Making It Tractable to Catch Duplicates and Conflicts in Graphs

Author:

Fan Wenfei1ORCID,Fu Wenzhi2ORCID,Jin Ruochun3ORCID,Liu Muyang2ORCID,Lu Ping4ORCID,Tian Chao4ORCID

Affiliation:

1. Shenzhen Institute of Computing Sciences; University of Edinburgh; & Beihang University, Shenzhen, China

2. University of Edinburgh, Edinburgh, United Kingdom

3. National University of Defense Technology, Changsha, China

4. Beihang University, Beijing, China

Abstract

This paper proposes an approach for entity resolution (ER) and conflict resolution (CR) in large-scale graphs. It is based on a class of Graph Cleaning Rules (GCRs), which support the primitives of relational data cleaning rules, and may embed machine learning classifiers as predicates. As opposed to previous graph rules, GCRs are defined with a dual graph pattern to accommodate irregular structures of schemaless graphs, and adopt patterns of a star form to reduce the complexity. We show that the satisfiability, implication and validation problems are all in polynomial time (PTIME) for GCRs, as opposed to the intractability of these classical problems for previous graph dependencies. We develop a parallel algorithm to discover GCRs by combining the generations of patterns and predicates, and a parallel PTIME algorithm for "deep" ER and CR by recursively applying the mined GCRs. We show that these algorithms guarantee to reduce runtime when more processors are used. Using real-life and synthetic graphs, we experimentally verify that rule discovery and error detection with GCRs are substantially faster than with previous graph dependencies, with improved accuracy.

Funder

State Key Laboratory of Software Development Environment

Royal Society Wolfson Research Merit Award

National Natural Science Foundation of China

Engineering and Physical Sciences Research Council

EPSRC CDT in Pervasive Parallelism at the University of Edinburgh

Publisher

Association for Computing Machinery (ACM)

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