Document and Corpus Quality Challenges for Knowledge Management in Engineering Enterprises

Ahlers, Dirk; Krogstie, John

doi:10.1145/2818379

Cited by 1 publication

(1 citation statement)

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“…From that side, an important aspect is data quality [29] which needs to be explored for two reasons. First, the document base needs to contain the required knowledge; second, the metadata and the relations of documents needed to infer context need to be available and of a sufficient, to be determined, quality [30], and the used methodologies tuned to the availability of features in the documents.…”

Section: Future Workmentioning

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: Future Workmentioning

confidence: 99%