Community-Based Question Answering via Heterogeneous Social Network Learning

By Hanyin Fang, Fei Wu, Zhou Zhao, Xinyu Duan, Yueting Zhuang, Martin Ester. 2016

80 citations recorded by the source corpus.

Community-based question answering (cQA) sites have accumulated vast amount of questions and corresponding crowdsourced answers over time. How to efficiently share the underlying information and knowledge from reliable (usually highly-reputable) answerers has become an increasingly popular research topic. A major challenge in cQA tasks is the accurate matching of high-quality answers w.r.t given questions. Many of traditional approaches likely recommend corresponding answers merely depending on the content similarity between questions and answers, therefore suffer from the sparsity bottleneck of cQA data. In this paper, we propose a novel framework which encodes not only the contents of question-answer(Q-A) but also the social interaction cues in the community to boost the cQA tasks. More specifically, our framework collaboratively utilizes the rich interaction among questions, answers and answerers to learn the relative quality rank of different answers w.r.t a same question. Moreover, the information in heterogeneous social networks is comprehensively employed to enhance the quality of question-answering (QA) matching by our deep random walk learning framework. Extensive experiments on a large-scale dataset from a real world cQA site show that leveraging the heterogeneous social information indeed achieves better performance than other state-of-the-art cQA methods.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

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Citations in the corpus

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “For example, [29] and [30] embed the sentences from questions/answers in cQA sites, and [44] embeds a sequence of nodes between two nodes for proximity embedding.”
Learning Community Embedding with Community Detection and Node Embedding on Graphs (2017, background): “Such node embedding has been shown very successful in preserving the network structure, and significantly improving a wide range of applications, including node classification [5, 20], node clustering [27, 34], link prediction [12, 19], graph visualization [24, 29] and more [10, 18].”
Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding (2017, methodology): “In this paper, we study an important task, semantic proximity search on the graph (Sun et al. 2011; Fang et al. 2016b).”
Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning (2017, methodology): “Currently, the problem of community-based question answering has attracted considerable attention (Ji et al. 2012; Yih et al. 2014; Qiu and Huang 2015; Shen et al. 2015; Wang and Nyberg 2015; Dong et al. 2015; Fang et al. 2016).”