A comparison between conceptual graphs and KL-ONE

Biebow, Brigitte; Chaty, Guy

doi:10.1007/3-540-56979-0_4

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Their properties include the ability to represent sentences as partial graphs that define conceptual structures involving individual terms, and to combine partial graphs to express relationships between concepts. They also provide a standard way to represent knowledge which can be mapped onto other representations or formal systems, such as first-order logic [25] and semantic networks [26]. Another way to consider conceptual graphs consists of viewing them as a theory, including in a unique formalism both the means to build canonical conceptual graphs [27] and valid operations, which allow to build new conceptual graphs by refinement, join, and projection of existing conceptual graphs, without regarding links this theory may have to other knowledge representation systems [28].…”

Section: The Conceptual Graphs Theorymentioning

confidence: 99%

Review of Biomedical Knowledge and Data Representation with Conceptual Graphs

Volot

Joubert²,

Fieschi³

1998

Methods Inf Med

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Abstract:The basis of conceptual graphs theory is an ontology of types of concepts. Concepts issued from the ontology are interlinked by semantic relationships and constitute canonical conceptual graphs. Canonical graphs may be combined to derive new conceptual graphs by means of formation rules. This formalism allows to separate knowledge representation into a conceptual level and a domain-dependent level, and enables to share and reuse a representation. This paper presents conceptual graph applications to biomedical data and concept representation, classification systems, information retrieval, and natural language understanding and processing. A discussion on the unifying role conceptual graphs theory plays in the implementation of knowledge-based systems is also presented.

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Section: The Conceptual Graphs Theorymentioning

confidence: 99%

Review of Biomedical Knowledge and Data Representation with Conceptual Graphs

Volot

Joubert²,

Fieschi³

1998

Methods Inf Med

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“…Subsumption is central in both formalisms: between graphs or between types in CGs (generalization), between concepts in DLs. Similarities between these formalisms have often been pointed out [1,11] but up to now, to our knowledge, no formal study has ever been carried out about correspondences between CGs and DLs. Beyond an interesting theoretical result, such a work would offer CGs and DLs communities a mutual advantage of about 15 years of research.…”

Section: Introductionmentioning

confidence: 99%

Towards correspondences between Conceptual Graphs and Description Logics

Coupey¹,

Faron-Zucker

1998

Conceptual Structures: Theory, Tools and Applications

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We present a formal correspondence between Conceptual Graphs and Description Logics. More precisely, we consider the Simple Conceptual Graphs model provided with type definitions (which we call TSC~) and the .Af~,f.O7. standard Description Logic. We prove an equivalence between a subset of TSC~ and a subset of .AEF_.OZ. Based on this equivalence, we suggest extensions of both formalisms while preserving the equivalence. In particular, regarding to standard Description Logics where a concept can be defined by the conjunction of any two concepts, we propose an extension of type definition in CGs allowing type definitions from the "conjunction" of any two types and consequently partial type definitions. Symmetrically, regarding generalization/specialization operations in Conceptual Graphs, we conclude by suggesting how Description Logics could take advantage of these correspondences to improve the explanation of subsumption computation.

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“…Furthermore, CGs provide syntactic clarity together with powerful expressiveness about quantifier processing, which concerns the individualgeneric distinction supported by the flexibility of references. 13 The application of inference rules supports extracting knowledge which is not directly encoded in the domain ontology.…”

mentioning

confidence: 99%

Examining the Use of Conceptual Graphs in Adaptive Web-Based Systems That Aid Terminology Learning

Bontcheva

Dimitrova

2004

Int. J. Artif. Intell. Tools

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This paper discussed the use of Conceptual Graphs (CGs) for implementing tasks employed in web-based educational systems that aid terminology learning. Specifically, we focus on two critical issues in intelligent tutoring -student diagnosis and generation of adaptive explanations. Both tasks are demonstrated in terminological domains where learners have to familiarize themselves with concepts in a specific subject area (e.g. computing, finance, chemistry). Based on CG reasoning, robust and computationally tractable algorithms for student modelling and adaptive explanation generation are defined. Two intelligent systems are presented -STyLE-OLM and HYLITE+. STyLE-OLM is an interactive learner modelling system that extracts extended models of the learners' cognition. HYLITE+ is a natural language generation system that generates adaptive Web pages based on a learner model(LM). The two systems are complementary and have been implemented separately. However, considered together they cover most of the key tasks in adaptive web-based educational hypermedia that aid learning technical terminology. Based on evaluative studies of STyLE-OLM and HYLITE+, the use of CGs for interactive open student modelling and adaptive concept explanations is examined. The applicability of CGs in adaptive web-based systems that aid learning technical terminology is discussed.

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A comparison between conceptual graphs and KL-ONE

Cited by 6 publications

References 4 publications

Review of Biomedical Knowledge and Data Representation with Conceptual Graphs

Review of Biomedical Knowledge and Data Representation with Conceptual Graphs

Towards correspondences between Conceptual Graphs and Description Logics

Examining the Use of Conceptual Graphs in Adaptive Web-Based Systems That Aid Terminology Learning

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