A language modeling approach to information retrieval
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Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
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ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
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ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
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ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
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A bottom-up, knowledge-aware approach to integrating and querying web data services
ACM Transactions on the Web (TWEB)
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Web Semantics: Science, Services and Agents on the World Wide Web
Proceedings of the 19th international conference on Intelligent User Interfaces
Exploratory search framework for Web data sources
The VLDB Journal — The International Journal on Very Large Data Bases
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Searching over heterogeneous structured data on the Web is challenging due to vocabulary and structure mismatches among different data sources. In this paper, we study two existing strategies and present a new approach to integrate additional data sources into the search process. The first strategy relies on data integration to mediate mismatches through upfront computation of mappings, based on which queries are rewritten to fit individual sources. The other extreme is keyword search, which does not require any up-front investment, but ignores structure information. Building on these strategies, we present a hybrid approach, which combines the advantages of both. Our approach does not require any upfront data integration, but also leverages the fine grained structure of the underlying data. For a structured query adhering to the vocabulary of just one source, the so-called seed query, we construct an entity relevance model (ERM), which captures the content and the structure of the seed query results. This ERM is then aligned on the fly with keyword search results retrieved from other sources and also used to rank these results. The outcome of our experiments using large-scale real-world data sets suggests that data integration leads to higher search effectiveness compared to keyword search and that our new hybrid approach consistently exceeds both strategies.