文章基本信息

标题：Unsupervised DNF Blocking for Efficient Linking of Knowledge Graphs and Tables
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作者：Mayank Kejriwal
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：3
页码：134
DOI：10.3390/info12030134
出版社：MDPI Publishing
摘要：Entity Resolution (ER) is the problem of identifying co-referent entity pairs across datasets, including knowledge graphs (KGs). ER is an important prerequisite in many applied KG search and analytics pipelines, with a typical workflow comprising two steps. In the first ’blocking’ step, entities are mapped to blocks. Blocking is necessary for preempting comparing all possible pairs of entities, as (in the second ‘similarity’ step) only entities within blocks are paired and compared, allowing for significant computational savings with a minimal loss of performance. Unfortunately, learning a blocking scheme in an unsupervised fashion is a non-trivial problem, and it has not been properly explored for heterogeneous, semi-structured datasets, such as are prevalent in industrial and Web applications. This article presents an unsupervised algorithmic pipeline for learning Disjunctive Normal Form (DNF) blocking schemes on KGs, as well as structurally heterogeneous tables that may not share a common schema. We evaluate the approach on six real-world dataset pairs, and show that it is competitive with supervised and semi-supervised baselines.
关键词：entity resolution; knowledge graphs; blocking; DNF blocking; heterogeneous linking; table-graph linking entity resolution ; knowledge graphs ; blocking ; DNF blocking ; heterogeneous linking ; table-graph linking