PARNT: A Statistic based Approach to Extract Non-Taxonomic Relationships of Ontologies from Text

Serra, Ivo; Girardi, Rosario; Nováis, Paulo

doi:10.1109/itng.2013.70

Cited by 16 publications

(6 citation statements)

References 18 publications

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“…Thus, to learn nontaxonomic relationships, for each sentence that contain the domain keyword, relationships represented by tuples in the form (np1, vp, np2) are extracted and evaluated for every verb phrase (vp) with the first noun phrase to its left (np1) and the second noun phrase to its right (np2). In a more recent work, (Serra et al, 2013) use on their work Natural Language Processing (NLP) and statistics to extract non-taxonomic relationships of domain ontology. The system provides three types of extraction rules: the Sentence Rule (SR), the Sentence Rule with Verb Phrase (SRVP) and the Apostrophe Rule (AR).…”

Section: Related Workmentioning

confidence: 99%

Learning Non-taxonomic Relationships of Financial Ontology

Esserhrouchni

Frikh

Ouhbi

2015

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Finance ontology is, in most cases, manually addressed. This results in a tedious development process and error prone that delay their applicability. This is why there is a need of domain ontology learning methods that built the ontology automatically and without human intervention. However, in this learning process, the discovery of non-taxonomic relationships has been recognized as one of the most difficult problems. In this paper, we propose a new methodology for learning non-taxonomic relationships and building financial ontology from scratch. Our new technique is based on using and adapting Open Information Extraction algorithms to extract and label domain relations between concepts. To evaluate our new method effectiveness, we compare the extracted non-taxonomic relations of our algorithm with related works in the same finance corpus. The results showed that our system is more accurate and more effective.

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Section: Related Workmentioning

confidence: 99%

Learning Non-taxonomic Relationships of Financial Ontology

Esserhrouchni

Frikh

Ouhbi

2015

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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“…This experiment uses RMEM to comparatively evaluate the effectiveness of TLN (Serra, Girardi and Novais 2013) and LEAR (Villaverde, Persson, Godoy and Amandi 2009) on the extraction of relationships in the situation where we want to maximize a measure via adjusting their pruning parameters. The match between a relationship recommended by a technique and a reference one was considered as already described in section 6.1.…”

Section: Using Rmem To Evaluate Tln and Lear With The Corpus Geniamentioning

confidence: 99%

“…illustrate the application of RARP and RMEM, two LNTRO techniques presented in the next sections are used: Technique for Learning Non-taxonomic Relationships (TLN)(Serra, Girardi and Novais 2013) and Learning relationships based on the Extraction of Association Rules (LEAR)(Villaverde, Persson, Godoy and Amandi 2009). These techniques were chosen for the case studies (section 6) because, considering the same set of ontology concepts for both learned and reference relationships (type <c 1 , vp, c 2 >), they allow exact match between these two and therefore permit the use of the evaluation measures recall, precision and F-measure.…”

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confidence: 99%

Evaluating techniques for learning non-taxonomic relationships of ontologies from text

Serra

Girardi

Nováis

2014

Expert Systems with Applications

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Learning Non-Taxonomic Relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. Several techniques have been proposed based on Natural Language Processing and Machine Learning. However just like for other techniques for Ontology Learning, evaluating techniques for Learning Non-TaxonomicRelationships is an open problem. Three general proposals suggest that the learned ontologies can be evaluated in an executable application or by domain experts or even by a comparison with a predefined reference ontology. This article proposes two procedures to evaluate techniques for Learning Non-Taxonomic Relationships based on the comparison of the relationships obtained with those of a reference ontology. Also, these procedures are used in the evaluation of two state of the art techniques performing the extraction of relationships from two corpora in the domains of biology and Family Law.

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“…Also, 7 utilized the distributions of co occurring concepts and verbs as significant measures to identify verbs as sematic label. Serra et al, 8 proposed PARNT, which is a novel approach that supports ontology engineers in extracting semantic relations from corpora.…”

Section: Introductionmentioning

confidence: 99%

Towards relation extraction from Arabic text: a review

Al-Arfaj¹

2019

IRATJ

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Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. The Arabic language has complex morphological, grammatical, and semantic aspects since it is a highly inflectional and derivational language, which makes task even more challenging. In this paper, we present a review of the state of the art for relation extraction from texts, addressing the progress and difficulties in this field. We discuss several aspects related to this task, considering the taxonomic and non-taxonomic relation extraction methods. Majority of relation extraction approaches implement a combination of statistical and linguistic techniques to extract semantic relations from text. We also give special attention to the state of the work on relation extraction from Arabic texts, which need further progress.

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PARNT: A Statistic based Approach to Extract Non-Taxonomic Relationships of Ontologies from Text

Cited by 16 publications

References 18 publications

Learning Non-taxonomic Relationships of Financial Ontology

Learning Non-taxonomic Relationships of Financial Ontology

Evaluating techniques for learning non-taxonomic relationships of ontologies from text

Towards relation extraction from Arabic text: a review

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