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integrate information from multiple interrelated pages to answer keyword queries meaningfully. Next-generation web search engines require link-awareness, or more generally, the capability of integrating correlative information items that are linked through hyperlinks. In this paper, we study the problems of identifying the
Meaningful and useful return information is extraordinary important for information retrieval and XML keyword search. In this work, based on analysis the structure of XML document, we propose an algorithm to classify return matched nodes, we present formal analysis on LCA (lowest common ancestor) nodes ranking and LCA
The study on XML keyword search gradually becomes the focus of information retrieval. Most previous XML keyword search algorithms are based on SLCA (smallest lowest common ancestor), but in the process of keyword search, we discover that some weakness or flaw exists in SLCA, it is summarized as follows: (1) the query
With the XML becomes a de-facto standard for exchanging and presenting information, the study on XML keyword search has become the focus of information retrieval. Several recent studies have finished the effective XML keyword search, but not all approach is effective in identifying return information, not all search
Keyword search is considered to be an effective information discovery method for both structured and semi-structured data. In XML keyword search, query semantics is based on the concept of Lowest Common Ancestor (LCA). However, naive LCA-based semantics leads to exponential computation and result size. In the
XML filter approaches aim at XPath queries. However, many users tend to use keywords to describe requirements. SLCA (Smallest Lowest Common Ancestor)-based XML keyword search is one of the most important information retrieval approaches. Former approaches focus on building centralized index for a large scale of XML
answers, such as short-answer questions, discussion questions etc. There are two factors that will affect the subjective item scoring: knowledge point and the nearness level. The unidirectional nearness algorithm in the fuzzy mathematics only focus on the keyword matching, but ignore the scoring of the knowledge point and
consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multidimensional querying approach to semi-structured data searches in personal information systems by allowing users to provide fuzzy structure and metadata conditions in addition to traditional keyword
when the sentence is analyzed. The goal is to put each noun and verb of the sentence on the right place on the tree. Taking this information into account, it is possible to solve the ambiguity problem for the query keywords and create the indicative summaries taking into account query words, and semantically related
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