graph Based Frame Embeddings ndash RDF2Vec[Ristoski and Paulheim 2016]
Method
Word2Vec converts raw text into vector representations
RDF2Vec converts a graph into a sequence of nodes and edges
Models
Continuous Bag of Words
Given a context of next and previous words as input the central word is predicted
Example Capital Austria Ntilde Vienna
Skip-Gram
Given a word its context is predicted
Example Vienna Ntilde Capital Austria
Representing RDF Graphs into a sequence of nodes and edges
Graph Walks
Weisfeiler-Lehman Subtree RDF Graph Kernels
6 18
Graph Walks
Method
Define a depth d eg d ldquo 3
Perform walks of specific depth d to generate sequences of nodes and edges
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Sequences Generated
Event Ntilde inheritsFrom Ntilde Objective Influence Ntilde inheritsFrom Ntilde Transitive Action
Intentionally affect Ntilde inheritsFrom Ntilde Invasion Scenario Ntilde subFrameOf Ntilde Conquering
7 18
Weisfeiler-Lehman Subtree RDF Graph Kernels
e a
b
c d
eb abcd
baedc
cab dab
abcd Ntilde f
eb Ntilde h
baedc Ntilde g
dab Ntilde i
cab Ntilde j
h f
g
j i
Generated Sequences
bNtildegNtildej bNtildegNtildei bNtildegNtildef bNtildegNtildeh bNtildegNtildejNtildef
aNtildefNtildeg aNtildefNtildej aNtildefNtildei aNtildefNtildegNtildeh
8 18
Continuous Bag of Words and Skip-gram Models
9 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Example Framester Frame Graph
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Figure A fragment of FrameNet-OWL graph dotted lines represent subFrameOf relation andsolid lines represent the inheritsFrom relation as defined in FrameNet-OWL
5 18
RDF Graph Based Frame Embeddings ndash RDF2Vec[Ristoski and Paulheim 2016]
Method
Word2Vec converts raw text into vector representations
RDF2Vec converts a graph into a sequence of nodes and edges
Models
Continuous Bag of Words
Given a context of next and previous words as input the central word is predicted
Example Capital Austria Ntilde Vienna
Skip-Gram
Given a word its context is predicted
Example Vienna Ntilde Capital Austria
Representing RDF Graphs into a sequence of nodes and edges
Graph Walks
Weisfeiler-Lehman Subtree RDF Graph Kernels
6 18
Graph Walks
Method
Define a depth d eg d ldquo 3
Perform walks of specific depth d to generate sequences of nodes and edges
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Sequences Generated
Event Ntilde inheritsFrom Ntilde Objective Influence Ntilde inheritsFrom Ntilde Transitive Action
Intentionally affect Ntilde inheritsFrom Ntilde Invasion Scenario Ntilde subFrameOf Ntilde Conquering
7 18
Weisfeiler-Lehman Subtree RDF Graph Kernels
e a
b
c d
eb abcd
baedc
cab dab
abcd Ntilde f
eb Ntilde h
baedc Ntilde g
dab Ntilde i
cab Ntilde j
h f
g
j i
Generated Sequences
bNtildegNtildej bNtildegNtildei bNtildegNtildef bNtildegNtildeh bNtildegNtildejNtildef
aNtildefNtildeg aNtildefNtildej aNtildefNtildei aNtildefNtildegNtildeh
8 18
Continuous Bag of Words and Skip-gram Models
9 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
RDF Graph Based Frame Embeddings ndash RDF2Vec[Ristoski and Paulheim 2016]
Method
Word2Vec converts raw text into vector representations
RDF2Vec converts a graph into a sequence of nodes and edges
Models
Continuous Bag of Words
Given a context of next and previous words as input the central word is predicted
Example Capital Austria Ntilde Vienna
Skip-Gram
Given a word its context is predicted
Example Vienna Ntilde Capital Austria
Representing RDF Graphs into a sequence of nodes and edges
Graph Walks
Weisfeiler-Lehman Subtree RDF Graph Kernels
6 18
Graph Walks
Method
Define a depth d eg d ldquo 3
Perform walks of specific depth d to generate sequences of nodes and edges
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Sequences Generated
Event Ntilde inheritsFrom Ntilde Objective Influence Ntilde inheritsFrom Ntilde Transitive Action
Intentionally affect Ntilde inheritsFrom Ntilde Invasion Scenario Ntilde subFrameOf Ntilde Conquering
7 18
Weisfeiler-Lehman Subtree RDF Graph Kernels
e a
b
c d
eb abcd
baedc
cab dab
abcd Ntilde f
eb Ntilde h
baedc Ntilde g
dab Ntilde i
cab Ntilde j
h f
g
j i
Generated Sequences
bNtildegNtildej bNtildegNtildei bNtildegNtildef bNtildegNtildeh bNtildegNtildejNtildef
aNtildefNtildeg aNtildefNtildej aNtildefNtildei aNtildefNtildegNtildeh
8 18
Continuous Bag of Words and Skip-gram Models
9 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Graph Walks
Method
Define a depth d eg d ldquo 3
Perform walks of specific depth d to generate sequences of nodes and edges
Event initial state Event Event end state
Objective influence Motion
Transitive action Control
Intentionally affect Mass motion Motion Noise
Invasion Scenario Attack
Invading Conquering Besieging
Repel
precedes precedes
precedes
precedes
Sequences Generated
Event Ntilde inheritsFrom Ntilde Objective Influence Ntilde inheritsFrom Ntilde Transitive Action
Intentionally affect Ntilde inheritsFrom Ntilde Invasion Scenario Ntilde subFrameOf Ntilde Conquering
7 18
Weisfeiler-Lehman Subtree RDF Graph Kernels
e a
b
c d
eb abcd
baedc
cab dab
abcd Ntilde f
eb Ntilde h
baedc Ntilde g
dab Ntilde i
cab Ntilde j
h f
g
j i
Generated Sequences
bNtildegNtildej bNtildegNtildei bNtildegNtildef bNtildegNtildeh bNtildegNtildejNtildef
aNtildefNtildeg aNtildefNtildej aNtildefNtildei aNtildefNtildegNtildeh
8 18
Continuous Bag of Words and Skip-gram Models
9 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Weisfeiler-Lehman Subtree RDF Graph Kernels
e a
b
c d
eb abcd
baedc
cab dab
abcd Ntilde f
eb Ntilde h
baedc Ntilde g
dab Ntilde i
cab Ntilde j
h f
g
j i
Generated Sequences
bNtildegNtildej bNtildegNtildei bNtildegNtildef bNtildegNtildeh bNtildegNtildejNtildef
aNtildefNtildeg aNtildefNtildej aNtildefNtildei aNtildefNtildegNtildeh
8 18
Continuous Bag of Words and Skip-gram Models
9 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Continuous Bag of Words and Skip-gram Models
9 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
FRED Graphs Generated for two texts
T1 Spaniards conquered the Incas
T2 Spaniards attacked the Incas
10 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Framester Role and Frame Mappings
11 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Similarity Between two Frames
Wu Palmer
simwuppf1 f2q ldquo2 ˚ depthplcspf1 f2qq
depthpf1q ` depthpf2q
Leacock Chodorow
simlcpf1 f2q ldquo acutelogplenpf1 f2q ` 1
2 ˚ Dq
Path Similarity
simpathpf1 f2q ldquo1
lenpf1 f2q ` 1
Cosine Similarity (Vectors Computed using RDF2Vec)
simcosinepf1 f2q ldquoV1 uml V2
||V1|| uml ||V2||
lcs least common subsumer
len shortest path
D maximum depth of taxonomy
12 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Merged graph
13 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Experimentation
Cross-document Coreference Resolution (CCR) on RDF graphs
Associates RDF nodes about a same entity (object person concept etc) acrossdifferent RDF graphs generated from text
Dataset
EECB dataset specifies coreferent mentions (text fragments)
RDF graphs were generated from EECB using FRED
Text mentions were manually (via CrowdFlower) associated with graph nodes
The evaluation framework is built on top of the original MERGILOa
ahttpwitistccnritstlab-toolsmergilo
14 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Evaluation Measures
ndash MUC Link-based metric that quantifies the number of merges necessary to coverpredicted and gold clusters
ndash B3 Mention-based metric that quantifies the overlap between predicted and goldclusters for a given mention
ndash CEAFM (Constrained Entity Aligned F-measure Mention-based) Mention-basedmetric based on a one-to-one alignment between gold and predicted clusters
ndash CEAFE (Constrained Entity Aligned F-measure Entity-Based) Entity-based metricbased on a one-to-one alignment between gold and predicted clusters
ndash BLANC (Bilateral Assessment of NounPhrase Coreference) Rand-index-basedmetric that considers both coreference and non-coreference links
15 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Experimental Results
muc bcub ceafm blanc ceafeMERGILO Baseline 2405 1736 2861 1070 2620
FrameNet Inheritance Similarity MeasuresWu-Palmer 2714 1991 3191 1281 2941Path 2716 1993 3185 1273 2938Leacock Chodorow 2704 1980 3174 1277 2921
Graph walks (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 CBOW 200 2734 1999 3215 1266 2982CBOW 200 SG 800 2738 1997 3229 1269 2998CBOW 200 SG 500 2728 1995 3199 1269 2954
Graph kernels (full frame and role graphs)Frame2Vec Role2Vec muc bcub ceafm blanc ceafeCBOW 200 SG 200 2670 1952 3145 1240 2899CBOW 200 SG 500 2670 1952 3145 1240 2899SG 200 CBOW 200 2686 1962 3167 1248 2918SG 500 CBOW 200 2690 1968 3158 1260 2908
Table Event-Based Knowledge Reconciliation Results
16 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
Conclusions amp Perspectives
We introduce similarity measures for frames and semantic roles using Framestergraphs and RDF2Vec
We evaluate them as an improvement over MERGILO using event knowledge fromFRED graphs
Frame-based similarity is sensible even with top-down intensional embeddings
Frame embedding seems to improve also over classical graph-based similarityalgorithms
Further practical applications of frame embeddingsNext experimenting with extensional embeddings from corpus annotation (seebelow)
news series integrationknowledge graph evolution with robust event reconciliationconflict detection across texts describing similar factstext summarization or dialogue
Extended version of this paper just published on KBS [Alam et al 2017]
Frame2Vec models available athttplipnuniv-paris13fr~alamFrame2Vec
Anticipation new (extensional) frame embeddings based on frame extraction fromfull WFD of Wikipedia (come visit at the poster session for a demo)
17 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18
References I
Alam M Reforgiato Recupero D Mongiovi M Gangemi A and Ristoski P(2017)Event-based knowledge reconciliation using frame embeddings and frame similarityKnowledge-Based Systems 135192ndash203
Gangemi A Alam M Asprino L Presutti V and Recupero D R (2016)Framester a wide coverage linguistic linked data hubIn Knowledge Engineering and Knowledge Management 20th InternationalConference Bologna Italy pages 239ndash254
Mongiovı M Recupero D R Gangemi A Presutti V and Consoli S (2016)Merging open knowledge extracted from text with MERGILOKnowl-Based Syst 108155ndash167
Ristoski P and Paulheim H (2016)Rdf2vec Rdf graph embeddings for data miningIn ISWC pages 498ndash514
18 18