用紧凑、有代表性、相关的知识图谱丰富文档
Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
摘要：
知识图谱（KG）的一个突出应用是文档丰富。现有的方法识别背景KG中的实体的提及，并利用实体类型和直接关系来丰富文档。我们计算一个实体关系子图（ERG），可以更有表现力地表示一组提到的实体之间的间接关系。为了找到紧凑的，有代表性的，和相关的ERG有效的丰富，我们提出了一个有效的最佳优先搜索算法来解决一个新的组合优化问题，实现了代表性和紧凑性之间的权衡，然后我们利用本体知识排名ERG基于实体的文档KG和KG内的相关性。广泛的实验和用户研究表明，我们的方法有前途的性能。
英文摘要：
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.
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。
