Pellet: A practical OWL-DL reasoner

By Evren Sirin, Bijan Parsia, Bernardo Cuenca Grau, Aditya Kalyanpur, Y. Katz. Journal of Web Semantics, 2007

3,275 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/17d41e6fd35629a9cc14f203b0035985f44500d5

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Citations in the corpus

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.”
The OWL API: A Java API for OWL ontologies (2011, methodology): “OWLSight 3 is a web based ontology browser written by Clark & Parsia that uses the Pellet reasoner.”
Integration of numeric and symbolic information for semantic image interpretation (2016, positive): “This is done by checking the consistency of KB extended with the ABox that represents I p 7 using a standard DL reasoner, e.g., Pellet [33].”
Integrating phenotype ontologies across multiple species (2010, positive): “We tried a variety of reasoning tools, including OWL-based reasoners such as Pellet, FaCT++ and HermiT [38-40].”
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity (2007, neutral): “We use the Knowledge Machine (KM) [6] to support these tasks in our technology platform, but other implementations such as Pellet [ 22 ] or Jena [17] can be used as well.”
Ontology Engineering in a Networked World (2012, methodology): “Data Knowl Eng 31(2):183–220 Gangemi A, Lehmann J, Presutti V, Nissim M, Catenacci C (2007) C-ODO: an OWL meta-model for collaborative ontology design. In: Fridman Noy N, Alani H, Stumme G, Mika P, Sure Y, Vrandecic D (eds) CKC, CEUR-WS.org Gardiner T, Horrocks I, Tsarkov D (2006) Automated Benchmar”
Semantics : Science , Services and Agents on the World Wide Web (2014, methodology): “Conversely, reasoners that implement OWL’s Direct Semantics – such as FaCT++ [51], HermiT [52], RacerPro [53], Pellet [54], etc. – often support existential semantics and anonymous individuals.”
Conjunctive Query Answering for the Description Logic SHIQ (2007, methodology): “Despite the high worst case complexity of the standard reasoning problems for very expressive DLs such asSHIQ, there are highly optimized implementations available, e.g., FaCT++ (Tsarkov & Horrocks, 2006), KAON21, Pellet ( Sirin, Parsia, Cuenca Grau, Kalyanpur, & Katz, 2006 ), and RacerPro2.”
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 Protégé 1 and Swoop [21].”
Ontology Matching (2007, methodology): “engines for these languages, such as Pellet (Sirin et al. 2007) or HermiT (Motik”
HermiT: An OWL 2 Reasoner (2014, methodology): “A key novel idea in HermiT is the hypertableau calculus [30], which allows the reasoner to avoid some of the nondeterministic behaviour exhibited by the tableau calculus used in Pellet [36] and FaCT++ [39]—two other popular and widely used OWL reasoners.”
Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision (2018, neutral): “These procedures are implemented in optimized tools called reasoners that are freely available, such as FaCT++ [105], HermiT [94], Pellet [96] and RacerPro [43].”
Relationships and data sanitization: a study in scarlet (2010, background): “Pellet [81] and FaCT++ [87] are two leading free and open-source reasoners that seem good candidates.”