An empirical analysis of terminological representation systems

Heinsohn, Jochen; Kudenko, Daniel; Nebel, Bernhard; Profitlich, Hans-Jürgen

doi:10.1016/0004-3702(94)90071-x

Cited by 36 publications

(15 citation statements)

References 19 publications

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“…Studies on selected services have shown that websites providing appropriate metadata for SearchMonkey have a 15% higher click-trough-rate once the website made it to the top 10 results. 30 Other companies such as BestBuy.com even report higher traffic of about 30% since they have started publishing semantic data with their website in September 2009. describe its e-commerce offerings. Also Google has started exploiting semantic data from e-commerce websites describing their products with the GoodRelations vocabulary.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, description logics systems, which are now the backbone of Web Ontologies, were severely limited in scale, typically capable of handling not more than a few hundred concepts in the mid 1990's (cf. [30]). In the meanwhile, several such problems have been overcome using much increased computational power as well as better understood and optimized algorithms leading to the practical handling of large ontologies such as SNOMED 6 with several hundreds of thousands of axioms (cf.…”

Section: Architecture For Representing Knowledge On the Webmentioning

confidence: 99%

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The Semantic Web: Collective Intelligence on the Web

Janik

Scherp

Staab

2011

Informatik Spektrum

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Abstract. The World Wide Web has turned hypertext into a success story by enabling world-wide sharing of unstructured information and informal knowledge. The Semantic Web targets the sharing of structured information and formal knowledge pursuing objectives of achieving collective intelligence on the Web. Germane to the structure of the Semantic Web is a layering and standardization of concerns. These concerns are reflected by an architecture of the Semantic Web that we present through a common use case. Semantic Web data for the use case is now found on the Web and is part of a quickly growing set of Semantic Web resources available for formal processing.

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Section: Resultsmentioning

confidence: 99%

Section: Architecture For Representing Knowledge On the Webmentioning

confidence: 99%

The Semantic Web: Collective Intelligence on the Web

Janik

Scherp

Staab

2011

Informatik Spektrum

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“…an arbitrary KB) is of a relatively high complexity (EXPTIME-complete), the pathological cases that lead to such high worst case complex-ity are rather artificial, and rarely occur in practice [127,86,155,95]. Even in realistic applications, however, problems can occur that are much too hard to be solved by naive implementations of theoretical algorithms such as the one sketched in Section 3.4.1.…”

Section: Implementation and Optimisation Techniquesmentioning

confidence: 99%

Chapter 3 Description Logics

Baader¹,

Horrocks²,

Sattler³

2008

Foundations of Artificial Intelligence

151

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In this chapter we will introduce description logics, a family of logic-based knowledge representation languages that can be used to represent the terminological knowledge of an application domain in a structured way. We will first review their provenance and history, and show how the field has developed. We will then introduce the basic description logic ALC in some detail, including definitions of syntax, semantics and basic reasoning services, and describe important extensions such as inverse roles, number restrictions, and concrete domains. Next, we will discuss the relationship between description logics and other formalisms, in particular first order and modal logics; the most commonly used reasoning techniques, in particular tableau, resolution and automata based techniques; and the computational complexity of basic reasoning problems. After reviewing some of the most prominent applications of description logics, in particular ontology language applications, we will conclude with an overview of other aspects of description logic research, and with pointers to the relevant literature. IntroductionDescription logics (DLs) [14,25,50] are a family of knowledge representation languages that can be used to represent the knowledge of an application domain in a structured and formally well-understood way. The name description logics is motivated by the fact that, on the one hand, the important notions of the domain are described by concept descriptions, i.e., expressions that are built from atomic concepts (unary predicates) and atomic roles (binary predicates) using the concept and role constructors provided by the particular DL; on the other hand, DLs differ from their predecessors, such as semantic networks and frames, in that they are equipped with a formal, logic-based semantics.We will first illustrate some typical constructors by an example; formal definitions will be given in Section 3.2. Assume that we want to define the concept of "A man that is married to a doctor, and all of whose children are either doctors or professors." This concept can be described with the following concept description:Human ¬Female (∃married.Doctor) (∀hasChild.(Doctor Professor)).

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“…Yelland (53) for instance, proposed a scheme for integration of subsumption mechanisms smoothly integrated with object structures. The main problem with these powerful mechanisms is that they are not complete (for ex., see (27) for an evaluation of various terminological systems). In our context, we believe that the sophistication of subsumption mechanisms is not needed, since the collection of potential solutions is to support only limited extensions.…”

Section: Representing the Taxonomy Of Possible Solutions -Partmentioning

confidence: 99%

Effective Domain-Dependent Reuse in Medical Knowledge Bases

Dojat

1995

Computers and Biomedical Research

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An empirical analysis of terminological representation systems

Cited by 36 publications

References 19 publications

The Semantic Web: Collective Intelligence on the Web

The Semantic Web: Collective Intelligence on the Web

Chapter 3 Description Logics

Effective Domain-Dependent Reuse in Medical Knowledge Bases

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