Searching a file system using inferred semantic links

Bhagwat, Deepavali; Polyzotis, Neoklis

doi:10.1145/1083356.1083372

Cited by 21 publications

(15 citation statements)

References 7 publications

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“…In order to search and access such files, prototypical search engines have been developed for different use cases. Eureka (Bhagwat & Polyzotis, 2005) is a file system search engine that infers the relations among files for improving the ranking of search results. For inferring semantic links between files, it defines three types of semantic links, content overlap, name overlap, and name reference link.…”

Section: Information Retrieval and Search Engines In File Systemsmentioning

confidence: 99%

Investigating contextual ontologies and document corpus characteristics for information access in engineering settings

Mehrpoor

Ahlers

Gulla

et al. 2017

Journal of Information Technology Case and Application Research

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Knowledge and information resources play a pivotal role in enterprises and are valuable for solution reuse and learning through information access. However, identifying relevant information from a rapidly growing number of unstructured resources is challenging for users. We discuss a personalized information access tool for professional workplaces based on the recommender systems to provide relevant documents for users in specific work contexts based on domain-specific ontologies. Our use case is a multidisciplinary engineering project building an energy-efficient vehicle. We provide an indepth analysis of document corpus characteristics of this real-life shared engineering workspace to understand the content and context of documents using information retrieval methods and semantic annotations. Upon this, we build a contextual ontology as our knowledge domain for the recommender system. We validate our ontology-based content matching approach by evaluating the level of retrievability and coverage of the ontology against the indexed document corpus through experiments on the corpus and ontology. Our results provide insight into engineers' document workspaces and show that even a simple domain ontology is able to match a majority of documents from a domain-oriented corpus. The findings support our approach of using ontology-based recommendation for domain-specific workspaces.

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Section: Information Retrieval and Search Engines In File Systemsmentioning

confidence: 99%

Investigating contextual ontologies and document corpus characteristics for information access in engineering settings

Mehrpoor

Ahlers

Gulla

et al. 2017

Journal of Information Technology Case and Application Research

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“…Most of the search engines [20][21] is used to search the keywords. Web pages are searched by search engine to find required information.…”

Section: "An Ontology Is An Manifestmentioning

confidence: 99%

Information Retrieval From Google Map Using Semantic Web Technology

Nahar¹,

Afrose²,

Sharmin³

2016

IJRET

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“…The SWQ architecture contains three main parts: SWQ search engine and its subcomponents: "query parser" and "context ontology determination engine"; context ontologies for domains of application; a semantic search filter which is to improve search precision based on retrieving term properties in context ontologies. Bhagwat and Polyzotis [3] propose a semantic-based file system search engine -Eureka, which uses an inference model to build the links between files and a FileRank metric to rank the files according to their semantic importance. Kandogan et al [17] develop a semantic search engine Avatar, which combines the traditional text search engine with use of ontology annotations [17].…”

Section: Related Workmentioning

confidence: 99%

Search Engines Going beyond Keyword Search: A Survey

Rahman¹

2013

IJCA

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In order to solve the problem of information overkill on the web or large domains, current information retrieval tools especially search engines need to be improved. Much more intelligence should be embedded to search tools to manage the search and filtering processes effectively and present relevant information. As the web swells with more and more data, the predominant way of sifting through all of that data -keyword search -will one day break down in its ability to deliver the exact information people want at our fingertips. Hence search engines are trying to break the shackles of the concept of keyword search what typically most search engines do. This paper tries to identify the major challenges for today's keyword search engines to adapt with the fast growth of web and support comprehensive user demands in quick time. Then it surveys different non-keyword based paradigms proposed, developed or implemented by researchers and different search engines and also classifies those approaches according to the features focused by the different search engines to deliver results. General Terms:Search Engine, Information Retrieval Keywords:keyword based search, semantic web search, search engine, computational knowledge engine, question-answering system

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Searching a file system using inferred semantic links

Cited by 21 publications

References 7 publications

Investigating contextual ontologies and document corpus characteristics for information access in engineering settings

Investigating contextual ontologies and document corpus characteristics for information access in engineering settings

Information Retrieval From Google Map Using Semantic Web Technology

Search Engines Going beyond Keyword Search: A Survey

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