Data Extraction Using NLP Techniques and Its Transformation to Linked Data

Kríž, Vincent; Hladká, Barbora; Nečaský, Martin; Knap, Tomáš

doi:10.1007/978-3-319-13647-9_13

Cited by 7 publications

(9 citation statements)

References 11 publications

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“…The second group of approaches also investigate knowledge extraction and entity mapping [4], [8], [9]. In some studies [4], [8], a triple is extracted from natural language text by using natural language processing (NLP) techniques.…”

Section: Related Workmentioning

confidence: 99%

“…In some studies [4], [8], a triple is extracted from natural language text by using natural language processing (NLP) techniques. Then, a triple is stored as a resource description framework (RDF) triple using its own ontology.…”

Section: Related Workmentioning

confidence: 99%

“…New knowledge is constantly becoming available; however, it is usually in the form of natural language text, which is not straightforward to transfer to a KG. Furthermore, the rate of publication of natural language text is increasing dramatically [4].…”

Section: Introductionmentioning

confidence: 99%

“…text [4]- [10]. Although these approaches perform well for extracting triples from text, they still have limitations regarding the mapping of the elements of a triple, especially the predicate, to its corresponding element in a KG.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Namely, we have developed the RExtractor system, 1 which processes texts by linguistically-aware tools and extracts entities and relations using queries over dependency trees. The language used for testing RExtractor was Czech and the legal domain was chosen to be explored in detail (Kríž et al, 2014;Kríž and Hladká, 2015). We evaluated RExtractor on the Czech Legal Text Treebank (CLTT) enriched with manually annotated entities and their relations (Kríž et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Improving Dependency Parsing Using Sentence Clause Charts

Kríž

Hladká

2016

Proceedings of the ACL 2016 Student Research Workshop

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We propose a method for improving the dependency parsing of complex sentences. This method assumes segmentation of input sentences into clauses and does not require to retrain a parser of one's choice. We represent a sentence clause structure using clause charts that provide a layer of embedding for each clause in the sentence. Then we formulate a parsing strategy as a two-stage process where (i) coordinated and subordinated clauses of the sentence are parsed separately with respect to the sentence clause chart and (ii) their dependency trees become subtrees of the final tree of the sentence. The object language is Czech and the parser used is a maximum spanning tree parser trained on the Prague Dependency Treebank. We have achieved an average 0.97% improvement in the unlabeled attachment score. Although the method has been designed for the dependency parsing of Czech, it is useful for other parsing techniques and languages.

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Leveraging Distributed Representations of Elements in Triples for Predicate Linking

Kertkeidkachorn

Ichise

2017

Lecture Notes in Computer Science

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Data Extraction Using NLP Techniques and Its Transformation to Linked Data

Cited by 7 publications

References 11 publications

An Automatic Knowledge Graph Creation Framework from Natural Language Text

An Automatic Knowledge Graph Creation Framework from Natural Language Text

Improving Dependency Parsing Using Sentence Clause Charts

Leveraging Distributed Representations of Elements in Triples for Predicate Linking

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