1. Ontologies (What they are; Why you should care; What you should know)
Deborah L. McGuinness
Associate Director and Senior Research Scientist
Knowledge Systems Laboratory
Stanford University
Stanford, CA 94305

2. 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.

3. Ontologies and importance to E-Commerce
Simple ontologies (taxonomies) provide:
Controlled shared vocabulary (search engines, authors, users, databases, programs/agents all speak same language)
Site Organization and Navigation Support
Expectation setting (left side of many web pages)
“Umbrella” Upper Level Structures (for extension)
Browsing support (tagged structures such as Yahoo!)
Search support (query expansion approaches such as FindUR, e-Cyc)
Sense disambiguation

4. Ontologies and importance to E-Commerce II
Consistency Checking
Completion
Interoperability Support
Support for validation and verification testing (e.g. )
Configuration support
Structured, “surgical” comparative customized search
Generalization/ Specialization
… Foundation for expansion and leverage

5. A Few Observations about Ontologies
Simple ontologies can be built by non-experts
Verity’s Topic Editor, Collaborative Topic Builder, GFP, Chimaera, Protégé, OIL-ED, 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, Amazon, Yahoo! Shopping, VerticalNet, etc.
Controlled vocabularies (for the web) abound - SIC codes, UMLS, UN/SPSC, Open Directory (DMOZ), Rosetta Net, SUO
Business interest expanding – ontology directors, business ontologies are becoming more complicated (roles, value restrictions, …), VC firms interested,
DTDs are making more ontology information available
Markup Languages growing XML, RDF, DAML, RuleML, xxML
“Real” ontologies are becoming more central to applications

6. Implications and Needs
Ontology Language Syntax and Semantics (DAML+OIL)
Environments for Creation and Maintenance of Ontologies
Training (Conceptual Modeling, reasoning implications, …)

7. Issues Collaboration among distributed teams
Interconnectivity with many systems/standards
Analysis and diagnosis
Scale
Versioning
Security
Ease of use
Diverse training levels /user support
Presentation style
Lifecycle
Extensibility

8. Chimaera – A Ontology Environment Tool
An interactive web-based tool aimed at supporting:
Ontology analysis (correctness, completeness, style, …)
Merging of ontological terms from varied sources
Maintaining ontologies over time
Validation of input
Features: multiple I/O languages, loading and merging into multiple namespaces, collaborative distributed environment support, integrated browsing/editing environment, extensible diagnostic rule language
Used in commercial and academic environments
Available as a hosted service from www-ksl-svc.stanford.edu
Information:

9. Discussion/Conclusion
Ontologies are exploding; core of many applications
Business “pull” is driving ontology language tools and languages
New generation applications need more expressive ontologies and more back end reasoning
New generation users (the general public) need more support than previous users of KR&R systems
Distributed ontologies need more support: merging, analysis, incompleteness, versioning, etc.
Scale and distribution of the web force mind shift
Everyone is in the game – US Government (DARPA, NSF, NIST, …), EU, W3C, consortiums, business, …
This is THE time for ontology work!!!

10. Some Pointers
Ontologies Come of Age Paper:
Ontologies and Online Commerce Paper:
DAML+OIL:

11. Extras

12. E-Commerce Search (starting point Forrester Research 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)

13. The Need For KB Analysis
Large-scale knowledge repositories will necessarily contain KBs produced by multiple authors in multiple settings
KBs for applications will typically 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
For such KBs to be used as building blocks -
They must be reviewed for appropriateness and “correctness”
That is, they must be analyzed

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