NDLI: Active Learning for Multi-relational Data Construction

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Active Learning for Multi-relational Data Construction

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Active Learning for Multi-relational Data Construction

Content Provider	ACM Digital Library
Author	Kajino, Hiroshi Kishimoto, Akihiro Botea, Adi Kotoulas, Spyros Daly, Elizabeth
Abstract	Knowledge on the Web relies heavily on multi-relational representations, such as RDF and Schema.org. Automatically extracting knowledge from documents and linking existing databases are common approaches to construct multi-relational data. Complementary to such approaches, there is still a strong demand for manually encoding human expert knowledge. For example, human annotation is necessary for constructing a common-sense knowledge base, which stores facts implicitly shared in a community, because such knowledge rarely appears in documents. As human annotation is both tedious and costly, an important research challenge is how to best use limited human resources, whiles maximizing the quality of the resulting dataset. In this paper, we formalize the problem of dataset construction as active learning problems and present the Active Multi-relational Data Construction (AMDC) method. AMDC repeatedly interleaves multi-relational learning and expert input acquisition, allowing us to acquire helpful labels for data construction. Experiments on real datasets demonstrate that our solution increases the number of positive triples by a factor of 2.28 to 17.0, and that the predictive performance of the multi-relational model in AMDC achieves the highest or comparable to the best performance throughout the data construction process.
Starting Page	560
Ending Page	569
Page Count	10
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741103
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Active learning Multi-relational data Human annotation
Content Type	Text
Resource Type	Article

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