Finding relevant information of certain types from enterprise data

Liu, Xitong; Fang, Hui; Yao, Conglei; Wang, Min

doi:10.1145/2063576.2063588

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…AND RETRIEVAL Developing vertical or enterprise search [14] for knowledge workers is a complex task that should accommodate and enhance the existing workflows [15]. We are working towards support by a recommendation system as part of semantic search [8].…”

Section: Challenges For Knowledge Managementmentioning

confidence: 99%

Challenges for information access in multi-disciplinary product design and engineering settings

Ahlers

Mehrpoor

Kristensen

et al. 2015

2015 Tenth International Conference on Digital Information Management (ICDIM)

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In any larger engineering setting, there is a huge number of documents that engineers and others need to use and be aware of in their daily work. To improve the handling of this amount of documents, we propose to view it under the angle of a new domain for professional search, thus incorporating search engine knowledge into the process. We examine the use of Information Retrieval (IR), Recommender Systems (RecSys), and Knowledge Management (KM) methods in the engineering domain of Knowledge-based Engineering (KBE). The KBE goal is to capture and reuse knowledge in product and process engineering with a systematic method. Based on previous work in professional search and enterprise search, we explore a combination of methods and aim to identify key issues in their application to KBE. We list detected challenges, discuss information needs and search tasks, then focus on issues to solve for a successful integration of the IR and KBE domain and give a system overview of our approach to build a search and recommendation tool to improve the daily informationseeking workflow of engineers in knowledge-intense disciplines. Our work contributes to bridging the gap between Information Retrieval and engineering support systems.

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Section: Challenges For Knowledge Managementmentioning

confidence: 99%

Challenges for information access in multi-disciplinary product design and engineering settings

Ahlers

Mehrpoor

Kristensen

et al. 2015

2015 Tenth International Conference on Digital Information Management (ICDIM)

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“…The entity ranking of INEX [8] also focused on retrieving related entities with the emphasis on the type of the target entities (i.e., categories) rather than the relation between the target and input entities. Liu et al [15] studied the problem of finding relevant information with specified types. Unlike previous work, we use unstructured queries to find the related entities, and no entity type is specified in the query and no explicit relation is specified either.…”

Section: Related Workmentioning

confidence: 99%

Entity centric query expansion for enterprise search

Liu

Fang

Chen

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Many information needs of enterprise search center around entities. Intuitively, information related to the entities mentioned in the query, such as related entities, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are termcentric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities to improve search quality. Experiment results show that the proposed entity-centric query expansion strategy is more effective to improve the search performance than the state-of-the-art pseudo feedback methods on longer, natural language-like queries with entities.

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