1. Reasoning Breakout Session 7/20/01 Richard Fikes Mike Dean DAML PI Meeting Nashua, New Hampshire July 18-20, 2001

2. Knowledge Systems Laboratory, Stanford University2 Roles For Reasoning In The Semantic Web For ontology builders Classification Inconsistency detection For Web site builders Recognition Inconsistency detection For users seeking information Query answering For users seeking services to take actions Planning

3. Knowledge Systems Laboratory, Stanford University3 Why Reasoning Services? Ontology design Check class consistency and (unexpected) implied relationships Particularly important with large ontologies/multiple authors Ontology integration Assert inter-ontology relationships Reasoner computes integrated class hierarchy/consistency Ontology deployment Determine if set of facts are consistent w.r.t. ontology Determine if individuals are instances of ontology classes No point in having a semantics unless exploited by agents The Semantic Web needs a logic on top – Henry Thompson

4. Knowledge Systems Laboratory, Stanford University4 A Reasoner For Every Web Site Associate an information services agent with each Web site Associate an information services agent with each Web site An expert on the information contained in that site An expert on the information contained in that site Agent provides information services based on that expertise >Query answering using the markup on the site as its knowledge base >Some or all of the content of that knowledge base in various forms E.g., RDF statements, KIF logical theory, HTML document, … Agent is a knowledge server for the site Perhaps the *only* thing one encounters at a Web site is an agent(!) Perhaps the *only* thing one encounters at a Web site is an agent(!) Where one of the agents services is to provide the sites pages

5. Knowledge Systems Laboratory, Stanford University5 Query Answering Examples Declaring an inverse of a property Declaring an inverse of a property child Bill is a child of Joe. Is Joe a parent of Bill? Yes. Declaring a property to be a subproperty with a range Declaring a property to be a subproperty with a range father John is a father of Joe. Is John a parent of Joe? Yes. What is John? A Man.

6. Knowledge Systems Laboratory, Stanford University6 Query Answering Examples Inferences using toClass and hasValue Content A Seafood-Course is a Meal-Course. Every drink of a Seafood-Course has white as a color. New-Course is a Seafood-Course. W1 is a drink of New-Course. Key forward chaining rules >toClass rule 1 >(=> (and (PropertyValue onProperty ?r ?p) (PropertyValue hasValue ?r ?v) (PropertyValue hasValue ?r ?v) (Type ?i ?r)) (Type ?i ?r)) (PropertyValue ?p ?i ?v)) (PropertyValue ?p ?i ?v)) Query What is a color of W1? White

7. Knowledge Systems Laboratory, Stanford University7 Inconsistency Detection Example Incorrect portion of translated Wines KB (Type color Property) (PropertyValue maxCardinality color 1) Assumed assertions (PropertyValue subClassOf Restriction Class) (PropertyValue subClassOf Class rdfs:Class) (PropertyValue disjointWith Property rdfs:Class) (PropertyValue domain maxCardinality Restriction) Key forward chaining rules (=> (and (PropertyValue domain ?prop ?dm) (PropertyValue ?prop ?fr ?val)) (Type ?fr ?dm)) (=> (and (PropertyValue disjointWith ?c1 ?c2) (Type ?i ?c1) (Type ?i ?c2)) false) ConclusionInconsistent

8. Knowledge Systems Laboratory, Stanford University8 Reasoning Work By DAML Contractors Cycorp Cycorp's OpenCyc for DAML ontologies will provide taxonomic inferences as described at http://opencyc.sourceforge.net/daml/daml-taxonomic-inferences.html. Cycorp has provided java bindings for its ontology navigation api that will soon be published at http://www.opencyc.org and http://www.sourceforge.net/projects/opencyc. Stephen Reed Lockheed Martin, VIS, Kestrel Within the UBOT project (Lockheed Martin, VIS and Kestrel) we are working on consistency checking of DAML ontologies. We have developed a program called ConsVISor which checks whether all axioms of DAML are satisfied by a particular ontology or annotation. Additionally, we have translated DAML KIF axioms into Slang. This allowed us to perform "deeper" consistency checking of both the DAML axiomatization and of DAML ontologies and annotations. Once an ontology is translated to Slang, we can not only check its consistency, but also perform reasoning (theorem proving). More information on our efforts can be found at http://vis.home.mindspring.com.Mitch Kokar

9. Knowledge Systems Laboratory, Stanford University9 Reasoning Work By DAML Contractors Stanford KSL KSL is developing technology for reasoning with knowledge expressed in DAML on distributed Web sites. We are addressing both the standard issues about how to reason effectively with knowledge expressed in an object-oriented language augmented with rules and the issues raised by the knowledge using ontologies resident on (perhaps multiple) other Web sites. The technology includes a DAML reasoner called JTP implemented in JAVA that contains a general-purpose theorem prover integrated with a collection of special-purpose reasoners designed specifically for DAML+OIL and specific task domains. http://www.ksl.stanford.edu/projects/DAML/ Richard Fikes

10. Knowledge Systems Laboratory, Stanford University10 Reasoning Work By DAML Contractors Teknowledge While it's more the focus of other projects rather than our DAML effort, we are doing some work with extending Mark Stickel's PTTP theorem prover to support our inference needs. We can read and do first order logic inference on a version of KIF. Since we can translate KIF to DAML and back, we expect that this software will be useful for our DAML efforts in the coming year, especially as we develop more sophisticated ontology translation methods. Adam Pease

11. Knowledge Systems Laboratory, Stanford University11 Reasoning Work By DAML Contractors UMBC UMBC has developed an environment for reasoning with information expressed in DAML to support agents which do intelligent filtering of talk announcements as part of the ITTALKS application. The ITTALKS agent sends a user's agent ACL messages notifying them of new talks or changes to earlier talks using DAML as the "content language". The user's agent reasons about the new talk to decide (1) how well it matches the user's interests, (2) if it is feasible for him to attend based on his expected location and (3) it it fits his current schedule. If the outcome is positive, the agent places an item for the talk on the user's schedule. We are also doing a more limited range of reasoning with DAML using XSB in support of service description and discovery for bluetooth agents. Our current environment uses XSB as the inference engine, YAJXB as the bridge between XSB and Java, and the RDF API as a DAML parser. More information can be found at http://daml.umbc.edu/papers/, http://daml.umbc.edu/reasoning/ and by contacting Youyong Zou (mailto:yzou1@cs.umbc.edu). Tim Finin

12. Knowledge Systems Laboratory, Stanford University12 Reasoning Work By DAML Contractors University of Manchester The FaCT system (http://www.cs.man.ac.uk/FaCT) provides reasoning services for the SHIQ description logic via a CORBA client-server interface. A simple translation of DAML+OIL into SHIQ allows FaCT to be used as a reasoning service for DAML+OIL (a direct DAML+OIL interface is under development). By using a highly optimized implementation of a sound and complete tableaux algorithm, FaCT is able to provide reasoning services that are both efficient and effective. FaCT is used by both the OilEd (http://img.cs.man.ac.uk/oil) and Protégé ontology editors to provide subsumption and consistency checking support for ontology design. Ian Horrocks

13. Knowledge Systems Laboratory, Stanford University13 Reasoning Work By DAML Contractors Yale/BBN/Kestrel The Yale/BBN/Kestrel group is working on the problem of taking a service description from a web-based agent, and using it as the basis for planning. This raises several issues, all of which involve reasoning: (1) If the service description is in an unexpected vocabulary, how do you translate? (2) What must a service description look like in order for a planner to use it? (3) What changes to existing planners must be made for them to use these descriptions? (4) In the end, the only primitive actions you can take on the web are sending and receiving messages. How are these messages constructed and deconstructed?Drew McDermott

14. Knowledge Systems Laboratory, Stanford University14 A DAML+OIL Reasoning Working Group Promote interaction and collaboration among DAML contractors working on reasoning E-mail distribution list Web site Design consensus DAML query language? Design consensus DAML justification language?

15. Knowledge Systems Laboratory, Stanford University15 DAML Query Language Issues Relationship to DAML rules Is it confined to what is expressible in DAML+OIL? Expressive as SQL? >Would then be problematic to represent in DAML+OIL Because of operators like max, min, average, ascending

16. Knowledge Systems Laboratory, Stanford University16 Action Item Design query language for DAML+OIL Straw man proposal Premises >Conjunction of RDF statements containing premise variables >Premise variables treated as existential E.g., (and (Type ?sc Seafood-Course) (PropertyValue drink ?sc ?d)) Query pattern >Conjunction of RDF statements containing premise and query variables >Each answer is a binding of the query variables for which the query pattern is true E.g., (PropertyValue color ?d ?c)

17. Knowledge Systems Laboratory, Stanford University17 A DAML+OIL Reasoning Working Group Promote interaction and collaboration among DAML contractors working on reasoning E-mail distribution list Web site Design consensus DAML query language? Design a consensus language for querying DAML+OIL knowledge bases Design a consensus language for querying DAML+OIL knowledge bases Design consensus DAML justification language?

18. Knowledge Systems Laboratory, Stanford University18 DAML Justification Language Support both backward and forward inferencing Conclusions, e.g., answer to query, includes link to general info about the reasoner and its service Augment query to indicate what kind/depth of justifications are desired Permit iteration to investigate Minimal sufficient justification: {premises including sources} + {indication of KR entailment semantics} Crucial: attribute the multiple sources Related: delegation, trust policies dealing with conflict Typical rule/fact has source info for it Issue: pass-thru of justifications from sources

19. Knowledge Systems Laboratory, Stanford University19 A DAML+OIL Reasoning Working Group Promote interaction and collaboration among DAML contractors working on reasoning E-mail distribution list Web site Design consensus DAML query language? Design a consensus language for querying DAML+OIL knowledge bases Design a consensus language for querying DAML+OIL knowledge bases Design consensus DAML justification language? Do this in conjunction with and after design of DAML rule language. Do this in conjunction with and after design of DAML rule language.