Ontology Research Group Overview

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Ontology Research Group Overview ORG Dr. Valerie Cross Sriram Ramakrishnan Ramanathan Somasundaram En Yu Yi Sun Miami University OCWIC 2007 February 17, Deer Creek Resort OCWIC 2007 1

Outline Motivation for Research Agenda Semantic Web What are Ontologies? Some Ontologies Used in our Research Overview of Current Ontology Research Areas Semantic Similarity in Ontologies Ontology Evaluation Ontology Learning Ontology Querying Ontology 3D Visualization Possibilities OCWIC 2007 2

Building the Semantic Web Scientific America, May 2001 http://www.sciam.com/article.cfm?articleid=00048144-10d2-1c70-84a9809ec588ef21 OCWIC 2007 3

Ian Horrocks, ICCL Summer School Course 2005 OCWIC 2007 4

he Semantic Web is an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. T. Berners-Lee, J. Hendler, O. Lassila OCWIC 2007 5

Ian Horrocks, ICCL Summer School Course 2005 OCWIC 2007 6

Ontology Dimensions [Welty, Uschold, Gruninger, Lehmann & McGuinness, 1999] OCWIC 2007 7

Ian Horrocks, ICCL Summer School Course 2005 OCWIC 2007 8

Current Ontologies Used in Our Research WordNet UMLS Gene Ontology SEURAT s Argument Ontology PSCS Ontologies UNSPSC ecl@ass OCWIC 2007 9

WordNet Is a Hierarchy Four parts of speech nouns, verbs, adjectives and adverbs. Concept Node = Synonyms + Definition (gloss) ~ 111,400 concepts. ~ 13 types of relationships. 9 noun is a hierarchies with an average depth of 13. 628 verb is a hierarchies with an average depth of 2. other roots for noun hierarchies: psychological_feature, abstraction, state, event, act, possession, group, phenomenon synonymy relation implicit in each node (through synsets) hyponymy (IS-A) relation, its inverse, hypernymy; six meronymic (PART-OF) relations (and inverses): COMPONENT-OF, MEMBER-OF SUBSTANCE-OF antonymy, the COMPLEMENT-OF relation. OCWIC 2007 10

UMLS Semantic Network OCWIC 2007 11

Gene Ontology - BP GO contains three structured, controlled vocabularies describing gene products biological processes (BP), cellular components (C molecular functions (M in a species-independent manner. OCWIC 2007 12

Semantic Distance, Similarity, Relatedness Semantic relatedness General term involving many relationships car-wheel (meronymy) hot-cold (antonymy) pencil-paper (functional) penguin-antarctica (association) vehicle-car (subsumption) Semantic similarity More specific term involving likeness bank-trust company (synonymy) Distance Inverse of either one reldist(x)=semantic relatedness -1 (x) simdist(x)=semantic similarity -1 (x) OCWIC 2007 13

Applications of Semantic Relatedness Interoperability between Ontologies Merging, Linking, agent communication Information Retrieval To improve automated assignment of indexing based descriptors Semantic Vocabulary Integration Choose closest related concepts while translating in and out of the multiple vocabularies/ontologies Semantic Vocabulary Interoperability Project at http://cgsb2.nlm.nih.gov/~kashyap/projects/svip OCWIC 2007 14

Semantic Similarity Research (Youbo Wang) Comparison of semantic similarity measures in literature using experimental approach for different domain ontologies and types of ontologies Create a testbed Acquire different domain ontologies and different types of ontologies (WordNet and UMLS) Develop plug-ins for Protégé for all the existing semantic relatedness measures Develop comparison with human judgment for new domain ontologies such as medical OCWIC 2007 15

Motivation for Ontology Evaluation Ontologies the backbone of the Semantic Web Development and deployment of ontology-based software solutions requires considerable time and effort Numerous existing ontologies in libraries available on the WWW Why reinvent the wheel? Reuse of ontologies important to SW success OCWIC 2007 16

What is ontology evaluation? Ontology evaluation - key problem in the field of ontology development and reuse. Selection vs. Evaluation Two separate tasks? How related? When does it occur? Selection Evaluation? Evaluation Selection? Ontology Selection: Ontology Evaluation on the Real Semantic Web (Sabou, Lopez, Motta,Uren EON 2006) OCWIC 2007 17

Ontology Consumer Analysis Tool Objective is to examine from Consumer perspective to Reuse or adapt ontology Suite of metrics from conceptual modeling, software development, information systems development information retrieval Used on WordNet, UMLS, UNSPSC and ecl@ss OCWIC 2007 18

OntoCAT (Anindita Pal) plug-in for OWL Protégé very parameterized Intensional (classes) and extensional (instances) View metrics interested in Size Structure User selectable root for analysis Hub concept analysis User selectable relation for establishing extensional structure OCWIC 2007 19

OntoCAT intensional metrics UNSPSC RDF/RDF(S) version of UNSPSC is developed by Michel Klein and was obtained from http://www.cs.vu.nl/~mcaklein/unspsc OCWIC 2007 20

Ontology Learning (Vishal Bathija) Objective: Adapt the SEURAT Argument Ontology from the Software Engineering Domain to the Spacecraft Engineering Domain. Developed an Ontology Adaptation Methodology Pruning: Remove from the existing Argument Ontology concepts not relevant to Engineering Design (ED) by analyzing text corpuses specific to domain Adapting and Specializing: Adding new concepts and relations (is-a only) to transform pruned ontology into the ED criteria ontology. Evaluation Evaluate the adapted ontology based on performance measures used in ontology learning research literature OCWIC 2007 21

Ontology Adaptation Software Architecture Input Resources: Argument Ontology Training Corpus Test Corpus General Corpus Software Resources: Apache Lucene JWordNet Test Corpus General Corpus OCWIC 2007 22

QUOTA Ontology Querying (Yi Sun) QUerying with Ontological Terminologies and their Annotations uses previous research on: Semantic Similarity Ontology metrics Gene Ontology Annotations from model organism databases. OCWIC 2007 23

Ontology 3D Visualization (Ramanathan Somasundaram) Objective: build a package of 3D visualization algorithms to produce visualizations of different types of ontologies for different purposes Experiment with different weight functions Apply the weight functions to different force directed layout algorithms Designing filtering view for different tasks Parallelizing the execution OCWIC 2007 24

Visualization using weight function Interval rank (SpindleViz) 1; 5 B; 3 C; 3 I; 1 G; 0 J; 0 F; -1 E;-1 A;-2 D;-3 K; 0 5 3 1 0-1 -2-3 F; 1 B; 7 1; 11 I; 3 C; 5 G; 2 E; 2 J; 2 A; 1 D; 1 K; 1 11 7 5 3 2 1 0;-5 Balanced Height Weight Function, W BH (C) -5 0; 0 extent (c) Weight Function, W NS (C) 0 OCWIC 2007 25

Research Possibilities. Semantic Similarity Develop a task/application-specific evaluation procedure Continue with mathematical analysis and properties Apply to 3D visualziation Investigate semantic relatedness measures in assessing ontology quality OCWIC 2007 26

Research Possibilities Ontology Evaluation Interface with candidate selection approaches before perform detailed analysis Comparison metrics/charts/visualization for multiple ontologies for candidates Visualization to help consumers see ontology for reuse and comparison Hubs visualization Improvement Individual hub visualization Top-level summary / visualization Bottom-up level summary / visualization Combine and aggregate analysis results to provide consumers with summaries characterizing each ontology and a vocabulary for critiquing an ontology after all the metric analysis OCWIC 2007 27

Research Possibilities Ontology Learning More experimentation with OntoLT user-specified rules to produce concept trees Use of DR sublanguage to parse the training text collections looking for keywords of the domain sublanguage Limitation of Ontology adaptation software Dependency on SCHUG Find other open source software or develop software to provide the SCHUG functionality Domain expert user interface to provide input to the pruning and merging process OCWIC 2007 28

Interested? Send me email: crossv@muohio.edu Be happy to send some references Consider applying to Miami Unversity s masters program in computer science Most graduate students fully funded as a first year teaching assistant and a second year research assistant Pick up Miami folder at OCWIC if you don t have one. OCWIC 2007 29

OCWIC 2007 30