Topic
Knowledge extraction
About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.
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TL;DR: In this article, a rough set approach is proposed to discover classification rules through a process of knowledge induction which selects decision rules with a minimal set of features for classification of real-valued data.
191 citations
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TL;DR: A system that uses a representation of prototypical knowledge to guide computer consultations, and to focus the application of production rules used to represent inferential knowledge in the domain is presented.
191 citations
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TL;DR: It is proposed that this type of analysis could potentially be valuable for researchers in any field and presented using text mining to identify clusters and trends of related research topics from three major journals in the management information systems field.
Abstract: Text mining is a semi-automated process of extracting knowledge from a large amount of unstructured data. Given that the amount of unstructured data being generated and stored is increasing rapidly, the need for automated means to process it is also increasing. In this study, we present, discuss and evaluate the techniques used to perform text mining on collections of textual information. A case study is presented using text mining to identify clusters and trends of related research topics from three major journals in the management information systems field. Based on the findings of this case study, it is proposed that this type of analysis could potentially be valuable for researchers in any field.
191 citations
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Carleton University1, Stanford University2, University of New Brunswick3, Ontario Institute for Cancer Research4, University of Texas at El Paso5, University of Manchester6, Pompeu Fabra University7, National University of Ireland, Galway8, Rensselaer Polytechnic Institute9, Medical University of Vienna10, Technical University of Madrid11, University of Cambridge12
TL;DR: The Semanticscience Integrated Ontology is an ontology to facilitate biomedical knowledge discovery that provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services.
Abstract: The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real, hypothesized, virtual, fictional) objects, processes and their attributes. SIO specifies simple design patterns to describe and associate qualities, capabilities, functions, quantities, and informational entities including textual, geometrical, and mathematical entities, and provides specific extensions in the domains of chemistry, biology, biochemistry, and bioinformatics. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services. SIO is freely available to all users under a creative commons by attribution license. See website for further information: http://sio.semanticscience.org.
190 citations
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TL;DR: It is argued that a core ontology is one of the key building blocks necessary to enable the scalable assimilation of information from diverse sources and the subsequent building of a variety of services such as cross-domain searching, browsing, data mining and knowledge extraction.
Abstract: In this paper, we argue that a core ontology is one of the key building blocks necessary to enable the scalable assimilation of information from diverse sources. A complete and extensible ontology that expresses the basic concepts that are common across a variety of domains and can provide the basis for specialization into domain-specific concepts and vocabularies, is essential for well-defined mappings between domain-specific knowledge representations (i.e., metadata vocabularies) and the subsequent building of a variety of services such as cross-domain searching, browsing, data mining and knowledge extraction. This paper describes the results of a series of three workshops held in 2001 and 2002 which brought together representatives from the cultural heritage and digital library communities with the goal of harmonizing their knowledge perspectives and producing a core ontology. The knowledge perspectives of these two communities were represented by the CIDOC/CRM [31], an ontology for information exchange in the cultural heritage and museum community, and the ABC ontology [33], a model for the exchange and integration of digital library information. This paper describes the mediation process between these two different knowledge biases and the results of this mediation - the harmonization of the ABC and CIDOC/CRM ontologies, which we believe may provide a useful basis for information integration in the wider scope of the involved communities.
190 citations