TRANSLATOR: A TRANSlator from LAnguage TO Rules David Hirtle David R. Cheriton School of Computer Science University of Waterloo (Work done at the University.

Slides:



Advertisements
Similar presentations
Ontology-Based Computing Kenneth Baclawski Northeastern University and Jarg.
Advertisements

May 23, 2004OWL-S straw proposal for SWSL1 OWL-S Straw Proposal Presentation to SWSL Committee May 23, 2004 David Martin Mark Burstein Drew McDermott Deb.
A Semantic Web Approach to Digital Rights Management Roberto García González.
Requirements. UC&R: Phase Compliance model –RIF must define a compliance model that will identify required/optional features Default.
Three Theses of Representation in the Semantic Web
CH-4 Ontologies, Querying and Data Integration. Introduction to RDF(S) RDF stands for Resource Description Framework. RDF is a standard for describing.
First-Order Logic (and beyond)
Of 27 lecture 7: owl - introduction. of 27 ece 627, winter ‘132 OWL a glimpse OWL – Web Ontology Language describes classes, properties and relations.
Towards a hybrid approach to context modelling, reasoning and interoperation Karen Henricksen CRC for Enterprise Distributed Systems Technology (DSTC)
1 CSL Workshop, October 13-14, 2005 ESDI Workshop on Conceptual Schema Language and Tools - Aim, Scope, and Issues to be Addressed Anders Friis-Christensen,
ModelicaXML A Modelica XML representation with Applications Adrian Pop, Peter Fritzson Programming Environments Laboratory Linköping University.
Ontologies IS 277 Spring Outline n Ontologies n Types of ontologies n Examples n Ontology engineering n Ontology standards n Machine-readable ontologies.
DCS Architecture Bob Krzaczek. Key Design Requirement Distilled from the DCS Mission statement and the results of the Conceptual Design Review (June 1999):
COMP 6703 eScience Project Semantic Web for Museums Student : Lei Junran Client/Technical Supervisor : Tom Worthington Academic Supervisor : Peter Strazdins.
Semantics For the Semantic Web: The Implicit, the Formal and The Powerful Amit Sheth, Cartic Ramakrishnan, Christopher Thomas CS751 Spring 2005 Presenter:
XML on Semantic Web. Outline The Semantic Web Ontology XML Probabilistic DTD References.
CS 330 Programming Languages 09 / 16 / 2008 Instructor: Michael Eckmann.
From SHIQ and RDF to OWL: The Making of a Web Ontology Language
ANSWERING CONTROLLED NATURAL LANGUAGE QUERIES USING ANSWER SET PROGRAMMING Syeed Ibn Faiz.
Type-Directed, Whitespace-Delimited Parsing for Embedded DSLs Cyrus Omar School of Computer Science Carnegie Mellon University [GlobalDSL13] Benjamin ChungAlex.
Knowledge Mediation in the WWW based on Labelled DAGs with Attached Constraints Jutta Eusterbrock WebTechnology GmbH.
Evaluating Centralized, Hierarchical, and Networked Architectures for Rule Systems Benjamin Craig University of New Brunswick Faculty of Computer Science.
SymposiumPlanner-2011: Querying Two Virtual Organization Committees Zhili Zhao, Adrian Paschke, Chaudhry Usman Ali, and Harold Boley Corporate Semantic.
Principles of the SymposiumPlanner Instantiations of Rule Responder Zhili Zhao, Adrian Paschke, Chaudhry Usman Ali, and Harold Boley Corporate Semantic.
EARTH SCIENCE MARKUP LANGUAGE “Define Once Use Anywhere” INFORMATION TECHNOLOGY AND SYSTEMS CENTER UNIVERSITY OF ALABAMA IN HUNTSVILLE.
Shallow “Learning by Reading” In Slate Need either transition section from Selmer to Micah, or Title slide plus not-crappy title!
1 Expert Finding for eCollaboration Using FOAF with RuleML Rules MCeTECH May 2006 Jie Li 1,2, Harold Boley 1,2, Virendrakumar C. Bhavsar 1, Jing.
Marković Miljan 3139/2011
The 7th International Web Rule Symposium: Research Based and Industry Focused (RuleML 2013) July 11-13, 2013, Seattle, USA.
The Semantic Web Service Shuying Wang Outline Semantic Web vision Core technologies XML, RDF, Ontology, Agent… Web services DAML-S.
The Semantic Web William M Baker
SAWA: An Assistant for Higher-Level Fusion and Situation Awareness Christopher J. Matheus, Mieczyslaw M. Kokar, Kenneth Baclawski, Jerzy A. Letkowski,
A Z Approach in Validating ORA-SS Data Models Scott Uk-Jin Lee Jing Sun Gillian Dobbie Yuan Fang Li.
XML A web enabled data description language 4/22/2001 By Mark Lawson & Edward Ryan L’Herault.
Computer Science Department UoC. Outline Project Teams Key Points description Suggested Task Delegation Files Needed & previous work.
Resource Description Framework (RDF) Course: Electronic Document Team member: Ding Feng Ding Wei Wang Ling Date:
Semantic Web - an introduction By Daniel Wu (danielwujr)
1 The OO jDREW Reference Implementation of RuleML RuleML-2005, November 2005 Marcel Ball 1, Harold Boley 2, David Hirtle 1,2, Jing Mei 1,2, Bruce.
From POSL to d-POSL: Making the Positional-Slotted Language Defeasible Advisors: Nick Bassiliades, Efstratios Kontopoulos Instructor: Dr. Harold Boley.
Rules, RIF and RuleML.
Semantically Processing The Semantic Web Presented by: Kunal Patel Dr. Gopal Gupta UNIVERSITY OF TEXAS AT DALLAS.
Ontology-Based Computing Kenneth Baclawski Northeastern University and Jarg.
Programming Languages and Design Lecture 3 Semantic Specifications of Programming Languages Instructor: Li Ma Department of Computer Science Texas Southern.
OWL Representing Information Using the Web Ontology Language.
User Profiling using Semantic Web Group members: Ashwin Somaiah Asha Stephen Charlie Sudharshan Reddy.
RuleML Rules Lite Harold Boley, NRC IIT e-Business Said Tabet, Macgregor Corp With Key Contributions from the Joint Committee DAML PI Meeting, Captiva.
From RuleML 0.88 to 0.89 Sublanguages Beyond Horn Logic ― Validation and Translation David Hirtle NRC-IIT, UNB April 21, 2005 Update: June 8, 2005.
August 2003 At A Glance The IRC is a platform independent, extensible, and adaptive framework that provides robust, interactive, and distributed control.
Enhanced Content Models State and University Library, Denmark Open Repositories 2009 Asger Blekinge-Rasmussen Kåre Fiedler Christiansen.
©Silberschatz, Korth and Sudarshan10.1Database System Concepts W3C - The World Wide Web Consortium W3C - The World Wide Web Consortium.
Harold Boley NRC IIT e-Business MOST Workshop - Maritimes Open Source Technologies Université de Moncton Nov 10, 2004 Revised: Apr 14, 2005 The Open RuleML.
1 RIF Design Roadmap Draft PM Harold Boley (NRC), Michael Kifer (Stony Brook U), Axel Polleres (DERI), Jos de Bruijn (DERI), Michael Sintek.
The Semantic Web. What is the Semantic Web? The Semantic Web is an extension of the current Web in which information is given well-defined meaning, enabling.
A Portrait of the Semantic Web in Action Jeff Heflin and James Hendler IEEE Intelligent Systems December 6, 2010 Hyewon Lim.
LDK R Logics for Data and Knowledge Representation Propositional Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
Of 35 lecture 17: semantic web rules. of 35 ece 627, winter ‘132 logic importance - high-level language for expressing knowledge - high expressive power.
The International RuleML Symposium on Rule Interchange and Applications Visualization of Proofs in Defeasible Logic Ioannis Avguleas 1, Katerina Gkirtzou.
OWL Web Ontology Language Summary IHan HSIAO (Sharon)
 XML derives its strength from a variety of supporting technologies.  Structure and data types: When using XML to exchange data among clients, partners,
A Test Case Suite for Hornlog+ RuleML 1.01 A Test Case Suite for Hornlog+ RuleML 1.01 CS6795 Semantic Web Techniques Team 3: Zhenzhi Cui Radhika Yadav.
WonderWeb. Ontology Infrastructure for the Semantic Web. IST WP4: Ontology Engineering Heiner Stuckenschmidt, Michel Klein Vrije Universiteit.
Versatile Information Systems, Inc International Semantic Web Conference An Application of Semantic Web Technologies to Situation.
CmpE 583- Web Semantics: Theory and Practice RULES & RULE MARKUP
Rules, RIF and RuleML.
ece 720 intelligent web: ontology and beyond
Orlando Florida RuleML 2007 Thursday, October 25, 2007
Knowledge Representation and Inference
Towards Semantically Grounded Decision Rules Using ORM+
Logics for Data and Knowledge Representation
ONTOMERGE Ontology translations by merging ontologies Paper: Ontology Translation on the Semantic Web by Dejing Dou, Drew McDermott and Peishen Qi 2003.
Presentation transcript:

TRANSLATOR: A TRANSlator from LAnguage TO Rules David Hirtle David R. Cheriton School of Computer Science University of Waterloo (Work done at the University of New Brunswick) October 13, 2006

2 Outline Introduction Translating Language to Rules Attempto Controlled English Discourse Representation Structures Rule Markup Language Future Work Conclusion

3 Introduction Semantic Web still not widely used –Focus: machine-readable (meta)data facts, rules and ontologies –Problem: only experts can contribute formal standards like RDF and OWL (and soon RIF) are difficult to learn need to lower the barrier to entry

4 Example: Semantic Web rule student customer discount customer 15% Every student gets a discount of 15 percent.

5 Provide a user-friendly format –why not English? easy familiar expressive but ambiguous –“controlled English” avoids ambiguity formal, yet natural Our Approach

6 TRANSLATOR Java Web Start

7 GUI DRS Parser TRANSLATOR DRS grammar JavaCC APE webservice query (ACE + params) ACE RuleML, … DRS, … DRS RuleML Step 1: Input

8 Attempto Controlled English (1) Looks like English –Every honest student who does not procrastinate receives a good mark and easily passes the course. Actually a formal language, like RDF –tractable subset of English all ACE sentences are English, but not vice versa –unambiguously translatable into logic

9 Attempto Controlled English (2) Strategies for handling ambiguity: –exclude imprecise phrasings Students hate annoying professors. –interpretation rules The student brings a friend who is an alumnus and receives a discount. –Who receives the discount? »in ACE, student does (by default) »repeat relative pronoun for other interpretation The student brings a friend who is an alumnus and who receives a discount.

10 Attempto Controlled English (3) How can rules be expressed? –in natural language, many different forms e.g.,Everyone is mortal. All humanity is mortal. Every human being is mortal. For each person the person is mortal. If there is a member of the human race then he/she is mortal. –all above are valid ACE –further embellishment negation, relative clauses, etc.

11 Attempto Controlled English (4) What can’t yet be easily expressed? –“infix” implication The student is happy if there is no class. but TRANSLATOR supports it –just swap condition(s) and conclusion(s) »result: If there is no class then the student is happy. –production and reaction rules involve actions –If a student is caught cheating then send a report to the registrar’s office. require imperative mood (not yet in ACE)

12 GUI DRS Parser query (ACE + params) ACE RuleML, … DRS, … DRS RuleML Step 2: Query APE for DRS DRS grammar JavaCC TRANSLATOR APE webservice

13 Output by Attempto Parsing Engine (APE) Syntactic variant of first-order logic –facilitates translation to RuleML Basis is Discourse Representation Theory –formal way to handle contextual meaning across multiple sentences –developed by Hans Kamp (1981) APE uses extended “flat” notation –e.g., student(X)  object(X,…,student,…) Discourse Representation Structures

14 [] [A] object(A, atomic, student, person, cardinality, count_unit, eq, 1)-1 property(A, honest)-1 NOT [B] predicate(B, unspecified, procrastinate, A)-1 => [C, D, E, F] object(C, atomic, mark, object, cardinality, count_unit, eq, 1)-1 property(C, good)-1 predicate(D, unspecified, receive, A, C)-1 predicate(E, unspecified, pass, A, F)-1 modifier(E, manner, none, easily)-1 object(F, atomic, course, object, cardinality, count_unit, eq, 1)-1 Every honest student who does not procrastinate receives a good mark and easily passes the course. (DRS) (ACE)

15 DRS GUI DRS Parser query (ACE + params) ACE RuleML, … DRS, … RuleML Step 3: Parse DRS DRS grammar JavaCC TRANSLATOR APE webservice

16 RuleML DRS GUI DRS Parser query (ACE + params) ACE RuleML, … DRS, … Step 4: Map to RuleML DRS grammar JavaCC TRANSLATOR APE webservice

17 DRS-to-RuleML Mapping Performed “on-the-fly” by actions (Java code) embedded in DRS grammar Direct –preserves extended notation –uses positional RuleML syntax Explicit –e.g., quantifers:, Reversible –enables future rules  English extension

18 [] [A] object(A, … student, …) property(A, honest) NOT [B] predicate(B, … procrastinate, A) => [C, D, E, F] object(C, … mark, …) property(C, good) predicate(D, … receive, A, C) predicate(E, … pass, A, F) modifier(E, manner, … easily) object(F, … course, …) … A object … student … property … honest B predicate … procrastinate … C D E F object … mark … property … good predicate … receive … predicate … pass … modifier … easily object … course … … Every honest student who does not procrastinate receives a good mark and easily passes the course. (ACE) (DRS)(RuleML)

19 Rule Markup Language (1) Goal is interoperable rule markup –XSLT translators to other Semantic Web languages Family of “sublanguages” –modular XML Schemas –each represents well-known rule system –TRANSLATOR uses First-Order Logic sublanguage

20 Rule Markup Language (2) Why use RuleML? –ease of interchange (XML) –compatibility with RDF, OWL and SWRL also major input to W3C’s upcoming RIF –availability of tools OO jDREW, Mandarax, NxBRE, … –wide variety of features negation-as-failure, data types, weights, etc.

21 RuleML DRS GUI DRS Parser query (ACE + params) ACE RuleML, … DRS, … Step 5: Display results DRS grammar JavaCC TRANSLATOR APE webservice

22 Future Work Support new extensions in ACE 5 –modality If a student procrastinates and an assignment's due date is near then the student must work quickly. If the student misses the due date then he can only beg the professor for an extension. –negation as failure and passive voice If a transaction is not recorded by the bank then it is not provable that the transaction happens. Investigate adding option for “non-flat” notation Extend TRANSLATOR to be bidirectional (also capable of “verbalizing” rules)

23 Conclusion TRANSLATOR allows non-experts to write facts and rules for the Semantic Web –critical factor in success of original Web? Automated mapping from controlled English input to formal representation –ACE  DRS  RuleML Ongoing development by Attempto team

24 (includes Java Web Start demo) For more information