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  • × theme_ss:"Computerlinguistik"
  • × theme_ss:"Wissensrepräsentation"
  • × year_i:[2010 TO 2020}
  1. Clark, M.; Kim, Y.; Kruschwitz, U.; Song, D.; Albakour, D.; Dignum, S.; Beresi, U.C.; Fasli, M.; Roeck, A De: Automatically structuring domain knowledge from text : an overview of current research (2012) 0.01 
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    Abstract
    This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably as a useful tool within natural language processing, information retrieval and semantic web technology. Inspired by the ubiquitous propagation of domain model structures that are emerging in several research disciplines, we give an overview of the current research landscape and some techniques and approaches. We will also discuss trade-offs between different approaches and point to some recent trends.
  2. Shen, M.; Liu, D.-R.; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies (2012) 0.01 
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    Abstract
    Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is- part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.
  3. Rosemblat, G.; Resnick, M.P.; Auston, I.; Shin, D.; Sneiderman, C.; Fizsman, M.; Rindflesch, T.C.: Extending SemRep to the public health domain (2013) 0.01 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.1963-1974

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