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Using NLP Techniques for Meaning Negotiation

Bernardo Magnini, Luciano Serafini and Manuela Speranza

ITC-irst, via Sommarive 18, I-38050 Trento-Povo, Italy

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Outline

Motivations

Matching algorithm

NLP techniques

Conclusions

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Meaning negotiation in Distributed KM

Autonomous communities within an organization have their own conceptualizations of the world, that are partial and perspectival

Meaning negotiation is a dynamic process, through which mappings between different conceptualizations are discovered

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Local Ontology

A set of terms and relations used by the members of an autonomous community to operate with local knowledge

Examples: the directory structure of a file system, the logical organization of a web site, e-commerce catalogues, etc.

Data structures: local ontologies are represented by means of contexts

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Examples of contexts

Context A Context B

Vacation

2001 2000

Sea LakeSeaMountains

Puglia Spain USA

Sea holidays

Italy in Europe

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Examples of contexts

Context A Context B

Vacation

2001 2000


Puglia Spain USA

Sea holidays

Italy in Europe

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Mapping between contexts

Source context Target context

Vacation

2001 2000


Puglia Spain USA

Sea holidays

Italy in Europe

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Vacation

2001 2000


Puglia Spain USA

Sea holidays

Italy in Europe

?

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Vacation

2001 2000


Puglia Spain USA

Sea holidays

Italy in Europe

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Problems

Relations between concepts expressed by different labels (e.g. ‘holiday’ is more general than ‘honeymoon’ but equal to ‘vacation’)

Semantic ambiguity of labels (e.g. ‘apple’ as a fruit vs. ‘apple’ as a computer brand)

Structural differences between overlapping heterogeneous contexts (e.g. classification of holidays according to years vs. places)

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Our proposal

Use of a lexical database (WordNet)

Creation of specific rules for sense disambiguation

Interpretation of hierarchical relations as syntactic dependency relations

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WordNet senses and concepts: the word ‘vacation’

[vacation#2][leisure#1, leisure time#1]

ISA

ISA

[vacation#1, holiday#1]

[honeymoon#1]

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‘Vacation’ in WordNet

Sense 1vacation, holiday => leisure, leisure time => time off => time period, period of time, period => fundamental quantity, fundamental measure => measure, quantity, amount, quantum => abstractionSense 2vacation => abrogation, repeal, annulment => cancellation => nullification, override => change of state => change => action => act, human action, human activity

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Context mapping

A relation between a node S of a source context and a node T of a target context

Possible mappings: – S T (e.g. animal dog)– S T (e.g. dog animal)– S = T (e.g. holiday = vacation)– S T (e.g. mountain sea)– S * T (e.g. car * hi-fi)

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Matching algorithm (I)

Input: a source node in the source context and a target node in the target context

Output: a mapping between the source and the target node

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Matching algorithm (II)

Single labels’ analysis (linguistic and semantic)

Sense refinement rules

Sense matching

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Labels’ linguistic analysis

Input: a label = <token1, token2, …, token n> Output: a data structure providing identification

number, lemma, part of speech and linguistic function of each token

Example: Data structure for ‘Sea holidays’

Sea holidays

ID Token Lemma PoS Function

0 Sea sea noun mod-1

1 holidays holiday noun head

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Labels’ semantic analysis

Use of WordNet as a repository of sensesE.g. ‘sea’ has three senses:– sea#1: ‘a division of an ocean’– sea#2: ‘anything apparently limitless’– sea#3: ‘turbulent water’

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Labels’ semantic analysis

Use of WordNet as a repository of senses Each token in the data structure is provided with its

WordNet senses, if any

ID Token Lemma PoS Function W-senses

0 Sea sea noun mod-1 sea#1sea#2sea#3

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Sense refinement (I)

Aim: Elimination of the w-senses that are in disagreement with other w-senses

tree

apple#1 (a fruit)

apple#2 (a computer brand)

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Sense refinement (I)

Aim: Elimination of the w-senses that are in disagreement with other w-senses

tree

apple#1 (a fruit)

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Sense refinement (II)

Assumption: sibling nodes are disjoint

Consequence: if a W-concept has a part-of or an inclusion relation with a w-concept of a sibling node, the meanings have to be composed

Italy#1 Europe#1

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Sense refinement (II)

Assumption: sibling nodes are disjoint

Consequence: if a W-concept has a part-of or an inclusion relation with a w-concept of a sibling node, the meanings have to be composed

Italy#1 Europe#1 – Italy#1

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Vacation

2001 2000


Puglia Spain USA

Sea holidays

Italy in Europe

?

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Contextual meanings


Vacation

2001 2000


Puglia Spain USA

Sea holidays

Italy in Europe

?

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Sense matrix

holiday#1holiday#2

sea#1sea#2sea#3

Europe#1 -Italy#1

vacation#1vacation#2

2001

sea#1sea#2sea#3

Spain#1

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Sense matrix

holiday#1 sea#1sea#2sea#3

Europe#1 -Italy#1

vacation#1 =

2001

sea#1sea#2sea#3

Spain#1

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Sense matrix

holiday#1 sea#1 Europe#1 -Italy#1

vacation#1 =

2001

sea#1 =

Spain#1

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Sense matrix


vacation#1 =

2001

sea#1 =

Spain#1

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Sense matrix


vacation#1 =

2001

sea#1 =

Spain#1

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Computing the matching via Sat (I):

i. The set of documents classifiable under a node is the intersection of the components of its contextual meaning (e.g. A1 ∩ A2, if the node has contextual meaning A1-A2)

ii. Computing the mapping between two nodes means finding the best relation between the intersections

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Computing the matching via Sat (II):

iii.For each single relation in the matrix a propositional formula is generated

– Ai Bj Ai → Bj

– Ai Bj Bj → Ai

– Ai = Bj Ai Bj

– Ai Bj ¬(Ai Λ Bj)

E.g. Spain → Europe holiday vacation

¬(Italy Λ Spain)

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Computing the matching via Sat (III):

iv.We check for satisfiability the union of all the propositions and the negation of the implication between the intersectionsE.g. (h v) Λ (S → E) Λ ¬(I Λ S) Λ Λ ¬(v Λ 2001 Λ s Λ S → h Λ s Λ E Λ ¬I)

v. If the check fails, the source node contains the target node; otherwise a similar procedure is followed for the other possible mappings

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Vacation

2001 2000


Puglia Spain USA

Sea holidays

Italy in Europe

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Conclusions

Meaning negotiation

Mappings between contexts

Matching algorithm

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Future Work

Evaluation of the algorithm

Further development of the algorithm

Use of the algorithm within an information retrieval system

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