NDLI: Similarity Search of Human Behavior Processes Using Extended Linked Data Semantic Distance

Content Provider	IEEE Xplore Digital Library
Author	Zhang Zuo Hung-Hsuan Huang Kawagoe, K.
Copyright Year	2014
Description	Author affiliation: Grad. Sch. of Inf. Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan (Zhang Zuo; Hung-Hsuan Huang; Kawagoe, K.)
Abstract	Large amounts of sensed data on human behaviors have been collected, and technologies for modeling human behavior have recently been developed. However, the data collected are difficult to analyze and reuse. Thus, in order to mine various aspects of human behaviors, development of an appropriate semantic similarity measure of human behaviors is essential. In this paper, we propose a novel similarity search method that, given a particular behavior, searches for a similar behavior. The proposed method extends the semantic distance calculation method Linked Data Semantic Distance (LDSD) and applies it to human behavior distance calculations. Semantic distance measures for similarity search of human behaviors has, to the best of our knowledge, never been studied before, whereas many studies on similarity measures for linked data, graphs, and processes have been proposed. Our extended LDSD extends distance calculation from three standpoints: temporal, granularity, and content. We present the details of these extensions along with examples, and compare our extended LDSD to the original LDSD and another existing method.
Starting Page	178
Ending Page	182
File Size	495184
Page Count	5
File Format	PDF
ISBN	9781479957217
ISSN	15294188
e-ISBN	9781479957224
DOI	10.1109/DEXA.2014.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-01
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics Niobium Abstracts Artificial neural networks Databases Surgery
Content Type	Text
Resource Type	Article

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