Aggeliki Dimitriou

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Keyword search is the most popular paradigm for querying XML data on the web. In this context, three challenging problems are (a) to avoid missing useful results in the answer set, (b) to rank the results with respect to some relevance criterion and (c) to design algorithms that can efficiently compute the results on large datasets. In this paper, we(More)
Keyword search is the most popular technique for querying large tree-structured datasets, often of unknown structure, in the web. Recent keyword search approaches return lowest common ancestors (LCAs) of the keyword matches ranked with respect to their relevance to the keyword query. A major challenge of a ranking approach is the efficiency of its(More)
Keyword search is a popular technique for searching tree-structured data on the Web because it frees the user from knowing a complex query language and the structure of the data sources. However, the imprecision of the keyword queries usually results in a very large number of results of which only a few are relevant to the query. Multiple previous(More)
Keyword search has been for several years the most popular technique for retrieving information over semistructured data on the web. The reason of this unprecedented success is well known and twofold: (1) the user does not need to master a complex query language to specify her requests for data, and (2) she does not need to have any knowledge of the(More)
Keyword search is by far the most popular technique for searching linked data on the web. The simplicity of keyword search on data graphs comes with at least two drawbacks: difficulty in identifying results relevant to the user intent among an overwhelming number of candidates and performance scalability problems. In this paper, we claim that result ranking(More)
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