Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services

Doyle, Jon; Patil, Ramesh S.

doi:10.1016/0004-3702(91)90029-j

Cited by 125 publications

(60 citation statements)

References 14 publications

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“…Since DLs provide for both, they should be ideal candidates for ontology languages. That much was already clear ten years ago, but at that time there was a fundamental mismatch between the expressive power and the efficiency of reasoning that DL systems provided, and the expressivity and the large knowledge bases that users needed [67]. Through basic research in DLs over the last 10-15 years, as summarized in the introduction, this gap between the needs of ontologist and the systems that DL researchers provide has finally become narrow enough to build stable bridges.…”

Section: Important Extensions To Alcmentioning

confidence: 99%

Chapter 3 Description Logics

Baader¹,

Horrocks²,

Sattler³

2008

Foundations of Artificial Intelligence

152

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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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Section: Important Extensions To Alcmentioning

confidence: 99%

Chapter 3 Description Logics

Baader¹,

Horrocks²,

Sattler³

2008

Foundations of Artificial Intelligence

152

View full text Add to dashboard Cite

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“…not all information may be represented explicitly. False premises most commonly result from attempts to work around restrictive formalisms [1]. They are less of a problem with modern formalisms such as OWL using classifiers such as FaCT [5] or Racer [4].…”

Section: "Obstruction Which Involves Valve Of Heart" (Obstruction Andmentioning

confidence: 99%

“…i.e. is it appropriate to argue that "Super & not sub 1 & not sub 2 & not sub 3 … not sub n-1 implies sub n "? If the answer to either of these questions is "no", then the concept is treated as "self-standing".…”

Section: Criteria For Normalisation Of Implementations Of Domain Ontomentioning

confidence: 99%

Modularisation of domain ontologies implemented in description logics and related formalisms including OWL

Rector¹

2003

Proceedings of the International Conference on Knowledge Capture - K-Cap '03

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Modularity is a key requirement for large ontologies in order to achieve re-use, maintainability, and evolution. Mechanisms for 'normalisation' to achieve analogous aims are standard for databases. However, no similar notion of normalisation has yet emerged for ontologies. This paper proposes initial criteria for a two-step normalisation of ontologies implemented using OWL or related DL based formalisms. For the first -"ontological normalisation" -we accept Welty and Guarino's analysis. For the second -"implementation normalisation" -we propose an approach based on decomposing ("untangling") the ontology into independent disjoint skeleton taxonomies restricted to be simple trees, which can then be recombined using definitions and axioms to represent the relationships between them explicitly.

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“…Brachman and Levesque were advocating hybrid systems with radically restricted language for the 'Terminology box' ( 'T-Box'). However, Doyle and Patil's work trying to use such a restricted representation showed such restricted languages to be inadequate for medical terminology [34,35]. Equally discouraging, results by Touretzky [123], Etherington [36] and eventually Selman [113] made it clear that a complete solution to reasoning with defaults and exceptions in frame based systems was impossible.…”

Section: Everything Useful Is Intractable; Everything Tractable Is Usmentioning

confidence: 99%

AIM: a personal view of where I have been and where we might be going

Rector

2001

Artificial Intelligence in Medicine

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My own career in Medical Informatics and AI in Medicine has oscillated between concerns with medical records and concerns with knowledge representation with decision support as a pivotal integrating issue. It has focused on using AI to organise information and reduce 'muddle' and improve the user interfaces to produce 'useful and usable systems' to help doctors with a 'humanly impossible task'. Increasingly knowledge representation and ontologies have become the fulcrum for orchestrating re-use of information and integration of systems. Encouragingly, the dilemma between computational tractability and expressiveness is lessening, and ontologies and description logics are joining the mainstream both in AI in Medicine and in Intelligent Information Management generally. It has been shown possible to scale up ontologies to meet medical needs, and increasingly ontologies are playing a key role in meeting the requirements to scale up the complexity of clinical systems to meet the ever increasing demands brought about by new emphasis on reduction of errors, clinical accountability, and the explosion of knowledge on the Web.

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Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services

Cited by 125 publications

References 14 publications

Chapter 3 Description Logics

Chapter 3 Description Logics

Modularisation of domain ontologies implemented in description logics and related formalisms including OWL

AIM: a personal view of where I have been and where we might be going

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