The Experts below are selected from a list of 55812 Experts worldwide ranked by ideXlab platform
Thanh Tran - One of the best experts on this subject based on the ideXlab platform.
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Repeatable and reliable Semantic Search evaluation
Journal of Web Semantics, 2013Co-Authors: Roi Blanco, Harry Halpin, Daniel M. Herzig, Peter Mika, Jeffrey Pound, Henry S. Thompson, Thanh TranAbstract:An increasing amount of structured data on the Web has attracted industry attention and renewed reSearch interest in what is collectively referred to as Semantic Search. These solutions exploit the explicit Semantics captured in structured data such as RDF for enhancing document representation and retrieval, or for finding answers by directly Searching over the data. These data have been used for different tasks and a wide range of corresponding Semantic Search solutions have been proposed in the past. However, it has been widely recognized that a standardized setting to evaluate and analyze the current state-of-the-art in Semantic Search is needed to monitor and stimulate further progress in the field. In this paper, we present an evaluation framework for Semantic Search, analyze the framework with regard to repeatability and reliability, and report on our experiences on applying it in the Semantic Search Challenge 2010 and 2011.
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WWW (Companion Volume) - SemSearch'11: the 4th Semantic Search workshop
Proceedings of the 20th international conference companion on World wide web - WWW '11, 2011Co-Authors: Thanh Tran, Haofen Wang, Peter Mika, Marko GrobelnikAbstract:The use of Semantics and Semantic technologies for Search and retrieval has attracted interests both from academia and industry in recent years. What is now commonly known as Semantic Search is in fact a broad field encompassing ideas and concepts from different areas, including Information Retrieval, Semantic Web and database. This is the fourth edition of the Semantic Search workshop which aims to bring together reSearchers and practitioners from various communities, to provide a forum for dissemination, discussion, and for the exchange and transfer of knowledge related to the use of Semantics for Search and retrieval. This year's workshop will continue to push and promote efforts towards an evaluation benchmark for Semantic Search systems.
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Semantic Search using graph structured Semantic models for supporting the Search process
International Conference on Conceptual Structures, 2009Co-Authors: Thanh Tran, Peter Haase, Rudi StuderAbstract:Semantic Search attempts to go beyond the current state of the art in information access by addressing information needs on the Semantic level, i.e. considering the meaning of users' queries and the available resources. In recent years, there have been significant advances in developing and applying Semantic technologies to the problem of Semantic Search. To collate these various approaches and to try to better understand what the concept of Semantic Search entails, we describe Semantic Search from a process perspective. We argue that Semantics can be exploited in all steps of this process. We describe the elements involved in the process using graph-structured, Semantic models and present our existing work on Semantic Search in terms of this process.
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ICCS - Semantic Search --- Using Graph-Structured Semantic Models for Supporting the Search Process
Lecture Notes in Computer Science, 2009Co-Authors: Thanh Tran, Peter Haase, Rudi StuderAbstract:Semantic Search attempts to go beyond the current state of the art in information access by addressing information needs on the Semantic level, i.e. considering the meaning of users' queries and the available resources. In recent years, there have been significant advances in developing and applying Semantic technologies to the problem of Semantic Search. To collate these various approaches and to try to better understand what the concept of Semantic Search entails, we describe Semantic Search from a process perspective. We argue that Semantics can be exploited in all steps of this process. We describe the elements involved in the process using graph-structured, Semantic models and present our existing work on Semantic Search in terms of this process.
Haofen Wang - One of the best experts on this subject based on the ideXlab platform.
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WWW (Companion Volume) - SemSearch'11: the 4th Semantic Search workshop
Proceedings of the 20th international conference companion on World wide web - WWW '11, 2011Co-Authors: Thanh Tran, Haofen Wang, Peter Mika, Marko GrobelnikAbstract:The use of Semantics and Semantic technologies for Search and retrieval has attracted interests both from academia and industry in recent years. What is now commonly known as Semantic Search is in fact a broad field encompassing ideas and concepts from different areas, including Information Retrieval, Semantic Web and database. This is the fourth edition of the Semantic Search workshop which aims to bring together reSearchers and practitioners from various communities, to provide a forum for dissemination, discussion, and for the exchange and transfer of knowledge related to the use of Semantics for Search and retrieval. This year's workshop will continue to push and promote efforts towards an evaluation benchmark for Semantic Search systems.
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SPARK: Adapting keyword query to Semantic Search
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007Co-Authors: Qi Zhou, Haofen Wang, Miao Xiong, Chong Wang, Yong YuAbstract:Semantic Search promises to provide more accurate result than present-day\nkeyword Search. However, progress with Semantic Search has been delayed\ndue to the complexity of its query languages. In this paper, we explore\na novel approach of adapting keywords to querying the Semantic web:\nthe approach automatically translates keyword queries into formal\nlogic queries so that end users can use familiar keywords to perform\nSemantic Search. A prototype system named �SPARK� has been implemented\nin light of this approach. Given a keyword query, SPARK outputs a\nranked list of SPARQL queries as the translation result. The translation\nin SPARK consists of three major steps: term mapping, query graph\nconstruction and query ranking. Specifically, a probabilistic query\nranking model is proposed to select the most likely SPARQL query.\nIn the experiment, SPARK achieved an encouraging translation result.
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ISWC/ASWC - SPARK: adapting keyword query to Semantic Search
The Semantic Web, 2007Co-Authors: Qi Zhou, Chong Wang, Miao Xiong, Haofen WangAbstract:Semantic Search promises to provide more accurate result than present-day keyword Search. However, progress with Semantic Search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the Semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform Semantic Search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.
Rudi Studer - One of the best experts on this subject based on the ideXlab platform.
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Semantic Search using graph structured Semantic models for supporting the Search process
International Conference on Conceptual Structures, 2009Co-Authors: Thanh Tran, Peter Haase, Rudi StuderAbstract:Semantic Search attempts to go beyond the current state of the art in information access by addressing information needs on the Semantic level, i.e. considering the meaning of users' queries and the available resources. In recent years, there have been significant advances in developing and applying Semantic technologies to the problem of Semantic Search. To collate these various approaches and to try to better understand what the concept of Semantic Search entails, we describe Semantic Search from a process perspective. We argue that Semantics can be exploited in all steps of this process. We describe the elements involved in the process using graph-structured, Semantic models and present our existing work on Semantic Search in terms of this process.
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ICCS - Semantic Search --- Using Graph-Structured Semantic Models for Supporting the Search Process
Lecture Notes in Computer Science, 2009Co-Authors: Thanh Tran, Peter Haase, Rudi StuderAbstract:Semantic Search attempts to go beyond the current state of the art in information access by addressing information needs on the Semantic level, i.e. considering the meaning of users' queries and the available resources. In recent years, there have been significant advances in developing and applying Semantic technologies to the problem of Semantic Search. To collate these various approaches and to try to better understand what the concept of Semantic Search entails, we describe Semantic Search from a process perspective. We argue that Semantics can be exploited in all steps of this process. We describe the elements involved in the process using graph-structured, Semantic models and present our existing work on Semantic Search in terms of this process.
Qi Zhou - One of the best experts on this subject based on the ideXlab platform.
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SPARK: Adapting keyword query to Semantic Search
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007Co-Authors: Qi Zhou, Haofen Wang, Miao Xiong, Chong Wang, Yong YuAbstract:Semantic Search promises to provide more accurate result than present-day\nkeyword Search. However, progress with Semantic Search has been delayed\ndue to the complexity of its query languages. In this paper, we explore\na novel approach of adapting keywords to querying the Semantic web:\nthe approach automatically translates keyword queries into formal\nlogic queries so that end users can use familiar keywords to perform\nSemantic Search. A prototype system named �SPARK� has been implemented\nin light of this approach. Given a keyword query, SPARK outputs a\nranked list of SPARQL queries as the translation result. The translation\nin SPARK consists of three major steps: term mapping, query graph\nconstruction and query ranking. Specifically, a probabilistic query\nranking model is proposed to select the most likely SPARQL query.\nIn the experiment, SPARK achieved an encouraging translation result.
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ISWC/ASWC - SPARK: adapting keyword query to Semantic Search
The Semantic Web, 2007Co-Authors: Qi Zhou, Chong Wang, Miao Xiong, Haofen WangAbstract:Semantic Search promises to provide more accurate result than present-day keyword Search. However, progress with Semantic Search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the Semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform Semantic Search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.
Chong Wang - One of the best experts on this subject based on the ideXlab platform.
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SPARK: Adapting keyword query to Semantic Search
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007Co-Authors: Qi Zhou, Haofen Wang, Miao Xiong, Chong Wang, Yong YuAbstract:Semantic Search promises to provide more accurate result than present-day\nkeyword Search. However, progress with Semantic Search has been delayed\ndue to the complexity of its query languages. In this paper, we explore\na novel approach of adapting keywords to querying the Semantic web:\nthe approach automatically translates keyword queries into formal\nlogic queries so that end users can use familiar keywords to perform\nSemantic Search. A prototype system named �SPARK� has been implemented\nin light of this approach. Given a keyword query, SPARK outputs a\nranked list of SPARQL queries as the translation result. The translation\nin SPARK consists of three major steps: term mapping, query graph\nconstruction and query ranking. Specifically, a probabilistic query\nranking model is proposed to select the most likely SPARQL query.\nIn the experiment, SPARK achieved an encouraging translation result.
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ISWC/ASWC - SPARK: adapting keyword query to Semantic Search
The Semantic Web, 2007Co-Authors: Qi Zhou, Chong Wang, Miao Xiong, Haofen WangAbstract:Semantic Search promises to provide more accurate result than present-day keyword Search. However, progress with Semantic Search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the Semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform Semantic Search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.
