Using Description Logics for Managing Medical Terminologies

Cornet, Ronald; Abu‐Hanna, Ameen

doi:10.1007/978-3-540-39907-0_9

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…Another class of papers addresses representation and inference problems, such as formal representation of part-of relations, ontology mapping, or identification of redundant elements in concept definitions [32,33].…”

Section: Ontologies and Terminologiesmentioning

confidence: 99%

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Peek

Combi

Marín

et al. 2015

Artificial Intelligence in Medicine

103

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Section: Ontologies and Terminologiesmentioning

confidence: 99%

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Peek

Combi

Marín

et al. 2015

Artificial Intelligence in Medicine

103

View full text Add to dashboard Cite

“…Early works related to formal OL primarily focused on inducing disjoint axioms in a given ontology (learned or manual) so as to debug modeling errors. A heuristic approach (called semantic clarification) based on the assumption of strong disjointness [74] has been proposed in [75]. There has also been several machine learning (ML) based approaches to disjoint axiom induction.…”

Section: Formal Olmentioning

confidence: 99%

Formal Ontology Learning from English IS-A Sentences

Dasgupta¹,

Padia²,

Maheshwari³

et al. 2018

Preprint

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Ontology learning (OL) is the process of automatically generating an ontological knowledge base from a plain text document. In this paper, we propose a new ontology learning approach and tool, called DLOL, which generates a knowledge base in the description logic (DL) SHOQ(D) from a collection of factual non-negative IS-A sentences in English. We provide extensive experimental results on the accuracy of DLOL, giving experimental comparisons to three state-of-the-art existing OL tools, namely Text2Onto, FRED, and LExO. Here, we use the standard OL accuracy measure, called lexical accuracy, and a novel OL accuracy measure, called instance-based inference model. In our experimental results, DLOL turns out to be about 21% and 46%, respectively, better than the best of the other three approaches.

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“…Three such fragments, called DICE1, DICE2, and DICE3 in the following, were obtained from the DICE knowledge base [23], which comes from a medical application and defines concepts from the intensive care domain. The original DICE knowledge base contains more than 2000 concept definitions, is acyclic, and is written in the DL ALCQ, which extends ALC by so-called qualified number restrictions [35].…”

Section: The Background Tboxesmentioning

confidence: 99%

Computing the least common subsumer w.r.t. a background terminology

Baader

Sertkaya

Turhan

2007

Journal of Applied Logic

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Methods for computing the least common subsumer (lcs) are usually restricted to rather inexpressive Description Logics (DLs) whereas existing knowledge bases are written in very expressive DLs. In order to allow the user to re-use concepts defined in such terminologies and still support the definition of new concepts by computing the lcs, we extend the notion of the lcs of concept descriptions to the notion of the lcs w.r.t. a background terminology. We will show both theoretical results on the existence of the least common subsumer in this setting, and describe a practical approach-based on a method from formal concept analysis-for computing good common subsumers, which may, however, not be the least ones. We will also describe results obtained in a first evaluation of this practical approach.

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Using Description Logics for Managing Medical Terminologies

Cited by 11 publications

References 3 publications

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Formal Ontology Learning from English IS-A Sentences

Computing the least common subsumer w.r.t. a background terminology

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