An unsupervised instance matcher for schema-free RDF data

Kejriwal, Mayank; Miranker, Daniel P.

doi:10.1016/j.websem.2015.07.002

Cited by 30 publications

(7 citation statements)

References 48 publications

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“…Moreover, rather than training models to match instances, MDedup [ 40 ] trains models for discovering the matching dependencies (MDs) to select matched instances, where MD is one of the relaxed forms [ 41 , 42 ] of functional dependency [ 43 ] in data mining. Semi-supervised learning methods [ 44 , 45 ], unsupervised learning methods [ 46 , 47 ] and self-supervised learning model [ 48 ] are also introduced into the field of instance matching. Besides, works on representation learning for matching instances are gradually emerging [ 49 , 50 , 51 ].…”

Section: Related Workmentioning

confidence: 99%

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

Zhu

Wang

Jiang

et al. 2021

Entropy

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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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Section: Related Workmentioning

confidence: 99%

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

Zhu

Wang

Jiang

et al. 2021

Entropy

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“…Since the advent of large knowledge graphs (KGs) on the Web [13,21,22], the problem of ER has taken on new urgency [23][24][25][26]. Recently, similarity techniques have become quite advanced, especially due to the rise of language representation models, such as BERT, GPT-3, and T5 [27][28][29], as well as so-called knowledge graph embeddings [30][31][32].…”

Section: Elmagarmid Et Al Comprehensively Surveyed Ermentioning

confidence: 99%

“…Unfortunately, this method suffers from high complexity, and would likely benefit from the blocking methods described in this paper. In our own prior work, we have separately presented blocking methods for RDF graphs and for tables [7,26], but this is the first work to attempt to combine both types of inputs in a unified framework and demonstrate viable empirical performance on a range of datasets. A more theoretical treatment on graph-theoretic blocking schemes can be found in [42].…”

Section: Elmagarmid Et Al Comprehensively Surveyed Ermentioning

confidence: 99%

Unsupervised DNF Blocking for Efficient Linking of Knowledge Graphs and Tables

Kejriwal

2021

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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.

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“…Research on object similarity and class matching has issued a considerable amount of techniques and systems in the field of Ontology Matching [10], although less work has been devised for property alignment [3,19]. The work in [18] presents an unsupervised learning process for instance matching between entities. Queries considered in this paper involve terms for classes, properties and individuals.…”

Section: Related Workmentioning

confidence: 99%

Estimating query rewriting quality over LOD

Torre-Bastida

Bermúdez

Illarramendi

2019

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Nowadays it is becoming increasingly necessary to query data stored in different datasets of public access, such as those included in the Linked Data environment, in order to get as much information as possible on distinct topics. However, users have difficulty to query those datasets with different vocabularies and data structures. For this reason it is interesting to develop systems that can produce on demand rewritings of queries. Moreover, a semantics preserving rewriting cannot often be guaranteed by those systems due to heterogeneity of the vocabularies. It is at this point where the quality estimation of the produced rewriting becomes crucial. In this paper we present a novel framework that, given a query written in the vocabulary the user is more familiar with, the system rewrites the query in terms of the vocabulary of a target dataset. Moreover, it also informs about the quality of the rewritten query with two scores: firstly, a similarity factor which is based on the rewriting process itself, and secondly, a quality score offered by a predictive model. This model is constructed by a machine learning algorithm that learns from a set of queries and their intended (gold standard) rewritings. The feasibility of the framework has been validated in a real scenario.

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An unsupervised instance matcher for schema-free RDF data

Cited by 30 publications

References 48 publications

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

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

Unsupervised DNF Blocking for Efficient Linking of Knowledge Graphs and Tables

Estimating query rewriting quality over LOD

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