The RacerPro knowledge representation and reasoning system

RacerPro is a software system for building applications based on ontologies. The backbone of RacerPro is a description logic reasoner. It provides inference services for terminological knowledge as well as for representations of knowledge about individuals. Based on new optimization techniques and techniques that have been developed in the research field of description logics throughout the years, a mature architecture for typical-case reasoning tasks is provided. The system has been used in hundreds of research projects and industrial contexts throughout the last twelve years. W3C standards as well as detailed feedback reports from numerous users have influenced the design of the system architecture in general, and have also shaped the RacerPro knowledge representation and interface languages. With its query and rule languages, RacerPro goes well beyond standard inference services provided by other OWL reasoners.

[1]  Franz Baader,et al.  KRIS: Knowledge Representation and Inference System , 1991, SGAR.

[2]  Diego Calvanese,et al.  OWLlink: DIG for OWL 2 , 2008, OWLED.

[3]  Michael Wessel Flexible und konfigurierbare Software-Architekturen für datenintensive ontologiebasierte Informationssysteme , 2008 .

[4]  Ralf Möller,et al.  Sound Summarizations for Alchi Ontologies - How to Speedup Instance Checking and Instance Retrieval , 2010, ICAART.

[5]  Bernhard Nebel,et al.  An Empirical Analysis of Optimization Techniques for Terminological Representation Systems, or Making KRIS Get a Move On , 1992, KR.

[6]  Ian Horrocks,et al.  The Even More Irresistible SROIQ , 2006, KR.

[7]  Peter Crowther,et al.  The DIG Description Logic Interface , 2003, Description Logics.

[8]  Boris Motik,et al.  Hypertableau Reasoning for Description Logics , 2009, J. Artif. Intell. Res..

[9]  Volker Haarslev,et al.  Optimizing Reasoning in Description Logics with Qualified Number Restrictions , 2001, Description Logics.

[10]  Edith Schonberg,et al.  Scalable highly expressive reasoner (SHER) , 2009, J. Web Semant..

[11]  Britta Hummel,et al.  Description Logic for Scene Understanding: at the Example of Urban Road Intersections , 2010 .

[12]  Volker Haarslev,et al.  Parallel TBox Classification in Description Logics - First Experimental Results , 2010, ECAI.

[13]  Volker Haarslev,et al.  High Performance Reasoning with Very Large Knowledge Bases: A Practical Case Study , 2000, IJCAI.

[14]  Michael Wessel,et al.  What Happened to Bob? Semantic Data Mining of Context Histories , 2009, Description Logics.

[15]  Peter F. Patel-Schneider,et al.  A Semantics and Complete Algorithm for Subsumption in the CLASSIC Description Logic , 1993, J. Artif. Intell. Res..

[16]  Ashok K. Chandra Theory of database queries , 1988, PODS '88.

[17]  Volker Haarslev,et al.  Optimization Techniques for Retrieving Resources Described in OWL/RDF Documents: First Results , 2004, KR.

[18]  Michael Wessel,et al.  A High Performance Semantic Web Query Answering Engine , 2005, Description Logics.

[19]  John C. Mallery A common LISP hypermedia server , 1994, WWW Spring 1994.

[20]  Diego Calvanese,et al.  EQL-Lite: Effective First-Order Query Processing in Description Logics , 2007, IJCAI.

[21]  Franz Baader,et al.  Qualifying Number Restrictions in Concept Languages , 1991, KR.

[22]  Ian Horrocks,et al.  Using an Expressive Description Logic: FaCT or Fiction? , 1998, KR.

[23]  Volker Haarslev,et al.  Algebraic tableau reasoning for the description logic SHOQ , 2010, J. Appl. Log..

[24]  Peter Baumgartner,et al.  A Novel Architecture for Situation Awareness Systems , 2009, TABLEAUX.

[25]  Volker Haarslev,et al.  Exploiting Pseudo Models for TBox and ABox Reasoning in Expressive Description Logics , 2001, IJCAR.

[26]  Volker Haarslev,et al.  Incremental Query Answering for Implementing Document Retrieval Services , 2003, Description Logics.

[27]  Rob Lemmens,et al.  Semantic interoperability of distributed geo-services , 2006 .

[28]  Bernd Neumann,et al.  Ontology-Based Reasoning Techniques for Multimedia Interpretation and Retrieval , 2008 .

[29]  Franz Baader,et al.  CEL - A Polynomial-Time Reasoner for Life Science Ontologies , 2006, IJCAR.

[30]  Volker Haarslev,et al.  On the Scalability of Description Logic Instance Retrieval , 2006, Journal of Automated Reasoning.

[31]  Volker Haarslev,et al.  Planning of Axiom Absorption , 2008, Description Logics.

[32]  Ian Horrocks,et al.  Optimizing Terminological Reasoning for Expressive Description Logics , 2007, Journal of Automated Reasoning.

[33]  Volker Haarslev,et al.  The Description Logic ALCNHR+ Extended with Concrete Domains: A Practically Motivated Approach , 2000, IJCAR.

[34]  Bijan Parsia,et al.  Optimizations for Answering Conjunctive ABox Queries , 2006, Description Logics.

[35]  Volker Haarslev,et al.  High Performance Absorption Algorithms for Terminological Reasoning , 2006, Description Logics.

[36]  Michael Wessel,et al.  A Probabilistic Abduction Engine for Media Interpretation based on Ontologies , 2010, UniDL.

[37]  Alexander Borgida,et al.  Description Logics in Data Management , 1995, IEEE Trans. Knowl. Data Eng..

[38]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..

[39]  Diego Calvanese,et al.  DL-Lite: Tractable Description Logics for Ontologies , 2005, AAAI.

[40]  Volker Haarslev,et al.  Optimizing Algebraic Tableau Reasoning for SHOQ: First Experimental Results , 2010, Description Logics.

[41]  Michael Wessel,et al.  The Difference a Day Makes - Recognizing Important Events in Daily Context Logs , 2007, C&O:RR.

[42]  Ralf Möller,et al.  Formalizing Multimedia Interpretation based on Abduction over Description Logic Aboxes , 2009, Description Logics.

[43]  Volker Haarslev,et al.  On the Scalability of Description Logic Instance Retrieval , 2006, KI.

[44]  Anni-Yasmin Turhan,et al.  On the computation of common subsumers in description logics , 2007 .

[45]  Ralf Möller,et al.  Updatable Island Reasoning for Alchi-ontologies , 2009, KEOD.

[46]  Deborah L. McGuinness,et al.  CLASSIC: a structural data model for objects , 1989, SIGMOD '89.

[47]  Volker Haarslev,et al.  Querying the Semantic Web with Racer + nRQL , 2004 .

[48]  Volker Haarslev,et al.  Optimization Strategies for Instance Retrieval , 2002, Description Logics.

[49]  Jon W. Freeman Hard Random 3-SAT Problems and the Davis-Putnam Procedure , 1996, Artif. Intell..

[50]  Silvana Castano,et al.  Multimedia Interpretation for Dynamic Ontology Evolution , 2009, J. Log. Comput..

[51]  Ralf Moeller,et al.  Distributed Island-based Query Answering for Expressive Ontologies , 2010, Description Logics.

[52]  Adam Pease,et al.  IEEE standard upper ontology: a progress report , 2002, The Knowledge Engineering Review.

[53]  Bijan Parsia,et al.  From Wine to Water: Optimizing Description Logic Reasoning for Nominals , 2006, KR.

[54]  Ian Horrocks,et al.  Reasoning with Individuals for the Description Logic SHIQ , 2000, CADE.

[55]  Bernhard Nebel,et al.  Am empirical analysis of optimization techniques for terminological representation systems , 1994, Applied Intelligence.

[56]  J. Freeman Improvements to propositional satisfiability search algorithms , 1995 .

[57]  Thorsten Liebig,et al.  Who the Heck Is the Father of Bob? , 2009, ESWC.

[58]  Michael Wessel,et al.  Design Principles and Realization Techniques for User Friendly, Interactive, and Scalable Ontology Browsing and Inspection Tools , 2007, OWLED.

[59]  Franz Baader,et al.  Pushing the EL Envelope , 2005, IJCAI.

[60]  Volker Haarslev,et al.  Expressive ABox Reasoning with Number Restrictions, Role Hierarchies, and Transitively Closed Roles , 2000, KR.

[61]  Volker Haarslev,et al.  The Revival of Structural Subsumption in Tableau-based Reasoners , 2008, Description Logics.

[62]  Ian Horrocks,et al.  A Tableaux Decision Procedure for SHOIQ , 2005, IJCAI.

[63]  Boris Motik,et al.  Reasoning in description logics using resolution and deductive databases , 2006 .

[64]  Sebastian Wandelt,et al.  Towards Scalable Instance Retrieval over Ontologies , 2010, Int. J. Softw. Informatics.

[65]  Sean Bechhofer,et al.  The OWL API: A Java API for Working with OWL 2 Ontologies , 2009, OWLED.