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Title:Towards incorporating derived features in dataset alignment and linking
Author(s):Blauvelt, Catherine; Weigl, David M.; Downie, J. Stephen; Page, Kevin R.
Subject(s):Linked data
Entity alignment
Feature extraction
Abstract:The Semantic Alignment and Linking Tool (SALT) enables scholars and domain experts to establish connections between complementary datasets describing entities such as people, works, or performances, by generating alignment candidates based on contextual cues from shared bibliographic metadata. Here, we present a redesigned user interface for SALT to address usability concerns identified during a user evaluation, and extend it to incorporate computational features as additional semantic context. These derived features quantify specific aspects of information resources such as musical recordings and textual documents, mathematically characterizing, e.g., the musical keys represented in an audio signal, or the token, line, and page counts within a text. Such metadata describing aspects of the content of information resources provide valuable additional cues, alongside bibliographic facets, to the expert user undertaking the alignment task.
Issue Date:2017
Publisher:iSchools
Citation Info:Blauvelt, C., Weigl, D. M., Downie, J. S., & Page, K. R. (2017). Towards Incorporating Derived Features in Dataset Alignment and Linking. In iConference 2017 Proceedings (pp. 809-814). https://doi.org/10.9776/17346
Series/Report:iConference 2017 Proceedings
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/96727
DOI:https://doi.org/10.9776/17346
Rights Information:Copyright 2017 Catherine Blauvelt, David M. Weigl, J. Stephen Downie, and Kevin R. Page
Date Available in IDEALS:2017-07-27


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