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Cited by 5 publications
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References 8 publications
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“…In Table II we analyze existing proposals for different OWL2 profiles: RDFS++ [1], L2 [6], RDF 3.0/OWLPrime [10], OWLSIF/pD* [27], OWL-LD [7] and OWL-RL [20]. We check which terms are most commonly used and propose a new version of OWL 2 called OWL-P. We also considered time complexity for detecting a required rule application and frequently used vocabulary terms in our corpus.…”
Section: B Owlmentioning
confidence: 99%
“…In Table II we analyze existing proposals for different OWL2 profiles: RDFS++ [1], L2 [6], RDF 3.0/OWLPrime [10], OWLSIF/pD* [27], OWL-LD [7] and OWL-RL [20]. We check which terms are most commonly used and propose a new version of OWL 2 called OWL-P. We also considered time complexity for detecting a required rule application and frequently used vocabulary terms in our corpus.…”
Section: B Owlmentioning
confidence: 99%
“…In [62], DyKnow, a stream-based knowledge processing middleware is introduced, which supports incremental reasoning with streams using Metrical Temporal Logic [99] as the underlying logical language. In [47], LarKC is introduced, a platform for Web-scale reasoning; further implementation attempts towards LarKC have led to the definition of its underlying language L2 [50], a lightweight language based on the RDFS vocabulary and a limited subset of OWL. For more temporal description logics, we refer to the survey [83].…”
Section: Temporal Data Reasoningmentioning
confidence: 99%
“…This language was later extended to RDFS 3.0 along similar principles [19]. Fisher et al propose a similar profile to RDFS-Plus called L2, where the rationale for including or excluding features is given on an ad-hoc basis [11]. A more detailed overview of the main features for these languages is also found in Table 2.…”
Section: Owl and Its Fragmentsmentioning
confidence: 99%