An Environment for Merging and Testing Large Ontologies Deborah McGuinness, Richard Fikes, James Rice*, Steve Wilder Associate Director and Senior Research Scientist Knowledge Systems Laboratory Stanford University Stanford, CA 94305 650-723-9770 dlm@ksl.stanford.edu *CommerceOne, Mountain View, CA

Motivation: Ontology Integration Trends Integrated in most search applications (Yahoo, Lycos, Xift, …) Core component of E-Commerce applications (Amazon, eBay, Virtual Vineyards, REI, VerticalNet, CommerceOne, etc.) Integrated in configuration applications (Dell, PROSE, etc.)

Motivation: Ontology Evolution Controlled vocabularies abound (SIC-codes, UN/SPSC, RosettaNet, OpenDirectory,…) Distributed ownership/maintenance Larger scale (Open Directory >23.5K editors, ~250K categories, 1.65M sites) Becoming more complicated - Moving to classes and slots (and value restrictions, enumerated sets, cardinality)

Chimaera – A Merging and Diagnostic Ontology Environment Web-based tool utilizing the KSL Ontolingua platform that supports: merging multiple ontologies found in distributed environments analysis of single or multiple ontologies attention focus in problematic areas simple browsing and mixed initiative editing

The Need For KB Merging Large-scale knowledge repositories will contain KBs produced by multiple authors in multiple settings KBs for applications will be built by assembling and extending multiple modular KBs from repositories KBs developed by multiple authors will frequently Express overlapping knowledge in a common domain Use differing representations and vocabularies For such KBs to be used together as building blocks - Their representational differences must be reconciled

The KB Merging Task Combine KBs that: Were developed independently (by multiple authors) Express overlapping knowledge in a common domain Use differing representations and vocabularies Produce merged KB with Non-redundant Coherent Unified vocabulary, content, and representation

How KB Merging Tools Can Help Combine input KBs with name clashes Treat each input KB as a separate name space Support merging of classes and relations Replace all occurrences by the merged class or relation Test for logical consistency of merge (e.g. instances/subclasses of multiple disjoint classes) Actively look for inconsistent extensions Match vocabulary Find name clashes, subsumed names, synonyms, ... Focus attention Portions of KB where new relationships are likely to be needed E.g., sibling subclasses from multiple input KBs Derive relationships among classes and relations Disjointness, equivalence, subsumption, inconsistency, ...

Merging Tools Merging can be arbitrarily difficult KBs can differ in basic representational design May require extensive negotiation among authors Tools can significantly accelerate major steps KB merging using conventional editing tools is Difficult Labor intensive Error prone Hypothesis: tools specifically designed to support KB merging can significantly Speed up the merging process Make broader user set productive Improve the quality of the resulting KB

Our KB Analysis Task Review KBs that: Were developed using differing standards May be syntactically but not semantically validated May use differing modeling representations May have different purposes Produce KB logs (in interactive environments) Identify provable problems Suggest possible problems in style and/or modeling Are extensible by being user programmable

Chimaera Usage HPKB program – analyze diverse KBs, support KR novices as well as experts Cleaning semi-automatically generated KBs Browsing and merging multiple controlled vocabularies (e.g., internal vocabularies and UN/SPSC (std products and services codes)) Reviewing internal vocabularies

Discussion/Conclusion Ontologies are becoming more central to applications, they are larger, more distributed, and longer-lived Environmental support (in particular merging and diagnostic support) is more critical for the broader user base Chimaera provides merging and diagnostic support for ontologies in many formats It improves performance over existing tools It has been used by people of various training backgrounds in government and commercial applications and is available for use. http://www.ksl.Stanford.EDU/software/chimaera/ -movie, tutorial, papers, link to live system, etc.

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The Need For KB Analysis Large-scale knowledge repositories will contain KBs produced by multiple authors in multiple settings KBs for applications will be built by assembling and extending multiple modular KBs from repositories that may not be consistent KBs developed by multiple authors will frequently Express overlapping knowledge in different, possibly contradictory ways Use differing assumptions and styles Have different purposes KBs must be reviewed for appropriateness and “correctness”

Disjointness, Inverse, part-of… What is an Ontology? Thesauri “narrower term” relation Frames (properties) Formal is-a General Logical constraints Catalog/ ID Informal is-a Formal instance Disjointness, Inverse, part-of… Terms/ glossary Value Restrs.

Ontologies and importance to E-Commerce Simple ontologies provide: Controlled shared vocabulary (search engines, authors, users, databases, programs all speak same language) Organization (and navigation support) Expectation setting (left side of many web pages) Browsing support (tagged structures such as Yahoo!) Search support (query expansion approaches such as FindUR, e-Cyc) Sense disambiguation

Ontologies and importance to E-Commerce II Foundation for expansion and leverage Conflict detection Completion Regression testing/validation/verification support foundation Configuration support Structured, comparative search Generalization/ Specialization …

E-Commerce Search (starting point Forrester modified by McGuinness) Ask Queries - multiple search interfaces (surgical shoppers, advice seekers, window shoppers) - set user expectations (interactive query refinement) - anticipate anomalies Get Answers - basic information (multiple sorts, filtering, structuring) - modify results (user defined parameters for refining, user profile info, narrow query, broaden query, disambiguate query) - suggest alternatives (suggest other comparable products even from competitor’s sites) Make Decisions - manipulate results (enable side by side comparison) - dive deeper (provide additional info, multimedia, other views) - take action (buy)

A Few Observations about Ontologies Simple ontologies can be built by non-experts Consider Verity’s Topic Editor, Collaborative Topic Builder, GFP interface, Chimaera, etc. Ontologies can be semi-automatically generated from crawls of site such as yahoo!, amazon, excite, etc. Semi-structured sites can provide starting points Ontologies are exploding (business pull instead of technology push) most e-commerce sites are using them - MySimon, Affinia, Amazon, Yahoo! Shopping,, etc. Controlled vocabularies (for the web) abound - SIC codes, UMLS, UN/SPSC, Open Directory, Rosetta Net, … Business ontologies are including roles DTDs are making more ontology information available Businesses have ontology directors “Real” ontologies are becoming more central to applications

Implications and Needs Ontology Language Syntax and Semantics Environments for Creation and Maintenance of Ontologies Training (Conceptual Modeling, reasoning implications, …) Issues: Collaboration among distributed teams Diverse training levels Interconnectivity with many systems/standards Analysis and Diagnosis Scale