منابع مشابه
RDivF: Diversifying Keyword Search on RDF Graphs
In this paper, we outline our ongoing work on diversifying keyword search results on RDF data. Given a keyword query over an RDF graph, we define the problem of diversifying the search results and we present diversification criteria that take into consideration both the content and the structure of the results, as well as the underlying
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Keyword search on graph structured data has attracted a lot of attention in recent years. Graphs are a natural “lowest common denominator” representation which can combine relational, XML and HTML data. Responses to keyword queries are usually modeled as trees that connect nodes matching the keywords. In this paper we address the problem of keyword search on graphs that may be significantly lar...
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XKeyword provides efficient keyword proximity queries on large XML graph databases. A query is simply a list of keywords and does not require any schema or query language knowledge for its formulation. XKeyword is built on a relational database and, hence, can accommodate very large graphs. Query evaluation is optimized by using the graph’s schema. In particular, XKeyword consists of two stages...
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Keyword search in node-labeled graphs finds subtrees of the graph whose nodes contain all of the input keywords. Previous work ranks answer trees using combinations of structural and contentbased metrics, such as path lengths between keywords or relevance of the labels in the answer tree to the query keywords. We propose two new ways to rank keyword search results over graphs. The first takes n...
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Abstract—Keyword Search is known as a user-friendly alternative for structured languages to retrieve information from graph-structured data. Efficient retrieving of relevant answers to a keyword query and effective ranking of these answers according to their relevance are two main challenges in the keyword search over graph-structured data. In this paper, a novel scoring function is proposed, w...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2017
ISSN: 1041-4347
DOI: 10.1109/tkde.2017.2690637