K S L W i n e A g e n t : Testbed Application for Semantic Web Technologies Deborah McGuinness Eric Hsu Jessica Jenkins Rob McCool Sheila McIlraith Paulo Pinheiro da Silva Richard Fikes Knowledge Systems Laboratory, Computer Science Department, Stanford University  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 Premises: ((| | | (| | | Query Pattern: (| | ?x) Must Bind: ?x Answer Bundle Size Bound: NIL > Answer set: (X = | Process-Handle: #GR Semantic Web Example Integration Domain Ontology […] […]  DQL specifies abstract semantics of KB query-answering  Can be used as an agent-to-agent protocol and query language for interfacing with a networked knowledge base  Supports incomplete answer sets, discontinuous sessions, …  KSL DQL server processes incoming Wine Agent queries  Used to allow the wine agent to query the wines ontology loaded into JTP about consequences of the user defined scenario  Example: Finding descriptions of wines (or individual wines) that complement particular meal courses. The example below finds the wine color suggested with a seafood course. (Other queries map individual foods to course types, wines/varietals to properties, etc.)  In addition to being a pedagogical demonstration of the semantic web in action, the KSL Wine Agent has provided empirical input to research on:  DAML/OIL reasoner implementations  Reasoning from premises  DAML query language requirements  Semantic web integration issues  Explanation requirements  Defines classes of foods and wines and the restrictions between them, along with some some sample instances.  Excerpts: Bland Fish and Chardonnay classes and instances Reasoning System  JTP Theorem Prover infers pairings within given framework  Uses DAML Axioms to operationalize domain specification  Produces proofs for publication via Inference Web  Programmatic interface; knowledge base is loaded via URI and then embedded within DQL server  JTP available from the Stanford Knowledge Systems Lab  Querying the Wine Knowledge Base Justifying the Answers Web Services (current work)  Web-based reasoning/retrieval motivates explanation needs:  Provenance: verify that source information came from an up-to- date, trustworthy source.  Reasoning transparency: expose reasoner used and reasoning employed through browser navigation.  Interoperability: allow the sharing and composition of proofs from distributed and heterogeneous reasoners.  Reasoning abstraction: (in process) rewrite proof steps into more understandable proof fragments.  Wine Agent produces proofs in Inference Web DAML format  Further proof browser, portable proof, and registry information available from:  Example: Browsing proof that seafood pairs with white wine  Wine Agent accesses information about pricing and availability of wines through via a rudimentary on-site search engine.  used as information providing Web Service.  The inputs and outputs to will be annotated in DAML-S 0.9 the DAML+OIL/OWL ontology for Web Services.  Markup will enable automated invocation of the Web Service, facilitating interoperability, semantic translation of wine information, and enabling more sophisticated searching and filtering of wine information using the richer wine ontology provided by the Wine Agent.  More Wine Agent info: