NOVÁČEK, Vít, Loredana LAERA, Siegfried HANDSCHUH and Brian DAVIS. Infrastructure for Dynamic Knowledge Integration -- Automated Biomedical Ontology Extension Using Textual Resources. Journal of Biomedical Informatics. Amsterdam: Elsevier, 2008, vol. 41, No 5, p. 816-828, 12 pp. ISSN 1532-0464.
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Basic information
Original name Infrastructure for Dynamic Knowledge Integration -- Automated Biomedical Ontology Extension Using Textual Resources
Name in Czech Infrastructure for Dynamic Knowledge Integration -- Automated Biomedical Ontology Extension Using Textual Resources
Authors NOVÁČEK, Vít (203 Czech Republic, guarantor), Loredana LAERA (380 Italy), Siegfried HANDSCHUH (276 Germany) and Brian DAVIS (372 Ireland).
Edition Journal of Biomedical Informatics, Amsterdam, Elsevier, 2008, 1532-0464.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Ireland
Confidentiality degree is not subject to a state or trade secret
WWW ScienceDirect
Impact factor Impact factor: 1.924
RIV identification code RIV/00216224:14330/08:00024234
Organization unit Faculty of Informatics
UT WoS 000260137300013
Keywords in English dynamic ontology integration; ontology evolution; ontology alignment and negotiation; ontology learning; biomedical ontologies; knowledge acquisition; lifecycle
Tags biomedical ontologies, dynamic ontology integration, knowledge acquisition, lifecycle, ontology alignment and negotiation, ontology evolution, ontology learning
Tags International impact, Reviewed
Changed by Changed by: doc. Mgr. Bc. Vít Nováček, PhD, učo 4049. Changed: 3/11/2008 12:36.
Abstract
We present a novel ontology integration technique that explicitly takes the dynam- ics and data-intensiveness of e-health and biomedicine application domains into ac- count. Changing and growing knowledge, possibly contained in unstructured natural language resources, is handled by application of cutting-edge Semantic Web tech- nologies. In particular, semi-automatic integration of ontology learning results into a manually developed ontology is employed. This integration bases on automatic negotiation of agreed alignments, inconsistency resolution and natural language generation methods. Their novel combination alleviates the end-user effort in the incorporation of new knowledge to large extent. This allows for efficient application in many practical use cases, as we show in the paper.
Abstract (in Czech)
We present a novel ontology integration technique that explicitly takes the dynam- ics and data-intensiveness of e-health and biomedicine application domains into ac- count. Changing and growing knowledge, possibly contained in unstructured natural language resources, is handled by application of cutting-edge Semantic Web tech- nologies. In particular, semi-automatic integration of ontology learning results into a manually developed ontology is employed. This integration bases on automatic negotiation of agreed alignments, inconsistency resolution and natural language generation methods. Their novel combination alleviates the end-user effort in the incorporation of new knowledge to large extent. This allows for efficient application in many practical use cases, as we show in the paper.
Links
1ET100300419, research and development projectName: Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu
Investor: Academy of Sciences of the Czech Republic, Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)
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