Abstract
Keyword search has been widely adopted to explore graph data. Due to the intrinsic ambiguity of terms, it is desirable to develop query expansion techniques to find useful and diversified information progressively in large graphs. To support exploration with keywords, we study the problem of diversified keyword expansion in graphs. Given a set of validated content nodes in a graph, it is to find a set of terms that maximizes the aggregated relevance of the validated nodes. Moreover, the terms should be diversified to cover different search interests. We develop a fast stream-based (\(\frac{1}{2}\)-\(\epsilon \))-approximation to suggest diversified terms, which guarantees a linear scan of the terms in the content nodes up to a bounded area with small update cost. Using real-world graphs, we experimentally verify the effectiveness and efficiency of our algorithms, and their applications in knowledge base exploration.
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Acknowledgment
Namaki and Wu are supported in part by NSF IIS-1633629 and Huawei Innovation Research Program (HIRP).
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Namaki, M.H., Wu, Y., Zhang, X. (2018). Diversified Keyword Expansion on Multi-labeled Graphs. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_19
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DOI: https://doi.org/10.1007/978-3-319-96890-2_19
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