ph d thesis-ahsan_slidesv3

Ahsan Morshed, FAO 1 / 54

http://www.fao.org/aims/

Aligning Controlled vocabularies for enabling semantic matching in a distributed knowledge

management system

Ahsan Morshed Doctoral CandidateUniversity of Trento

[email protected]

PhD Supervisor: Professor Fausto [email protected]



Publications (1-3)

A. Morshed. Controlled Vocabulary Matching in Distributed Systems, at

BNCOD 2009 Conference,UK.

A. Morshed and M. Sini. Aligning Controlled vocabularies: Algorithm and

Architecture at Workshop on Advance Technologies for Digital Libraries

2009, AT4DL, Trento, Italy.

M. Sini, J. Keizer, G. Johannsen, A. Morshed, S. Rajbhandari and M.

Amirhosseini.The AGROVOC Concept Server Workbench System:

Empowering management of agricultural vocabularies with semantics at

International Association of Agricultural Information Specialists (IAALD),

France, 2010.



Publications (4-6)

A. Morshed, G. Johanssen, J. Keizer and M. Zeng,. Bridging End Users’ Terms and AGROVOC Concept Server Vocabularies. International Conference on Dublin Core and Metadata Applications (DC-2010), Pittsburgh, USA, 2010 (submitted).

A. Morshed, M. Sini and J. Keizer. Aligning Controlled Vocabularies using a facet based approach. (Technical Paper at FAO).

A. Morshed and R. Singh. Evaluation and Ranking of Ontology Construction Tools (Technical Paper).



Agenda

Background: the role of controlled vocabulary in semantic matching

The overall goal: Aligning Controlled Vocabularies in a distributed system

A facet based matching

An Architecture for matching system

A running prototype for matching system

Evaluation Methodology and Results

Limitations and Related Works

Conclusions and Future work



Some matching techniques

Element Matching techniques

ex: edit distance

Corpus-based techniques

ex: token or extension of classes

Structure-based tecniques

ex: graph matching

Knowledge-based techniques

ex: external resources



Some matching systems

Cupid

- element level and structure level matching

RiMOM

- based on edit distance and Vector distance

FALCON-AO

- based on Linguistic and structure matching

CTXMatch, S-match

-based on knowledge based



Some matching projects

HILT (High Level Thesaurus Project)

-JISC funded project, UK

-to facilitate the cross-searching of distributed information services by subject in a multi-schema environment.

-used datasets (e.g.,DDC,LCSH, IPSV, AAT)

CAT to AGROVOC Dr. Chan chung

64,638 Chinese terms, 51,614 descriptors and 13,024 non-descriptors

13,105 exact matches,11,408 BT match, 173 NT match, and 17,47other matches



Some matching project

OAEI 2007 (Ontology Alignment Evaluation Initiative) -Food Track- AGROVOC-NALT thesauri

System Alignment Alignment Type

Falcon-AO 15,300 exactMatch

RiMOM 18,420 exactMatch

X-SOM 6,583 exactMatch

DSSim 14,962 exactMatch

[Willem , 2008]



Matching in Distributed System

Edutella

Edutella is an open source project that creates an infrastructure for sharingmetadata in RDF format

It applies the peer-to-peer model using the JXTA protocol

Swap

aims at overcoming the lack of semantics in current Peer-to-Peer system



Semantic Matching in Lighweight ontologies

To use of lightweight ontologies for matching purpose, all entities need toagree on the exact meaning of the concepts.

Descriptive lightweight ontologies

-used for defining the meaning of terms as well the nature and structure of adomain.

Classification lightweight ontologies

-used for describing, classifying, and accessing collection of document.

[Fausto et al.,2007]



Controlled Vocabulary (CV)

A vocabulary stores words, synonyms, word sense definitions (i.e.glosses), relations between word senses and concepts; such avocabulary is generally referred to as the Controlled Vocabulary (CV)if choice or selection of terms are done by domain specialists [ahsan etal.,2009]



Controlled Vocabulary

General controlled vocabulary:

Example: Thesaurus, WordNet, Classification, Directories, Lightweight Ontologies

Subject specific controlled vocabulary (SSCV)

Library of Congress and Authors List

Uniform List

Series List



Applications for managing controlled vocabularies

Traditional Controlled Vocabulary tools

Ex: Old Agrovoc Thesaurus

Modern Controlled Vocabulary

Ex: AGROVOC Concept Server



AGROVOC Concept Server

-store concepts

-Edit concepts

-visualize the

concepts

modern controlled vocabulary

Ref: http://nais.cpe.ku.ac.th/agrovoc/



Applications for exploiting controlled vocabularies

Background Knowledge

Document annotation

Information retrieval and extraction

Audio and Video retrieval



Challenges of Matching

Factors of heterogeneity problem

Time

Place

Structure

Culture diversity

Different vocabulary specialists



Challenges of Matching

Different heterogeneity

Syntactic heterogeneity

Lexical heterogeneity

Semantic heterogeneity

Pragmatic heterogeneity

Metadata heterogeneity

[Pavel, 2006 ]



Problem of CV



FACET

A facet is like a diamond that consists of different faces.

Its distinct features allow thesauri, classifications or taxonomies tobe organized in different ways.

composed of collectively exhaustive aspects of properties orcharacteristics of a domain.

For example, a collection of rice might be classified using culturaland seasonal facets.

[Fausto et al.,2009] [ahsan et al.,2009]



Faceted Controlled vocabulary



Faceted Controlled vocabulary

Seasonal rice type Cultural rice type



Creation of a Facet

Domain Analysis

analysis of terms are done by consulting domain experts

simple concept are identified.

Term collections and organization

terms are order according to their characteristic and meaningful sequence

ex: cow and milk form a facet called Diary system(part of relationship)

[Fausto et al., 2009]



Exisiting Metholodies

PMEST : Personality(P), Matter(M), Energy(E), Space (S), and Time(T)

[Ranganathan]

DEPA : Discipline(D), Entity (E), Property (P), Action(A)

[Bhattachary and Fausto et al., 2009 ]



Properties of facets

Hospitalities

Compactness

Flexibility

Reusability

The Methodology

Homogeneity

[Bhattachary and Fausto et al., 2009 ]



Concept Facet Matcher

Based on DEPA model

CF={mg,lg,R} Where, mg is more general concepts ,lg is less general concepts, R is related concepts.

Based on Element Lebel Matchers

[Ahsan, 2009 and Ahsan et al., 2009 ]




Algorithm 1 buildCFacet(CV)

for i = 0 to CV do

store cF (Mg,Lg;R)

end for

return cF

[Ahsan et al., 2009 ]




Algorithm 2 MatchingFacet(CV1,CV2)cF1=BuildCFacet(CV1)cF2=BuildCFacet(CV2)for i = 0 to cF 1:size dofor j = 0 to cF 2:size docfmatcher=elementLevelMatcher(cF 1;cF2)end forend for

[Ahsan et al., 2009 ]



System Architecture



Data Model

Agrovoc databaseRef: http://aims.fao.org/website/Download/sub



DATA Model

CABI database

Ref: http://cabi.org



A Running Prototype

Search Sring

Validators/ domain

specialist s



An architecture for a semantic search



Running Prototype for search

user’s choice



Evaluation and Results

A domain Expert Exact Match Partial Match No Match



Datasets

Comparision

Characteristics AGROVOC CAB

Tree leaves 29172 47805

Term counts 18200 32884

Single words 6842 11720

MultiWords 11358 21161

Hierarchy depth 7 14

multiple BT 2546 1207

redundant BT 57 76



Datasets

AGROVOC version 2007-08-10 2007-08-10

CABI version 2009-11-01 2009-11-01

AGROVOC term-leaves 35036 35036

CABI term-leaves 29172 29172

Coversion hierarchy hierarchy

Knowledge base WordNet 2.1 SWN 400.000



Datasets

Relationship

BT NT RT UF

AGROVOC 228466 228424 326389 54370

CABI 15154 15841 41239 7094



Input files

Agrovoc input file CAB input file



Results

Experiment 1 Experiment 2

Exact Match 5976 6021

Partial Match 164255 164278

No Match 69800745 69800745

Facet based appraoch



Results

Experiment 1 Experiment 2

Exact Match 8795 8795

Partial Match 334255 334258

No Match N/A N/A

Standard Tool



Results

Min Max Min Max

Overall 25.8065 31.4496 21.7391 21.7391

Positive 18.6047 14.0814 10.4895 14.6154

Negative 97.1831 52.1495 94.7368 99.1304



Advantage of Facet based System

No knowledge base required

Based on hidden semantic. Semantic meaning retrived during the processing



Limitations Structure Problems

AGROVOC SQL Format and CABI Text Format Provided CABI file does not contain chemical and scientific concepts

Term Variants In AGROVOC, we found ``frog farms" which should have been ``frog farming"

because ``frog farms" is used for ``frog culture" and BT is ``aquaculture". Also, wefound the abbreviated term ``UHT milk" (one kind of milk product) which shouldhave been "UHT milk".

There were some ambiguous term which had different meanings, for example``cutting" ( i.e., slicing of bread or meat) or ``cuttings" (i.e.,propagation material).

there were some terms spells whose meaning is to difficult to capture, forexample “2.4.4-T”, “2.4.5-TP 2.4-D”, “2.4 DES”, “2.4 dinitrohenol”. Similarly, CABIcontained the term “4-H Clubs”. These terms did make sense during anymapping experiments.



Limitations

Domain expert

To evaluate our results, we were able to find one domain expert from

FAO but we did not get any domain expert from CABI. The results mayhave been different if we had another domain expert.

Lack of consistency

Since the relationships in thesauri lack precise semantics, they areapplied inconsistently, both creating ambiguity in the interpretationof the relationships and resulting in an overall internal structure thatis irregulated and unpredictable



Limitations

Limited automated processing

Traditional thesauri are designed for indexing and query formulation bypeople and not for automated processing. The ambiguous semantics thatcharacterizes many thesauri makes them unsuitable for automatedprocessing.



Related Works

[Fausto et. al, 2004] apply element level matching techniques

for semantic matching

[Stamou et.al] apply string matching techniques for ontologymatching

[Karin Koogan Breitman et.al 2005] apply string matching

techniques for lighweight ontology matching

[Paul Buitelaar et. al, 2009] apply string matching for linguisticmatching system

[Maria Teresa Pazienza et.al, 2007] Apply string matching forsemi-automatic matching system



Conclusion and Future work

To build the extended knowledge base



Conclusion and Future work Integrating Mapping into AGROVOC concept Server



Conclusion and Future work

We have described the facet based matching system for a large dataset

We have shown a running prototype for this system.

The majority of this work was done under the supervision of the FAO andthe CABI. At the moment, a prototype is running at the FAO

We will integrate this mapping file for searching purpose in AGROVOCConcept Server.



Questions



References

[Fausto et al., 2003]: F.Gunchiglia and P. Shvaiko. Semantic MatchingOntologies and Distributed System workshop, IJCAL,2003

[Fausto et al., 2004]: F. Gunchiglia, P. Shvaiko, and M. Yatskevich. S-Match: An algorithm and an implementation of semantic matching.In Proceedings of ESWS’04, 2004.

[Fausto et al., 2004]: F.Gunchiglia and M. Yatskevich. Element levelsemantic matching. In meaning Coordination and Negotiationworkshop, ISWC,2004

[Pavel et al., 2006]: P. Shvaiko, F.Gunchiglia and M. Yatskevich.Discovering missing background knowledge in ontology matching. In17th European Conference on Artificial Intelligence (ECAI 2006),volume 141,pages 382-386,2006



References (cont)

[Fausto et al., 2007]: F.Gunchiglia and I. Zaihrayeu. Light weight

Ontologies . Technical report at DIT, University of Trento Italy, October

2007

[Pavel et al., 2007]: P. Shvaiko, and J.Euzenate. Ontology matching.

Springer, 1st edition , 2007.

[Fausto et al., 2004]: F.Gunchiglia and M. Yatskevich. Element levelsemantic matching. In meaning Coordination and Negotiationworkshop, ISWC,2004

[S.R. Ranganathan]: S.R. Ranganathan. Element of library classification.Asia Publishing house



References (cont)

[Fausto et al., 2009]: F.Gunchiglia, B. Dutta, and V. Maltese. Facetedlightweight ontologies. In LNCS, 2009

[Bhattachary 1979]: G. Bhattachary. POPSI: its foundamentals andprecedure based on a general theory of subject indexing language. InLibrary Science with a slant to Documentation, volume 16, pages.

[Pavel]: P. Shvaiko . Iterative schema-based semantic matching (PhDthesis), Technical report DIT-06-10Pavel]: 2,December 2006.

[morshed 2009]: A. Morshed and M. Sini. Aligning Controlledvocabularies: Algorithm and Architecture at Workshop on AdvanceTechnologies for Digital Libraries 2009, AT4DL, Trento, Italy

[Morshed 2009]: A. Morshed, M. Sini and J. Keizer. AligningControlled Vocabularies using a facet based approach. (Technical Paperat FAO).



Thank You

ph d thesis-ahsan_slidesv3

Documents

controlled vocabulary

matches ahsan morshed

thagrovoc ahsan morshed

peer system ahsan morshed

knowledge based ahsan

video retrieval ahsan

future work ahsan morshed

semantic matching