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  Data Integration in the Life Sciences; ; Schneider, Reinhard  Book published by Springer Verlag (2017) This book constitutes the proceedings of the 12th International Conference on Data Integration in the Life Sciences, DILS 2017, held in Luxembourg, in November 2017. The 5 full papers and 5 short papers ... [more ▼] This book constitutes the proceedings of the 12th International Conference on Data Integration in the Life Sciences, DILS 2017, held in Luxembourg, in November 2017. The 5 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 16 submissions. They cover topics such as: life science data modelling; analysing, indexing, and querying life sciences datasets; annotating, matching, and sharing life sciences datasets; privacy and provenance of life sciences datasets. [less ▲] Detailed reference viewed: 192 (7 UL) Complaint Ontology Pattern - COP; ; et al in Workshop on Ontology and Semantic Web Patterns, Kobe 18 October 2016 (2016) In this paper we present an ontology design pattern to conceptualize complaints - an important domain still uncovered by ODPs. The proposed Complaint Ontology Pattern (COP) has been designed based on the ... [more ▼] In this paper we present an ontology design pattern to conceptualize complaints - an important domain still uncovered by ODPs. The proposed Complaint Ontology Pattern (COP) has been designed based on the analysis of free text complaints from available complaint datasets (banking, air transport, automobile) among other knowledge sources. We present a detailed use case from consumer disputes. We evaluate the pattern by annotating the complaints from our use case and by discussing how COP aligns to existing ontologies. [less ▲] Detailed reference viewed: 123 (5 UL) Adaptive Ontology-based Web Information Retrieval: The TARGET Framework; Guelfi, Nicolas ; in International Journal of Web Portals (2011), 3 Finding relevant information on the Web is difficult for most users. Although Web search applications are improving, they must be more 'intelligent' to adapt to the search domains targeted by queries, the ... [more ▼] Finding relevant information on the Web is difficult for most users. Although Web search applications are improving, they must be more 'intelligent' to adapt to the search domains targeted by queries, the evolution of these domains, and users' characteristics. In this paper, the authors present the TARGET framework for Web Information Retrieval. The proposed approach relies on the use of ontologies of a particular nature, called adaptive ontologies, for representing both the search domain and a user's profile. Unlike existing approaches on ontologies, the authors make adaptive ontologies adapt semi-automatically to the evolution of the modeled domain. The ontologies and their properties are exploited for domain specific Web search purposes. The authors propose graph-based data structures for enriching Web data in semantics, as well as define an automatic query expansion technique to adapt a query to users' real needs. The enriched query is evaluated on the previously defined graph-based data structures representing a set of Web pages returned by a usual search engine in order to extract the most relevant information according to user needs. The overall TARGET framework is formalized using first-order logic and fully tool supported. [less ▲] Detailed reference viewed: 36 (0 UL) |
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