Computing the Least Common Subsumer w.r.t. a Background Terminology

Baader, Franz; Sertkaya, Barış; Turhan, Anni-Yasmin

doi:10.1007/978-3-540-30227-8_34

Cited by 33 publications

(48 citation statements)

References 19 publications

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“…Most of the solutions exploit the (underlying) structure of the input descriptions, like trees for DL formulae (e.g., [1,14,27]) and for unary tree-shaped BGPQs [2,16], and directed single-root graphs for the RDF r-graphs of [7]. Roughly speaking, they all consist in a simultaneous traversal of the input descriptions, starting from their roots, while incrementally computing their lgg.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

“…Since then, it has also received consideration in the Knowledge Representation field, where least general generalizations were rebaptized least common subsumers [5], in Description Logics [1,5,14,27] and in Conceptual Graphs [3]. More recently, this problem started being investigated in RDF [7,9] and its associated SPARQL query language [2,10,16], the two prominent Semantic Web standards by W3C.…”

Section: Introductionmentioning

confidence: 99%

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Learning Commonalities in SPARQL

Hassad

Goasdoué

Jaudoin

2017

Lecture Notes in Computer Science

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Abstract. Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning. It was formalized in the early 70's as computing a least general generalization (lgg) of such descriptions. We revisit this well-established problem in the SPARQL query language for RDF graphs. In particular, and by contrast to the literature, we address it for the entire class of conjunctive SPARQL queries, a.k.a. Basic Graph Pattern Queries (BGPQs), and crucially, when background knowledge is available as RDF Schema ontological constraints, we take advantage of it to devise much more precise lggs, as our experiments on the popular DBpedia dataset show.

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Section: Related Work and Conclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Commonalities in SPARQL

Hassad

Goasdoué

Jaudoin

2017

Lecture Notes in Computer Science

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“…The case L 2 = ALC and L 1 = ALE has been studied [4] and it has been proved that the ALE LCS w.r.t. an acyclic ALC TBox always exists, while it cannot exist in case of cyclic or general TBoxes.…”

Section: Basicsmentioning

confidence: 99%

“…Disjunction is too weak to fulfill this task. We have exploited the so called good common subsumer, proposed by Baader et al [4], that let us generalize from several more specific concepts in a meaningful way.…”

Section: Introductionmentioning

confidence: 99%

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Dl-Link: A Conceptual Clustering Algorithm for Indexing Description Logics Knowledge Bases

d’Amato

Staab

Fanizzi

et al. 2010

Int. J. Semantic Computing

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Efficient resource retrieval is a crucial issue, particularly when semantic resource descriptions are considered which enable the exploitation of reasoning services during the retrieval process. In this context, resources are commonly retrieved by checking if each available resource description satisfies the given query. This approach becomes inefficient with the increase of available resources. We propose a method for improving the retrieval process by constructing a tree index through a new conceptual clustering method for resources expressed as class definitions or as instances of classes in ontology languages. The available resource descriptions are located at the leaf nodes of the index, while inner nodes represent intensional descriptions (generalizations) of their child nodes. The retrieval is performed by following the tree branches whose nodes satisfy the query. Query answering time may be improved as the number of retrieval steps may be O(log n) in the best case.

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Nonstandard Inferences in Description Logics: The Story So Far

Baader

Küsters

2006

International Mathematical Series

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Description logics (DLs) are a successful family of logic-based knowledge representation formalisms that can be used to represent the terminological knowledge of an application domain in a structured and formally well-founded way. DL systems provide their users with inference procedures that allow to reason about the represented knowledge. Standard inference problems (such as the subsumption and the instance problem) are now well-understood. Their computational properties (such as decidability and complexity) have been investigated in detail, and modern DL systems are equipped with highly optimized implementations of these inference procedures, which-in spite of their high worst-case complexity-perform quite well in practice.In applications of DL systems it has turned out that building and maintaining large DL knowledge bases can be further facilitated by procedures for other, nonstandard inference problem, such as computing the least common subsumer and the most specific concept, and rewriting and matching of concepts. While the research concerning these non-standard inferences is not as mature as the one for the standard inferences, it has now reached a point where it makes sense to motivate these inferences within a uniform application framework, give an overview of the results obtained so far, describe the remaining open problems, and give perspectives for future research in this direction.

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Computing the Least Common Subsumer w.r.t. a Background Terminology

Cited by 33 publications

References 19 publications

Learning Commonalities in SPARQL

Learning Commonalities in SPARQL

Dl-Link: A Conceptual Clustering Algorithm for Indexing Description Logics Knowledge Bases

Nonstandard Inferences in Description Logics: The Story So Far

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