BIGMAT: A Distributed Affinity-Preserving Random Walk Strategy for Instance Matching on Knowledge Graphs

Assi, Ali; Mcheick, Hamid; Dhifli, Wajdi

doi:10.1109/bigdata47090.2019.9006348

Cited by 7 publications

(12 citation statements)

References 20 publications

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“…BIGMat is an IM approach implemented in Spark and based on an affinity‐preserving random walk technique. It represents the IM problem as a graph‐based node ranking and selection problem in a constructed candidates association graph.…”

Section: Discussionmentioning

confidence: 99%

“…For this purpose, a bipartite graph could first be built from the inferred identity links. Then, a post‐processing, including stable marriage problem (SMP), Hungarian algorithm, and symmetric best match strategy (SBM)) could be adopted to obtain the final set of co‐referent pairs from this bipartite graph as in BIGMat …”

Section: Main Lessons and Open Challengesmentioning

confidence: 99%

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Data linking over RDF knowledge graphs: A survey

Assi

Mcheick

Dhifli³

2020

Concurrency and Computation

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Summary Instance matching (IM) is the process of matching instances across Knowledge Bases (KBs) that refer to the same real‐world object (eg, the same person in two different KBs). Several approaches in the literature were developed to perform this process using different algorithmic techniques and search strategies. In this article, we aim to provide the rationale for IM and to survey the existing algorithms for performing this task. We begin by identifying the importance of such a process and define it formally. We also provide a new classification of these approaches depending on the “source of evidence,” which can be considered as the context information integrated explicitly or implicitly in the IM process. We survey and discuss the state‐of‐the‐art IM methods regarding the context information. We, furthermore, describe and compare different state‐of‐the‐art IM approaches in relation to several criteria. Such a comprehensive comparative study constitutes an asset and a guide for future research in IM.

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Section: Discussionmentioning

confidence: 99%

Section: Main Lessons and Open Challengesmentioning

confidence: 99%

Data linking over RDF knowledge graphs: A survey

Assi

Mcheick

Dhifli³

2020

Concurrency and Computation

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“…MSBlockSlicer [ 58 ] pays attention to the problem of load imbalance and adopts a block slice strategy to balance the load of each worker in the distributed cluster. The BIGMAT framework [ 22 ] applies the affinity-preserving random walk algorithm to express IM as a graph-based node ranking and selection problem in the constructed candidate association graph and selects matching results through a distributed architecture. Our framework leverages the proposed blocking algorithm to divide the matching task into multiple logistic regression tasks that can be executed distributionally.…”

Section: Related Workmentioning

confidence: 99%

“…The distributed computing model, such as MapReduce [ 21 ], allows the instance matching process to be divided into multiple matching tasks that can be executed by multiple workers. Frameworks that adopt this include LINDA [ 14 ], BIGMAT [ 22 ], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Although standalone frameworks [ 18 , 20 , 23 ] can obtain high-quality matching results, they have high requirements for time and computing resources to match large-scale knowledge graphs. Meanwhile, several distributed frameworks [ 14 , 22 ] have been proposed and claimed to be able to process large-scale knowledge graphs efficiently, but their matching quality can be further improved.…”

Section: Introductionmentioning

confidence: 99%

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FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion

Zhu

Wang

Jiang

et al. 2021

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Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects’ features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.

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