Relationship Queries on Extended Knowledge Graphs

Mohamed Yahya, Denilson Barbosa, K. Berberich, Qiuyue Wang, G. Weikum
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引用次数: 48

Abstract

Entity search over text corpora is not geared for relationship queries where answers are tuples of related entities and where a query often requires joining cues from multiple documents. With large knowledge graphs, structured querying on their relational facts is an alternative, but often suffers from poor recall because of mismatches between user queries and the knowledge graph or because of weakly populated relations. This paper presents the TriniT search engine for querying and ranking on extended knowledge graphs that combine relational facts with textual web contents. Our query language is designed on the paradigm of SPO triple patterns, but is more expressive, supporting textual phrases for each of the SPO arguments. We present a model for automatic query relaxation to compensate for mismatches between the data and a user's query. Query answers -- tuples of entities -- are ranked by a statistical language model. We present experiments with different benchmarks, including complex relationship queries, over a combination of the Yago knowledge graph and the entity-annotated ClueWeb'09 corpus.
扩展知识图上的关系查询
文本语料库上的实体搜索不适用于关系查询,其中答案是相关实体的元组,并且查询通常需要连接来自多个文档的线索。对于大型知识图,对它们的关系事实进行结构化查询是一种选择,但由于用户查询与知识图之间的不匹配或由于弱填充关系,通常会导致召回率较低。本文提出了一种基于triit的扩展知识图搜索引擎,该搜索引擎将关系事实与文本web内容相结合,用于扩展知识图的查询和排序。我们的查询语言是基于SPO三重模式的范例设计的,但它更具表现力,支持每个SPO参数的文本短语。我们提出了一个自动查询松弛模型,以补偿数据与用户查询之间的不匹配。查询答案——实体元组——由统计语言模型排序。我们在Yago知识图和实体注释的ClueWeb'09语料库的组合上进行了不同基准的实验,包括复杂的关系查询。
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