Matches in ScholarlyData for { ?s ?p ?o. }
- 56 hasAuthorList authorList.
- 56 hasTopic Data_integration.
- 56 hasTopic Formal_languages.
- 56 hasTopic Inference.
- 56 hasTopic Logic.
- 56 hasTopic Ontology_%28computer_science%29.
- 56 hasTopic Ontology_alignment.
- 56 hasTopic Semantic_Web.
- 56 hasTopic Web_Ontology_Language.
- 56 isPartOf proceedings.
- 56 keyword "description logic".
- 56 keyword "dl-lite".
- 56 keyword "forgetting".
- 56 keyword "ontology".
- 56 keyword "restricting".
- 56 title "Restricting and forgetting in DL-Lite".
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- item-1 hasContent zhe-wang.
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- item-2 hasContent kewen-wang.
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- item-3 hasContent rodney-topor.
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- 63 creator haofen-wang.
- 63 creator kang-zhang.
- 63 creator qiaoling-liu.
- 63 creator thanh-tran.
- 63 creator yong-yu.
- 63 type InProceedings.
- 63 label "Q2Semantic: A Lightweight Keyword Interface to Semantic Search".
- 63 sameAs 63.
- 63 abstract "The increasing amount of data on the Semantic Web offers opportunities for semantic search. However, formal query hinders the casual users in expressing their information need as they might be not familiar with the query's syntax or the underlying ontology. Because keyword interfaces are easier to handle for casual users, many approaches aim to translate keywords to formal queries. However, these approaches yet feature only very basic query ranking and do not scale to large repositories. We tackle the scalability problem by proposing a novel clustered-graph structure that corresponds to only a summary of the original ontology. The so reduced data space is then used in the exploration for the computation of top-k queries. Additionally, we adopt several mechanisms for query ranking, which can consider many factors such as the query length and the relevance of ontology elements w.r.t. the query. Our experimental results performed against our implemented system Q2Semantic show that we achieve good performance on many datasets of different sizes.".
- 63 hasAuthorList authorList.
- 63 hasTopic Search_engine.
- 63 hasTopic Semantic_Web.
- 63 hasTopic User_interface.
- 63 isPartOf proceedings.
- 63 keyword "clustered-graph structure".
- 63 keyword "q2semantic".
- 63 keyword "query ranking".
- 63 keyword "scalability".
- 63 keyword "top-k queries".
- 63 title "Q2Semantic: A Lightweight Keyword Interface to Semantic Search".
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- item-1 hasContent haofen-wang.
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- item-2 hasContent kang-zhang.
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- 72 creator joerg-doerflinger.
- 72 creator kay-uwe-schmidt.
- 72 creator ljiljana-stojanovic.
- 72 creator mehdi-sahbi.
- 72 creator susan-thomas.
- 72 creator tirdad-rahmani.
- 72 type InProceedings.
- 72 label "An User Interface Adaptation Architecture for Rich Internet Applications".
- 72 sameAs 72.
- 72 abstract "The need for adaptive and personalized Rich Internet Application puts a new dimension to already existing approaches of Adaptive Hypermedia Systems. Instead of computing the adaptation steps at the server, Rich Internet Applications need a client-side approach that can react immediately on user input. In this paper we present a novel approach that holistically combines page annotations, semantic Web usage mining, user modeling, ontologies and rules to adapt AJAX pages. The focus of our pater is the conceptual introduction of the autonomous client. An autonomous client directly executes all necessary adaptation steps based on a user model, without requesting any logic on the server. In order to realize this, we use ontologies to annotate Rich Internet Applications and to describe the user model as well as semantic Web usage mining for detecting adaptation rules. Additionally, we provide a detailed overview and evaluation of how we moved resource-intensive ontology processing and rules execution from the server to the client.".
- 72 hasAuthorList authorList.
- 72 hasTopic Logic.
- 72 hasTopic Machine_learning.
- 72 hasTopic Personalization.
- 72 hasTopic Rule_of_inference.
- 72 hasTopic Semantic_Web.
- 72 hasTopic User_interface.