A Framework for Integrating Deep and Shallow Semantic Structures in Text Mining.
ABSTRACT Recent work in knowledge representation undertaken as part of the Semantic Web initiative has enabled a common infrastructure (Resource De- scription Framework (RDF) and RDF Schema) for sharing knowledge of on- tologies and instances. In this paper we present a framework for combining the shallow levels of semantic description commonly used in MUC-style informa- tion extraction with the deeper semantic structures available in such ontologies. The framework is implemented within the PIA project software called Ontol- ogy Forge. Ontology Forge offers a server-based hosting environmentfor ontolo- gies, a server-side information extraction system for reducing the effort of writ- ing annotations and a many-featured ontology/annotation editor. We discuss the knowledge framework, some features of the system and summarize results from extended named entity experiments designed to capture instances in texts using support vector machine software.
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ABSTRACT: This paper reviews the developments in the past three years in the topics of Knowledge Capture, Knowledge Representation, and Knowledge Visualization, from a semantic Web ontology perspective. The paper tries to show that these three topics blend or even overlap one another. Concept Mapping is one particular unifying theme. The paper will try to shed light on this by reviewing several prototypes, leading to a discussion of research directions that aims to conclude that graphical representations will play a key role in KC, KR, and KV and the semantic Web. Moreover, the future of these fields will make use of both semiotics as well as the design of collaborative spaces—in addition to the technology that underlies them.
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ABSTRACT: This paper reports experiments in classifying texts based upon their favorability towards the subject of the text using a feature set enriched with topic information on a small dataset of music reviews hand-annotated for topic. The results of these experiments suggest ways in which incorporating topic information into such models may yield improvement over models which do not use topic information.
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ABSTRACT: Text mining of the biomedical literature provides patterns of relationships among concepts, people, and institutions, offering enhanced medical/ technical intelligence unobtainable by other means. This report describes myriad text mining capabilities. It starts with a description of the larger context of knowledge management, then, addresses components of text mining in particular. Section 1 covers biomedical knowledge management, the role of text mining in knowledge management, and describes the cultural changes and global agreements required to allow the full power and capabilities of text mining to be utilized. The next two sections address information retrieval issues. Section 2 describes the extraction of useful information from the published biomedical literature. Section 3 describes the information content in different record fields, in a major medical database.