Transcript Ontologies and the Semantic Web
Ontologies and the Semantic Web
Deborah L. McGuinness Associate Director and Senior Research Scientist Knowledge Systems Laboratory Stanford University Stanford, CA 94305 650-723-9770 [email protected]
The Web is moving to a Semantic Web What is it How can a web with semantics be used Ontologies What are they How can they be used Second Session How can I get started (a look at requirements, languages, ad tools) Discussion in an example domain Session 1: Based loosely on Ontologies Come of Age.
Session 2: Based loosely on Ontology Engineering 101, OWL Overview, and OWL Guide, How and When to Live with a Kl-ONE-like System August 9, 2004 Deborah L. McGuinness 1
Yesterday: Rich Information Source for Human Manipulation/Interpretation
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“I know what was input”
The web knows what text was input (and is great at information dissemination) but does little interpretation, manipulation, integration, and action. Analogous to a new assistant who is thorough yet lacks common sense, context, adaptability, and the ability to interpret for you Some people view this as the “syntactic web”
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Moving to… Rich Information Source for Agent Manipulation/Interpretation
Human Agent Agent
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“I know what was meant”
Understand term meaning and user background Interoperable (can translate between applications) Programmable (thus agent friendly and operational) Explainable (thus maintains context and can adapt) Capable of filtering (thus limiting display and human intervention requirements) Capable of executing services August 9, 2004 Deborah L. McGuinness 5
Scientific American, May 2001:
Having a web that knows “what you want” or “what you mean” is accomplished by semantics…. specifically using semantic annotation on web resources August 9, 2004 Deborah L. McGuinness 6
Languages for representing term meaning – used to build ontologies Tools for generating, maintaining, and evolving ontologies Tools for reasoning with and using semantically enhanced applications August 9, 2004 Deborah L. McGuinness 7
Layer Cake Foundation
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What is an Ontology?
Catalog/ ID Thesauri “narrower term” relation Terms/ glossary Informal is-a Formal is-a Frames General (properties) Logical constraints Formal instance Disjointness, Value Restrs.
Inverse, part of… *based on AAAI ’99 Ontologies panel – McGuinness, Welty, Ushold, Gruninger, Lehmann August 9, 2004 Deborah L. McGuinness 9
General Nature of Descriptions
a WINE a LIQUID a POTABLE general categories grape: chardonnay, ... [>= 1] sugar-content: dry, sweet, off-dry color: red, white, rose price: a PRICE winery: a WINERY structured components grape dictates color (modulo skin) harvest time and sugar are related interconnections between parts August 9, 2004 Deborah L. McGuinness 10
General Nature of Descriptions
class superclass a WINE a LIQUID a POTABLE general categories
grape: chardonnay, ... [>= 1] sugar-content: dry, sweet, off-dry color: red, white, rose price: a PRICE winery: a WINERY structured components grape dictates color (modulo skin) harvest time and sugar are related interconnections between parts August 9, 2004 Deborah L. McGuinness 11
Some uses of Ontologies
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 August 9, 2004 Deborah L. McGuinness 12
Example Search Application
Research exemplar of many “smart” search applications
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Content to Search:
Research Site Technical Memorandum
Yellow Pages (Directory Westfield) Newspapers (Leader) Internal Sites (Rapid Prototyping) AT&T Solutions Worldnet Customer Care Medical Information
Pages or Databases
Domain Domain Knowledge Knowledge Search Engine
CLASSIC Knowledge Representation System Verity (and topic sets)
User Interface: August 9, 2004
GUI supporting browsing and selection Results (standard format) Results (domain specific)
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Collaborative Topic Set Tool
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Uses of Ontologies 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 August 9, 2004 Deborah L. McGuinness 19
KSL Wine Agent Semantic Web Integration
Wine Agent receives a meal description and retrieves a selection of matching wines available on the Web, using an ensemble of emerging standards and tools:
• DAML+OIL / OWL for representing a domain ontology of
foods, wines, their properties, and relationships between them
• JTP theorem prover for deriving appropriate pairings • DQL for querying a knowledge base consisting of the above • Inference Web for explaining and validating the response • [Web Services for interfacing with vendors] • Utilities for conducting and caching the above transactions August 9, 2004 Deborah L. McGuinness 20
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Given a description of a meal, Use OWL-QL/DQL to state a premise (the meal) and query the knowledge base for a suggestion for a wine description or set of instances Use JTP to deduce answers (and proofs) Use Inference Web to explain results (descriptions, instances, provenance, reasoning engines, etc.) Access relevant web sites (wine.com, …) to access current information Use OWL-S for markup and protocol*
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Querying multiple online sources
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Observations from the Wine Agent
Background knowledge is reasonably simple and built in OWL (includes foods and wine and pairing information similar to the OWL Guide, Ontology Engineering 101, CLASSIC Tutorial, …) Background knowledge can be used for simple query expansion over wine sources to retrieve for example documents about red wines (including zinfandel, syrah, …) Background knowledge used to interact with structured queries such as those possible on wine.com
Constraints allows a reasoner like JTP to infer consequences of the premises and query.
Explanation system (Inference Web) can provide provenance information such as information on the knowledge source (McGuinness’ wine ontology) and data sources (such as wine.com) Services work could allow automatic “matchmaking” instead of hand coded linkages with web resources August 9, 2004 Deborah L. McGuinness 27
Semantically Driven Information Rich Task Architecture: KANI
Keyword Search The World Semantic Search Knowledge Interaction
Relevant Knowledge Identification (TAP) Knowledge Browsing & Selection Corpus Knowledge Extraction Inference Web Explanation Generation Knowledge Transfer Extracted Knowledge DB Working KB Shared Reasoning Hypothesis Modeling & Testing Devil’s Advocate Entities Ontology Background KB Models August 9, 2004 Analysis Management Data
System Service System Component User Interface Feature Deborah L. McGuinness 28
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) e-commerce - Amazon, Yahoo! Shopping, VerticalNet, … Controlled vocabularies (for the web) abound - SIC codes, UMLS, UNSPSC, Open Directory (DMOZ), Rosetta Net, SUMO Business interest expanding – ontology directors, business ontologies are becoming more complicated (roles, value restrictions, …), VC firms interested, Markup Languages growing XML,RDF, DAML,OWL,RuleML, xxML “Real” ontologies are becoming more central to applications Search companies moving towards them – Yahoo, recently Google August 9, 2004 Deborah L. McGuinness 29
Implications and Needs for Ontology-enhanced applications
Ontology Language Syntax and Semantics (DAML+OIL, OWL) Upper Level and Domain ontologies for reuse (Cyc, SUMO, CNS coalition, DAML-S… UMLS, GO, …) Environments for Creation of Ontologies (Protégé, Sandpiper, Construct, OilEd, …) Environments for Maintenance of Ontologies (Chimaera, OntoBuilder, …) Reasoning Environments (Cerebra, Fact, JTP, Snark, …) Environment support for Explanation (Inference Web, …) Training (Conceptual Modeling, reasoning usage, tutorials – OWL Guide, Ontologies 101, OWL Tutorial, …) August 9, 2004 Deborah L. McGuinness 30
Extends vocabulary of XML and RDF/S
Rich ontology representation language
Language features chosen for efficient implementations Frame Systems Web Languages RDF/S XML DAML-ONT DAML+OIL OWL OIL Formal Foundations Description Logics FACT, CLASSIC, DLP, …
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W3C Web Ontology Working Group and OWL
WebOnt is part of W3C Semantic Web Activity aimed at extending meta-data efforts Begins from DAML+OIL W3C Note in 2001 Produces OWL in February 2004 which reached recommendation status OWL receives testimonials , news coverage , and usage escalates Best Practices Working Group Companies such as Network Inference, Sandpiper, etc support OWL as do open source and research orgs August 9, 2004 Deborah L. McGuinness 32
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visual ontology modeler™ (VOM) 1.x
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Rapid Modeling, Visual Editing
Provides graphical and text environment for editing
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Exports to OWL; Processed by Cerebra Server
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•Validation of input
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 • 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; used in HORUS to support counter-terrorism ontology generation • Available as a hosted service from www-ksl-svc.stanford.edu
• Information: August 9, 2004 www.ksl.stanford.edu/software/chimaera Deborah L. McGuinness 38
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
KBs developed by multiple authors will frequently Express overlapping knowledge in different, possibly contradictory
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
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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
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question answering tasks by abstracting, storing, exchanging, combining, annotating, filtering, segmenting, comparing, and rendering proofs and proof fragments provided by question answerers
IW’s Proof Markup Language (PML)
is an interlingua for proof interchange
is a distributed repository of meta-information related to proofs and their explanations
is an IW tool for displaying PML documents containing proofs and explanations (possibly from multiple inference engines)
is an IW tool for abstracting proofs into more understandable formats August 9, 2004 Deborah L. McGuinness 44
• The Semantic Web is arriving – annotation information is emerging – may be hand done or simple meta tags such as date, author, etc.
• 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 • Everyone is in the game – US Government (DARPA, NSF, NIST, ARDA…), EU, W3C, consortiums, business, … • Consulting and product companies are in the space (not just academics) • This is THE time for ontology work…. August 9, 2004 Deborah L. McGuinness 45
Languages are stable, endorsed, and available – e.g., OWL from W3C Tools are stable, although less standardized, available open source and commercially – e.g., Protégé, Sandpiper, Network Inference, …
Next session will introduce how to get started identifying requirements, language overview, and tool support with an example
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- McGuinness. Ontologies come of age , 2003 - Das, Wei, McGuinness, Industrial Strength Ontology Evolution Environments , 2002.
- Kendall, Dutra, McGuinness. Towards a Commercial Strength Ontology Development Environment , 2002.
- McGuinness Description Logics Emerge from Ivory Towers , 2001.
- McGuinness. Ontologies and Online Commerce , 2001.
- McGuinness. Conceptual Modeling for Distributed Ontology Environments , 2000.
- McGuinness, Fikes, Rice, Wilder. An Environment for Merging and Testing Large Ontologies , 2000.
- Brachman, Borgida, McGuinness, Patel-Schneider. Knowledge Representation meets Reality , 1999.
- McGuinness. Ontological Issues for Knowledge-Enhanced Search , 1998.
- McGuinness and Wright. Conceptual Modeling for Configuration, 1998.
Selected Tutorials: -Smith, Welty, McGuinness. OWL Web Ontology Language Guide , 2003.
-Noy, McGuinness. Ontology Development 101: A Guide to Creating your First Ontology . 2001.
-Brachman, McGuinness, Resnick, Borgida. How and When to Use a KL-ONE-like System , 1991.
Languages, Environments, Software:
- OWL http://www.w3.org/TR/owl-features/ , http://www.w3.org/TR/owl-guide/ - DAML+OIL: http://www.daml.org/ - Inference Web http://www.ksl.stanford.edu/software/iw/ - Chimaera http://www.ksl.stanford.edu/software/chimaera/ - FindUR - TAP – - DQL http://www.research.att.com/people/~dlm/findur/ http://tap.stanford.edu/ http://www.ksl.stanford.edu/projects/dql/ August 9, 2004 Deborah L. McGuinness 47
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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 August 9, 2004 Deborah L. McGuinness 49