An Ontology Creation Methodology: A Phased Approach Jon Atle Gulla Norwegian University of Science and Technology; Norway jag@idi.ntnu.no Vijay Sugumaran Oakland University, USA sugumara@oakland.edu

Agenda Ontology development Traditional ontology learning Limitations of ontology learning A phased approach to ontology learning

The Challenge How to develop large complex ontologies? How to keep ontologies updated in dynamic domains?

Ontology Modeling vs. Learning Traditional ontology engineering approach Project: Form team of ontology and domain experts Ontology & domain experts: Collaborative manual modeling process Domain experts: Verify ontology against domain knowledge Ontology experts: Verify ontology against syntactic and semantic quality measures Expensive and time-consuming approach Stable domains assumed Ontology learning approach: Domain experts: Find representative domain text Tool: Extract candidate classes, individuals and properties automatically from domain texts Ontology & domain experts: Verify candidate structures and complete ontology Can also be used to verify domain quality of existing ontology Cost-effective approach Not unproblematic in dynamic domains

Agenda Ontology development Traditional ontology learning Limitations of ontology learning A phased approach to ontology learning

Ontology Learning Basis People communicate using domain-specific concepts People document using domain-specific concepts Ontology learning: Extract ontology structures from written documentation Requirements: Documents representative for domain terminology Documents cover all the terminology Well-defined and consistent use of terminology in domain Ontology discussions Realm of ontology engineering Ontology in use Realm of ontology learning

Levels of Ontology Learning Degree of difficulty  x,y(manager(x,y) → report(y,x)) Rules Relations FINANCE(ag:SPONSOR, go: PROJECT) Concept hierarchies is_a(MANAGER, EMPLOYEE) Concepts PROJECT Synonyms (leader, manager, lead) Terms sponsors, costs, charter

Ontology Learning Strategies Term extraction Linguistic analysis Statistical analysis Synonyms Classification-based techniques Distribution-based techniques Concept formation Structure recognition Keyphrase generation Instance learning Concept hierarchy Clustering Lexico-syntactic patterns Head-modifier approaches Subsumption approaches Relations Association rules Concept vectors Rules Structure recognition for meta-property recognition Dependency trees and path similarities

Ontology Learning Process Scope management WBS Business need Constituent components Product description ... PMBOK Abstract elements Constraints Properties Rules Domain text Concept candidates Search ontology Reference set Automatic extraction of concept and relationship candidates Manual selection of candidates and completion of model

Ex 1. Learning Concept/Individual Candidates Scope planning is the process of progressively elaborating and documenting the project work (project scope) that produces the product of the project. Scope/NNP planning/NN is/VBZ the/DT process/NN of/IN progressively/RB elaborating/VBG and/CC documenting/VBG the/DT project/NN work/NN (/( project/NN scope/NN )/) that/WDT produces/VBZ the/DT product/NN of/IN the/DT project/NN ./. POS tagging Scope planning is the process of progressively elaborating and documenting the project work (project scope) that produces the product of the project. Stopword removal (571 words) Scope plan process progress elaborate document project work project scope produce product project Lemmatization/stemming (POS tags not shown) {scope planning, process, project work, project scope, product, project} Select consecutive nouns as candidate phrases {(scope planning, 0.0097), (project scope, 0.0047), (product, 0.0043), (project work, 0.0008), (project, 0.0001), (process, 0.0000)} Calculate tf.idf score for phrases

Classes Relevant to the Drama Genre Data sources: IMDB, Wikipedia, Videoload Keyphrase extraction technique Noun phrases ranked according to various statistical measures

Ex 2. Learning Relationship Candidates Tokenizer GATE Sentence splitter Tagger Lemmatizer Noun phrase extractor Noun phrase indexer Association rules miner Association rules Concept profiles Concept similarity calculation profile builder Lucene Document Paragraph Light stemmer Relationship merger

Relationships Relevant to Drama Genre Association rules on extracted concepts

Automatic OWL Generation

Agenda Ontology development Traditional ontology learning Limitations of ontology learning A phased approach to ontology learning

Limitations of Ontology Learning Different techniques produce different results Different data sources produce different results Lost control over process Extensive verification of final ontology needed New data hard to combine with old data

Agenda Ontology development Traditional ontology learning Limitations of ontology learning A phased approach to ontology learning

Ontology Learning for Entertainment Domain Ontology evolution for Deutsche Telecom’s Videoload download service What does Brangelina mean? Should Pitt be Brad Pitt or Michael Pitt? Actor vs. Schauspieler? All movies of Brad Pitt? Last movie of Pitt?

Ontology Learning Project Duration: Nov 2007 – Nov 2009 Domain: movie download service Ontology analysis and creation based on indexed noun phrases from movie documents Ontology used for search and navigation on top of FAST search platform Ontology learning challenges: Domain changes from one day to another No consistent domain terminology No professional domain terminology Multiple languages Movies about anything... unlimited domain Ontology needs to be up to date to support search

Ontology Workbench 3 phases that are carried out independently Crawling into Lucene indices Supervised extraction of candidates Combining candidates into ontology structures

Interactive Ontology Development Expandable indices Subset of data source Focus of analysis List of techniques Partial results Stored results Set operations for combining results

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