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Diverse and proportional size-l object summaries using pairwise relevance
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
College of Computer Science Beijing University of Technology, Beijing, China.
Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong.
2016 (English)In: The VLDB journal, ISSN 1066-8888, E-ISSN 0949-877X, Vol. 25, no 6, 791-816 p.Article in journal (Refereed) Published
Abstract [en]

The abundance and ubiquity of graphs (e.g., online social networks such as Google+" style="position: relative;" tabindex="0" id="MathJax-Element-1-Frame" class="MathJax">+ and Facebook; bibliographic graphs such as DBLP) necessitates the effective and efficient search over them. Given a set of keywords that can identify a data subject (DS), a recently proposed keyword search paradigm produces a set of object summaries (OSs) as results. An OS is a tree structure rooted at the DS node (i.e., a node containing the keywords) with surrounding nodes that summarize all data held on the graph about the DS. OS snippets, denoted as size-l OSs, have also been investigated. A size-l OS is a partial OS containing l nodes such that the summation of their importance scores results in the maximum possible total score. However, the set of nodes that maximize the total importance score may result in an uninformative size-l OSs, as very important nodes may be repeated in it, dominating other representative information. In view of this limitation, in this paper, we investigate the effective and efficient generation of two novel types of OS snippets, i.e., diverse and proportional size-l OSs, denoted as DSize-l and PSize-l OSs. Namely, besides the importance of each node, we also consider its pairwise relevance (similarity) to the other nodes in the OS and the snippet. We conduct an extensive evaluation on two real graphs (DBLP and Google+" style="position: relative;" tabindex="0" id="MathJax-Element-2-Frame" class="MathJax">+). We verify effectiveness by collecting user feedback, e.g., by asking DBLP authors (i.e., the DSs themselves) to evaluate our results. In addition, we verify the efficiency of our algorithms and evaluate the quality of the snippets that they produce.

Place, publisher, year, edition, pages
2016. Vol. 25, no 6, 791-816 p.
Keyword [en]
Keyword, search, Diversity, Proportionality, Snippets, Summaries
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-319053DOI: 10.1007/s00778-016-0433-6OAI: oai:DiVA.org:uu-319053DiVA: diva2:1086007
Available from: 2017-03-30 Created: 2017-03-30 Last updated: 2017-03-31Bibliographically approved

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