In the Internet that produces massive data, knowledge is mutually related.A rough domain-specific knowledge graph can display the structured information of the knowledge in this domain, but usually fails to present the potential relationships between entities.To implement smooth extension of relationships between entitites in domain-specific knowledge graphs, a relationship discovery method based on inter-entity association rule analysis and topic analysis is proposed.By utilizing the entity-related data in a specific domain, the potential relationships between domain-specific entities are obtained by analyzing the association rules and the similarity of topic distribution among entity-related data sets.Then the newly discovered relationships are merged into the roughly constructed knowledge graphs to realize the potential relationship extension of the domain-specific knowledge graphs.The experimental results show that the proposed method can discover the commonalities between entities of different sectors, and thus mine the potential relationships between these entities.It improves the efficiency of relationship discovery, and smoothes the extension of domain-specific knowledge graphs.

为解决在互联网文本信息爆炸性增长的前提下,在大规模文本数据中如何发现隐含的、 有价值的潜在知识的问题,提出基于多层次文本聚类的文本知识挖掘方法,针对不同规模的文本数据进行不同粒度的聚类,实现不同层次知识的挖掘.针对最广义层次的文本知识挖掘可实现各主题事务划分,针对子级分类数据的文本知识挖掘可发现下一层次主题分类,针对自定义层次的文本知识挖掘可发现该事件中存在的具体细节.对诉求实际数据的分析结果表明,该方法可在所有诉求数据中挖掘出各种诉求主题,精确挖掘出其中的细节问题,为管理者提供数据和决策支持,提高服务效率.