Flexible Expert Finding on the Web via Semantic Hypergraph Learning and Affinity Propagation Model
Yan T. Yang, Tingwen Liu, Jinqiao Shi, Qiuyan Wang, Li Guo · 2017
Expert finding (EF) task has received widespread attention as an important task of information retrieval.One key category of EF is expert finding on the web, which seeks to rank influential public figures from diverse webpage sources with respect to given query.Previous web expert finding approach relies on casting webpages to hypergraph structure and run heat diffusion process to find the top ranking person vertices according to their heat of popularity.Such approach suffer from two major drawbacks:First, previous web expert finding approach (CoDiffusion) suffers from unflexibility of selecting queries.This means that all corresponding queries must be stored as vertices in hypergraph index beforehand, otherwise CoDiffusion cannot run the expert finding process.Such defect make it ungeneric and incapable of handling the newly invented technical terms or phrases in real world scenarios.Second, the performance of previous approach is less satisfying.We incorporate semantic relatedness information with Hypergraph Learning Framework and Affinity Propagation ({HLFAP}) to handle the above drawbacks.In order to overcome the first disadvantage, we distribute initial heat to the related vertices according to their semantic similarity on given query. In order to solve the second disadvantage, we propose semantic labeled hypergraph learning framework and person influence affinity propagation model to make high quality candidates can receive more heat transition. Experimental results shows that our generic methodology achieves more satisfying results than the non-semantics state-of-the-art baseline method.