scispace - formally typeset
Search or ask a question
Topic

Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².


Papers
More filters
Journal ArticleDOI
TL;DR: The characteristics which make them suitable for representing knowledge and control strategies in expert systems are analyzed.

91 citations

Journal ArticleDOI
TL;DR: An efficient evaluation of all existing semantic similarity methods based on structure, information content and feature approaches is given to help researcher and practitioners to select the measure that best fit for their requirements.
Abstract: In recent years, semantic similarity measure has a great interest in Semantic Web and Natural Language Processing (NLP). Several similarity measures have been developed, being given the existence of a structured knowledge representation offered by ontologies and corpus which enable semantic interpretation of terms. Semantic similarity measures compute the similarity between concepts/terms included in knowledge sources in order to perform estimations. This paper discusses the existing semantic similarity methods based on structure, information content and feature approaches. Additionally, we present a critical evaluation of several categories of semantic similarity approaches based on two standard benchmarks. The aim of this paper is to give an efficient evaluation of all these measures which help researcher and practitioners to select the measure that best fit for their requirements.

91 citations

01 Jan 2001
TL;DR: It is shown that product configuration knowledge can be represented systematically and compactly using a logic program type rule language such that the answers of a configuration task, the configurations, correspond to the models of the rule representation.
Abstract: The paper demonstrates that product configuration applications fit naturally the framework of answer set programming. It is shown that product configuration knowledge can be represented systematically and compactly using a logic program type rule language such that the answers of a configuration task, the configurations, correspond to the models of the rule representation.. The paper pr.es.~nts such a"}ule-bised forrealization of a unified configuration oni~o]ogy using a" weight constraint rule language. The language extends normal logic programs with cardinality and weight constraints which leads to a compact and simple formalization. The complexity of the configuration task defined by the formalization is shown to be NP-complete.

91 citations

Journal Article
TL;DR: The focus of the effort is the development of SPECIALIST, an experimental natural language processing system for the biomedical domain that includes a broad coverage parser supported by a large lexicon, modules that provide access to the extensive Unified Medical Language System Knowledge Sources, and a retrieval module that permits experiments in information retrieval.
Abstract: This paper describes efforts to provide access to the free text in biomedical databases. The focus of the effort is the development of SPECIALIST, an experimental natural language processing system for the biomedical domain. The system includes a broad coverage parser supported by a large lexicon, modules that provide access to the extensive Unified Medical Language System (UMLS) Knowledge Sources, and a retrieval module that permits experiments in information retrieval. The UMLS Metathesaurus and Semantic Network provide a rich source of biomedical concepts and their interrelationships. Investigations have been conducted to determine the type of information required to effect a map between the language of queries and the language of relevant documents. Mappings are never straightforward and often involve multiple inferences.

91 citations

Journal ArticleDOI
TL;DR: A fuzzy neural network is proposed to enhance the learning ability of FCMs and incorporates the inference mechanism of conventional FCMs with the determination of membership functions, as well as the quantification of causalities.
Abstract: The fuzzy cognitive map (FCM) has gradually emerged as a powerful paradigm for knowledge representation and a simulation mechanism that is applicable to numerous research and application fields. However, since efficient methods to determine the states of the investigated system and to quantify causalities that are the very foundations of FCM theory are lacking, constructing FCMs for complex causal systems greatly depends on expert knowledge. The manually developed models have a substantial shortcoming due to the model subjectivity and difficulties with assessing its reliability. In this paper, we proposed a fuzzy neural network to enhance the learning ability of FCMs. Our approach incorporates the inference mechanism of conventional FCMs with the determination of membership functions, as well as the quantification of causalities. In this manner, FCM models of the investigated systems can automatically be constructed from data and, therefore, operate with less human intervention. In the employed fuzzy neural network, the concept of mutual subsethood is used to describe the causalities, which provides more transparent interpretation for causalities in FCMs. The effectiveness of the proposed approach in handling the prediction of time series is demonstrated through many numerical simulations.

91 citations


Network Information
Related Topics (5)
User interface
85.4K papers, 1.7M citations
82% related
Graph (abstract data type)
69.9K papers, 1.2M citations
81% related
Genetic algorithm
67.5K papers, 1.2M citations
79% related
Robot
103.8K papers, 1.3M citations
79% related
Fuzzy logic
151.2K papers, 2.3M citations
79% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202378
2022192
2021390
2020528
2019566
2018509