RACER System Description

By Volker Haarslev, Ralf Möller. International Joint Conference on Automated Reasoning, 2001

1,111 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Ltd Registered in England and Wales Registered Number : 1072954 Registered office : (2004, methodology): “However, standard DL reasoners based on the tableaux algorithm (such as Racer (Haarslev and Möller 2001), FaCT (Horrocks 1998), Pellet (Parsia and Sirin 2004)), only provide a list of unsatisfiable classes. The process of “debugging” the ontology (i.e., to determine why a reasoner has inferred that ”
Handbook of Satisfiability (2021, methodology): “Thus, for instance, in our terminology Ksat [30, 32], Fact [42, 41], Dlp [58], Racer [35] are DPLL-based systems.”
Reasoning on UML class diagrams (2005, background): “However current state-of-the-art DL-based reasoning systems [18] [19] are not able to deal with n-ary relations, identification constraints, or functional dependency constraints.”
A software framework for matchmaking based on semantic web technology (2003, methodology): “The agent platform for the prototype was JADE, and the RACER DL reasoner was used to compute semantic matches between service advertisements and service requests [5,10].”
The OWL API: A Java API for OWL ontologies (2011, methodology): “A number of existing implementations including the CEL [1], FaCT++ [41], HermiT [31], Pellet [38], and Racer Pro [16] and SnoRocket reasoners7 provide OWL API wrappers.”
Non-Standard Reasoning Services for the Debugging of Description Logic Terminologies (2003, background): “such as RACER [Haarslev and Moller, 2001] or FaCT [Ilorrocks, 1998], can detect this type of inconsistency and the knowledge engineer can identify the cause of the problem.”
KnowRob: A knowledge processing infrastructure for cognition-enabled robots (2013, methodology): “These tasks are usually performed by DL reasoners such as Racer (Haarslev and Müller, 2001), Pellet (Sirin et al., 2007), or HermiT (Shearer et al., 2008) that maintain a fully classified knowledge base in memory.”
Fuzzy spatial relation ontology for image interpretation (2008, methodology): “To perform these reasoning tasks, we can use reasoning tools such as RACER [37], FACT++ 6 or PELLET.”
Pushing the EL Envelope (2005, methodology): “This line of research has resulted in the availability of highly optimized DL systems for expressive DLs [Horrocks, 1998; Haarslev and Möller, 2001], and successful applications: most notably the recommendation by the W3C of the DLbased language OWL [Horrocks et al.”
The MASTRO system for ontology-based data access (2011, methodology): “Differently from QuOnto, other well-known DL reasoners such as Racer [19], Pellet [32], and Fact++ [20] are essentially focused on standard ontology reasoning services (instance checking, ontology satisfiability, concept subsumption, etc.”
A Tableaux Decision Procedure for SHOIQ (2005, methodology): “The algorithm extends the well-known tableaux algorithm for SHIQ [Horrocks et al., 1999], which is the basis for several highly successful implementations [Horrocks & Patel-Schneider, 1998; Haarslev & Möller, 2001; Pellet, 2003].”
Reducing OWL Entailment to Description Logic Satisfability (2003, background): “Because of the syntactic restrictions in OWL DL, it is possible to develop an abstract syntax for OWL DL [16] that looks much like an abstract syntax for a powerful frame language, and is not very different from description logic syntaxes.”
OWL 2: The next step for OWL (2008, methodology): “The standardization of OWL has sparked the development and/or adaption of a number of reasoners,including FacT++[37],Pellet[32],RACER[ 11 ], and HermiT [26], and ontology editors, including Prot´eg´e 1 and Swoop[21].”
Query Answering for OWL-DL with Rules (2004, background): “A related logic, SHIQ(D) [13] [12], distinguished from SHOIN (D) mainly by not supporting nominals (or named objects), has been successfully implemented in practical reasoning systems , such as Racer [9] and FaCT [10].”
Web ontology segmentation: analysis, classification and use (2006, background): “RACER System Description.”
From SHIQ and RDF to OWL: the making of a Web Ontology Language (2003, methodology): “Moreover, there are practical optimized algorithms for inference in OWL Lite, such as the algorithm underlying the Description Logic systems FaCT [21] and RACER [14].”
Computing the Least Common Subsumer w.r.t. a Background Terminology (2004, methodology): “It is easy to see that, for DLs allowing for conjunction of descriptions, (b) cannot occur.”