Semantic pheromone walking: A semantic clue discovering scheme based on concept relatedness in open domain knowledge network
Bo Zhang, Sihui Hu, Ruxiang Peng, Yajun Lü · 2016
Semantic clue discovering has been a significant issue for mining the form of correlation among concepts or things(Knowledge Entities). The correlation is seen as an important reference for improving information retrieval, knowledge reasoning or making decisions. At present, most searches on semantics focused on semantic relatedness computing rather than semantic clues discovering. To discover semantic clues, we propose a novel semantic clues discovering method, named as Semantic Pheromone Walking (SPW) that utilizes the strategies of pheromone in ant colony algorithm and random walk algorithm to discover close semantic clues among knowledge entities. Our proposed SPW comprises two aspects: concept network construction and semantic clue discovering. Firstly, constructing a weighted network model named Concept Network (CN), the link weights of which are determined in accordance with the hyperlink structure and semantic relatedness of text data in ODKN contains abundant semantic information. Then, a concept network based Semantic Pheromone Walking method is addressed to discover semantic clues between knowledge entities by using Semantic Pheromone (SP) which is a digital signal reflecting the compactness of concept correlation, as heuristic information. Finally, the experimental results show that: the human cognitive information contained in the knowledge network can meet the need of the exploration of correlation form between things and our solution could find reasonable semantic clues based it.