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A user who wants to get information from a relational database needs to know database schema and structured query languages like SQL. The ordinary users are not familiar to those things, so searching information from relational databases is hard to them. Keyword search is a solution of the problem, where a keyword
This paper proposes a strategy of the summary sentence selection for query-focused multi-document summarization through extracting keywords from relevant document set. It calculates the query related feature and the topic related feature for every word in relevant document set, then obtains the importance of the word
Identifying prospective customers is an important aspect of marketing research. In this paper, we provide support for a new type of query, the Reverse Top-k Geo-Social Keyword (RkGSK) query. This query takes into account spatial, textual, and social information, and finds prospective customers for geotagged objects
Given a set of keywords, we find a maximum Web query (containing the most keywords possible) that respects user-defined bounds on the number of returned hits. We assume a real-world setting where the user is not given direct access to a Web search engine's index, i.e., querying is possible only through an interface
uses presentation video and slides. A user inputs query keywords, which are used to extract the most appropriate interval for the query according to appearance positions in the media. This enables the user to extract intervals that can be viewed more efficiently than when the interval depends only on the presence of query
Language Model (LM) constitutes one of the key components in Keyword Spotting (KWS). The rapid development of the World Wide Web (WWW) makes it an extremely large and valuable data source for LM training, but it is not optimal to use the raw transcripts from WWW due to the mismatch of content between the web corpus
This paper presents a text query-based method for keyword spotting from online Chinese handwritten documents. The similarity between a text word and handwriting is obtained by combining the character similiarity scores given by a character classifier. To overcome the ambiguity of character segmentation, multiple
issued to the databases also contain spatial and textual components, for example, "Find shelters with emergency medical facilities in Orange County," or "Find earthquake-prone zones in Southern California." We refer to such queries as spatial-keyword queries or SK queries for short. In recent times, a lot of interest has
In this paper, we focus on efficient keyword query processing for XML data based on the SLCA and ELCA semantics. We propose a novel form of inverted lists for keywords which include IDs of nodes that directly or indirectly contain a given keyword. We propose a family of efficient algorithms that are based on the set
Keyword search in relational database is a central topic in integrating database and information technologies. We briefly introduce the problem, analyze the challenges, and survey the existing work. We also highlight our research progress of the SPARK project that partially addresses the challenges. A few future
With the growing popularity of XML and emergence of streaming data model, processing streaming XML has become an important topic. This paper proposes keyword search solution over XML fragment streams based on hole-filler model. Two efficient indexes, dual list and sketch are developed to further improve the
Keyword search is a user-friendly way to query XML data, such that users do not need to understand the complex syntax of structured query languages and the complex structural information of the underlying XML data. However, existing semantics suffer from limited expressiveness, thus users cannot obtain desired
Path and Keyword search in XML documents has recently become an active area of research, in which constructing an effective index is a key issue. Existing index structures and encoding schemes for XML query processing target the Parent-Child and Ancestor-Descendant relationships, however, they depend on large index
Empowering users to access databases using simple keywords can relieve users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast-evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword-based
The growth of the Semantic Web has seen a rapid increase in the amount of Semantic Web data. Meanwhile, the demand for access to Semantic Web data without detailed knowledge of RDF query languages is increasing. In this paper, an approach which can return ranked answers to keyword query without the help of data schema
Result integrating is a key component for keyword querying across heterogeneous databases. Once the results from various search engines are collected, the search engine merges them into a single ranked list. In this paper, firstly, we present a novel model of searching, which the database is an undirected graph and
Keyword search over relational databases (KSORD) enables casual users to use keyword queries (a set of keywords) to search relational databases just like searching the Web, without any knowledge of the database schema or any need of writing SQL queries. In KSORD, retrieval of user's initial query is often unsatisfying
Based on a knowledge base, we propose a new method to realize free-style Chinese keyword search over relational databases. Firstly, an index (also called knowledge base) is built by extracting related information of Chinese tuple words in a database, then query words and tuple words are matched quickly each other by
expressed in terms of keywords, over several XML streams. However, there are few algorithms that evaluate this kind of query. One of them is MKStream, which is the current state-of-the-art algorithm for processing keyword-based queries over XML streams. In order to improve scalability, in this paper we introduce PMKStream
Large volumes of geo-tagged text objects are available on the web. Spatial keyword top-k queries retrieve k such objects with the best score according to a ranking function that takes into account a query location and query keywords. In this setting, users may wonder why some known object is unexpectedly missing from
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