Context-aware semantic classification of search queries for browsing community question–answering archives

Figueroa, Alejandro; Neumann, Günter

doi:10.1016/j.knosys.2016.01.008

Cited by 36 publications

(23 citation statements)

References 3 publications

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“…The WWW has become a universal repository of human knowledge and culture. Given that millions of Internet users have created hundreds of billions of documents that compose the largest repository of human knowledge in history, finding information on the web has become quite challenging and often requires submitting queries to a search engine [7]. SEs are one of the most popular tools on the Web and are designed to help users find useful information that could contain text, pictures, or videos [8].…”

Section: Research Backgroundmentioning

confidence: 99%

Evaluating Search Results in Exploratory Search

Mahdi

Ahmad²,

Ismail³

2018

IJET

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The volume of information available on the World Wide Web is quite significant. This plethora of information has, to a large extent constantly challenged researchers in seeking for ways of making the information easily accessible to end users in as convenient ways as possible. Characteristically, ensuring the accessibility of this large amount of information involves three all-encompassing processes of retrieval, organization and presentation. The World Wide Web offers a platform for sharing information from large database repositories globally. However, information needs to be searched with specialized tools commonly referred to as search engines. While a number of search engines does presently exist, most of these search engines are noted for their inability to retrieve information usable to the end user. It is therefore critical for the results generated from search engines to be intelligently organized for the optimal usefulness of the information to the searcher. Exploratory web technologies is capable of filing this gap. Therefore, this paper reviews exploratory search as a mechanism for conducting result-oriented search. It also reviews the ways of evaluating the search results obtained from an exploratory search.

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Section: Research Backgroundmentioning

confidence: 99%

Evaluating Search Results in Exploratory Search

Mahdi

Ahmad²,

Ismail³

2018

IJET

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“…A survey of various techniques for detecting query intents using click-through and implicit relevance feedback data is presented in [84]. Accessing digital libraries based on topical context [106,107,108] Implicit feedback from user interaction with software tools [67,66,109,110] Query expansion and refinement with explicit user intervention [62,63] Query generation, augmentation and/or refinement from context without user intervention [24,27,36,37,49,111,112] Context sensitive query autocompletion [59,60,61] Query generation and rank-biasing based on context [49,50,113] Query understanding or disambiguation based on context [58,64,114] Implicit feedback from cursor movement, vertical scrolling, interactions in the areas of interest and/or eyetracking [71,72,73] Touch interaction data on mobile devices [74,75,76]…”

Section: Contextual Interaction Patterns As Indicators Of Plans Actimentioning

confidence: 99%

“…A novel strategy for query understanding in Community question answering platforms based on context (Context-aware cQA) is proposed in [114]. The proposed methods profit from search sessions for semantically categorizing question-like informational search queries and is implemented to help browse community question answering platforms.…”

Section: An Overview Of Context-based Search Systemsmentioning

confidence: 99%

Searching in the Context of a Task: A Review of Methods and Tools

Maguitman¹

2018

CLEIej

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Contextual information extracted from the user task can help to better target retrieval to task-relevant content. In particular, topical context can be exploited to identify the subject of the information needs, contributing to reduce the information overload problem. A great number of methods exist to extract raw context data and contextual interaction patterns from the user task and to model this information using higher-level representations. Context can then be used as a source for automatic query generation, or as a means to refine or disambiguate user-generated queries. It can also be used to filter and rank results as well as to select domain-specific search engines with better capabilities to satisfy specific information requests. This article reviews methods that have been applied to deal with the problem of reflecting the current and long-term interests of a user in the search process. It discusses major difficulties encountered in the research area of context-based information retrieval and presents an overview of tools proposed since the mid-nineties to deal with the problem of context-based search.

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“…(DeVochtetal.,2017)presentsasemanticsearchenginefocusinginparticularonintegration ofdifferentsourcesofdatainthescienceresearchfield.Toincreasetheprecisionoftheresults,the authorsannotatedandinterlinkedstructuredresearchdatawithontologiesfromvariousrepositories exploitingasemanticmodel.Thatapproachdoesnotconsideruser-awarenessandrequiresannotation. (Figueroa & Neumann, 2016) focuses on the search in the context of Community question answering(cQA)platforms.Itinducesthesemanticclassesofquestion-likesearchqueriesbymeans ofthecontextualinformation.Contextissetuporrepresentedbyinferredviewsoftheirrespective searchsessions,namelyviewsmodellingpreviousqueriesenteredbythesameuser.Theideaof introducingsemanticclassesandofcombiningthemwithcontextualinformationisveryinteresting, butinthatworkislimitedtoaspecificfield(cQA).…”

Section: Semantic Approachesmentioning

confidence: 99%

A User-Aware and Semantic Approach for Enterprise Search

Cabri

2018

International Journal on Semantic Web and Information Systems

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This article describes how in addition to general purposes search engines, specialized search engines have appeared and have gained their part of the market. An enterprise search engine enables the search inside the enterprise information, mainly web pages but also other kinds of documents; the search is performed by people inside the enterprise or by customers. This article proposes an enterprise search engine called AMBIT1-SE that relies on two enhancements: first, it is user-aware in the sense that it takes into consideration the profile of the users that perform the query; second, it exploits semantic techniques to consider not only exact matches but also synonyms and related terms. It performs two main activities: (1) information processing to analyse the documents and build the user profile and (2) search and retrieval to search for information that matches user's query and profile. An experimental evaluation of the proposed approach is performed on different real websites, showing its benefits over other well-established approaches.

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Context-aware semantic classification of search queries for browsing community question–answering archives

Cited by 36 publications

References 3 publications

Evaluating Search Results in Exploratory Search

Evaluating Search Results in Exploratory Search

Searching in the Context of a Task: A Review of Methods and Tools

A User-Aware and Semantic Approach for Enterprise Search

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