An Automatic Instance Expansion Framework for Mapping Instances to Linked Data Resources

Kertkeidkachorn, Natthawut; Ichise, Ryutaro; Suchato, Atiwong; Punyabukkana, Proadpran

doi:10.1007/978-3-319-14122-0_28

Cited by 2 publications

(12 citation statements)

References 9 publications

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“…As a result, an instance matching system, which map instances to a data set, cannot effectively map instances to the LOD cloud. Kertkeidkachorn et al therefore introduce an automatic instance expansion framework for mapping instances to the LOD [13]. The framework discovers and maps instances to the LOD cloud by gradually expanding the data set from one data set to another data set.…”

Section: Related Workmentioning

confidence: 99%

“…Although their work successfully increases coverage of mapping instances to the LOD cloud, the search space during the expansion process from one data set to another data set is still high. In this article, the major contribution differentiating from the work [13] is a heuristic function for the automatic instance expansion framework in order to limit the expanded search space in the LOD cloud to a reasonable range. Moreover,we also give rigid evidences why mapping instances to multiple data sets are necessary for the LOD cloud and also provide further empirical study of similarity metrics in the instance matching component.…”

Section: Related Workmentioning

confidence: 99%

“…Although we could gradually expand an source instance to an expanded instance by using adjacent instances and link properties, the expanded search space could grow up dramatically when expanding into higher depth level [13]. The depth level is measured by how many hops from the instance to the other instance via adjacent instances.…”

Section: Candidate Expandermentioning

confidence: 99%

“…According to this problem, a simple heuristic function is introduced by assuming that the search space should be expanded to an expanded instance if the expanded instances share at least a keyword with instances in traversed path of adjacent instances in the source data set [13]. Based on the preliminary experiment [13] with the maximum depth level set at 3, the expander module generates averagely 214,885 candidate pairs per an instance, while using the simple heuristic function the expander module establishes 203 candidate pairs per an instance. The heuristic function in the work [13] could reduce a number of candidate pairs.…”

Section: Candidate Expandermentioning

confidence: 99%

“…Based on the preliminary experiment [13] with the maximum depth level set at 3, the expander module generates averagely 214,885 candidate pairs per an instance, while using the simple heuristic function the expander module establishes 203 candidate pairs per an instance. The heuristic function in the work [13] could reduce a number of candidate pairs. Still, the work [13] generates enormous useless candidate pairs, which have no chance to be a correct match pair.…”

Section: Candidate Expandermentioning

confidence: 99%

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A Heuristic Expansion Framework for Mapping Instances to Linked Open Data

Kertkeidkachorn

Ichise

2016

IEICE Trans. Inf. & Syst.

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Mapping instances to the Linked Open Data (LOD) cloud plays an important role for enriching information of instances, since the LOD cloud contains abundant amounts of interlinked instances describing the instances. Consequently, many techniques have been introduced for mapping instances to a LOD data set; however, most of them merely focus on tackling with the problem of heterogeneity. Unfortunately, the problem of the large number of LOD data sets has yet to be addressed. Owing to the number of LOD data sets, mapping an instance to a LOD data set is not sufficient because an identical instance might not exist in that data set. In this article, we therefore introduce a heuristic expansion based framework for mapping instances to LOD data sets. The key idea of the framework is to gradually expand the search space from one data set to another data set in order to discover identical instances. In experiments, the framework could successfully map instances to the LOD data sets by increasing the coverage to 90.36%. Experimental results also indicate that the heuristic function in the framework could efficiently limit the expansion space to a reasonable space. Based upon the limited expansion space, the framework could effectively reduce the number of candidate pairs to 9.73% of the baseline without affecting any performances.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Candidate Expandermentioning

confidence: 99%

Section: Candidate Expandermentioning

confidence: 99%

Section: Candidate Expandermentioning

confidence: 99%

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A Heuristic Expansion Framework for Mapping Instances to Linked Open Data

Kertkeidkachorn

Ichise

2016

IEICE Trans. Inf. & Syst.

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An Automatic Knowledge Graph Creation Framework from Natural Language Text

Kertkeidkachorn

Ichise

2018

IEICE Trans. Inf. & Syst.

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SUMMARYKnowledge graphs (KG) play a crucial role in many modern applications. However, constructing a KG from natural language text is challenging due to the complex structure of the text. Recently, many approaches have been proposed to transform natural language text to triples to obtain KGs. Such approaches have not yet provided efficient results for mapping extracted elements of triples, especially the predicate, to their equivalent elements in a KG. Predicate mapping is essential because it can reduce the heterogeneity of the data and increase the searchability over a KG. In this article, we propose T2KG, an automatic KG creation framework for natural language text, to more effectively map natural language text to predicates. In our framework, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its corresponding predicate in a KG. Based on experimental results, the hybrid approach can identify more similar predicate pairs than a baseline method in the predicate mapping task. An experiment on KG creation is also conducted to investigate the performance of the T2KG. The experimental results show that the T2KG also outperforms the baseline in KG creation. Although KG creation is conducted in open domains, in which prior knowledge is not provided, the T2KG still achieves an F1 score of approximately 50% when generating triples in the KG creation task. In addition, an empirical study on knowledge population using various text sources is conducted, and the results indicate the T2KG could be used to obtain knowledge that is not currently available from DBpedia.

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An Automatic Instance Expansion Framework for Mapping Instances to Linked Data Resources

Cited by 2 publications

References 9 publications

A Heuristic Expansion Framework for Mapping Instances to Linked Open Data

A Heuristic Expansion Framework for Mapping Instances to Linked Open Data

An Automatic Knowledge Graph Creation Framework from Natural Language Text

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