基于局部训练嵌入的异构书目网络专家检索
Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings
摘要：
专家发现是工业界和学术界的一项重要任务。对于各种查询，对具有适当专业知识的候选人进行排名具有挑战性。此外，不同类型的对象相互作用，自然形成异构信息网络。本文从文本内容分析和权威性排序两个方面研究了异构书目网络中的专家发现问题。在文本内容分析方面，提出了一种基于概念层次结构的局部嵌入学习的查询扩展方法，该方法特别适合于语义较窄的查询.与全局嵌入学习相比，局部训练的嵌入学习将词项映射到一个受相关主题约束的潜在语义空间中，从而为特定查询保留了更精确和微妙的信息。考虑到候选人的排名，异构的信息网络结构，而在很大程度上被忽略了在以前的研究中的专家发现，提供了额外的信息。具体来说，对象之间不同类型的交互作用起着不同的作用。我们提出了一种排名算法来估计网络中对象的权威性，单独对待每个强类型的边缘类型。为了证明所提出的框架的有效性，我们将所提出的方法应用到一个大规模的书目数据集，超过200万个条目和100万名研究人员候选人。实验结果表明，该框架优于现有的方法，一般和特定的查询。
英文摘要：
Expert finding is an important task in both industry and academia. It is challenging to rank candidates with appropriate expertise for various queries. In addition, different types of objects interact with one another, which naturally forms heterogeneous information networks. We study the task of expert finding in heterogeneous bibliographical networks based on two aspects: textual content analysis and authority ranking. Regarding the textual content analysis, we propose a new method for query expansion via locally-trained embedding learning with concept hierarchy as guidance, which is particularly tailored for specific queries with narrow semantic meanings. Compared with global embedding learning, locally-trained embedding learning projects the terms into a latent semantic space constrained on relevant topics, therefore it preserves more precise and subtle information for specific queries. Considering the candidate ranking, the heterogeneous information network structure, while being largely ignored in the previous studies of expert finding, provides additional information. Specifically, different types of interactions among objects play different roles. We propose a ranking algorithm to estimate the authority of objects in the network, treating each strongly-typed edge type individually. To demonstrate the effectiveness of the proposed framework, we apply the proposed method to a large-scale bibliographical dataset with over two million entries and one million researcher candidates. The experiment results show that the proposed framework outperforms existing methods for both general and specific queries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
