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WIREs Data Mining Knowl Discov
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Automatic construction of lexicons, taxonomies, ontologies, and other knowledge structures

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Abstract, structured, representations of knowledge such as lexicons, taxonomies, and ontologies have proven to be powerful resources not only for the systematization of knowledge in general, but to support practical technologies of document organization, information retrieval, natural language understanding, and question‐answering systems. These resources are extremely time consuming for people to create and maintain, yet demand for them is growing, particularly in specialized areas ranging from legacy documents of large enterprises to rapidly changing domains such as current affairs and celebrity news. Consequently, researchers are investigating methods of creating such structures automatically from document collections, calling on the proliferation of interlinked resources already available on the web for background knowledge and general information about the world. This review surveys what is possible, and also outlines current research directions. This article is categorized under: Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Structure Discovery and Clustering
Examples of semantic relations.
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(a) Overall and (b) relative numbers of research publications in recent years.
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Interest in DBpedia, Freebase, and WordNet.
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Example of logical inconsistency in an ontology.
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Ontology building workflow in rapid ontology construction (ROC).
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(1) Merging, (2) compressing, and (3) pruning upper levels of WordNet's hypernym paths into a facet hierarchy.
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Example of paraphrase identification.
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n‐Grams versus noun phrases.
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Improvement in Watson's performance (the dot cloud shows people's performance).
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Relating a fragment of text to Wikipedia.
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Entry for ‘Africa’ in the New York Times taxonomy.
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Simple knowledge organization system (SKOS) core vocabulary for the Agrovoc Thesaurus; each circle represents a concept.
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The relation between complexity and expressiveness.
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Technologies > Structure Discovery and Clustering
Fundamental Concepts of Data and Knowledge > Knowledge Representation
Algorithmic Development > Text Mining

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