论文标题
用紧凑，代表性的相关知识图丰富文档
Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
论文作者
论文摘要
知识图（KG）的突出应用是文档丰富。现有方法确定了背景KG中实体的提及，并以实体类型和直接关系丰富文档。我们计算一个实体关系子图（ERG），该子图（ERG）可以在一组提到的实体之间更有表现地表示间接关系。为了找到有效富集的紧凑，代表性和相关的ERG，我们提出了一种有效的最佳搜索算法来解决一个新的组合优化问题，从而实现代表性和紧凑性之间的权衡，然后我们利用本体学知识将基于基于实体的文档kg和Intera-kg和Inta-kg的ERG对ERG进行排名。广泛的实验和用户研究表明我们的方法表现出色。
A prominent application of knowledge graph (KG) is document enrichment. Existing methods identify mentions of entities in a background KG and enrich documents with entity types and direct relations. We compute an entity relation subgraph (ERG) that can more expressively represent indirect relations among a set of mentioned entities. To find compact, representative, and relevant ERGs for effective enrichment, we propose an efficient best-first search algorithm to solve a new combinatorial optimization problem that achieves a trade-off between representativeness and compactness, and then we exploit ontological knowledge to rank ERGs by entity-based document-KG and intra-KG relevance. Extensive experiments and user studies show the promising performance of our approach.
