Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts

Ghosh, Mirna El; Naja, Hala; Abdulrab, Habib; Khalil, Mohamad

doi:10.5220/0006188004730480

Cited by 21 publications

(4 citation statements)

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“…With the spread of the application of ontologies, previous studies have built ontologies in various domains. In addition, previous research has promoted automatic [21] or semi-automatic [22,23] construction methods to enhance the efficiency of ontology construction. One study [23] extracted health records from a database and encoded them into ontological structured data for constructing a health ontology system, which shares the information within the healthcare community.…”

Section: The Ontology Construction Methodsmentioning

confidence: 99%

Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction

Hwang

Hsueh

Chang

2018

ASI

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Term extraction is an important task that automatically extracts relative terms from the texts in a given domain. A significant number of web applications need to model information for specific topics. In particular, we have explored a Taiwan government website that maintains the Laws & Regulations Database of the Republic of China (R.O.C) to provide the current Chinese law text to the public. However, the main issue is that there is no efficient structured method to handle such a large number of law texts. Therefore, in this paper, we propose a novel approach to extract legal as well as domain-relative terms, and then build a law ontology. We used the current Chinese law text from the Laws & Regulations Database as the data source. We then utilized natural language processing tools and data mining techniques to extract legal keywords and their definitions automatically. Subsequently, we constructed a Taiwan law ontology with the legal keywords and relative definitions. We have extracted 1114 legal keywords with definitions. With the characteristics of an ontology, users can view one keyword with its information and the associated keywords. Furthermore, we provide a service, which includes both the graphical and text interfaces to users on the web, such that a user can readily access the legal information on the Internet.

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Section: The Ontology Construction Methodsmentioning

confidence: 99%

Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction

Hwang

Hsueh

Chang

2018

ASI

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“…Previous works have constructed ontologies in various domain. El Ghosh et al [11] presented a semiautomatic ontology construction technique. The approach combines the top-down and bottom-up strategy of ontology construction.…”

Section: Related Workmentioning

confidence: 99%

A Semi-automated Ontology Construction for Legal Question Answering

et al. 2019

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The internet and the development of the semantic web have created the opportunity to provide structured legal data on the web. However, most legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language question. One approach is to develop systems of legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable legal rules. The purpose of the ontology is to provide reasoning support to a legal question answering tool that determines entailment between a pair of texts, one known as the background information (Bg) and the other question statement (Q), so whether Bg entails Q based on the application of the legal rules. The key contribution of this paper is the methodology and the semi-automated legal ontology generation tool, a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

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“…1. Solving the biodata deluge using text mining (TM) [9] NLP-based data-channeling process and their potential applications are many [12,13] as illustrated in (Fig. 2).…”

Section: Introductionmentioning

confidence: 99%

Biomolecular Event Extraction using Natural Language Processing

Bali

Anandaraj

2023

Int. j. electr. comput. eng. syst. (Online)

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Biomedical research and discoveries are communicated through scholarly publications and this literature is voluminous, rich in scientific text and growing exponentially by the day. Biomedical journals publish nearly three thousand research articles daily, making literature search a challenging proposition for researchers. Biomolecular events involve genes, proteins, metabolites, and enzymes that provide invaluable insights into biological processes and explain the physiological functional mechanisms. Text mining (TM) or extraction of such events automatically from big data is the only quick and viable solution to gather any useful information. Such events extracted from biological literature have a broad range of applications like database curation, ontology construction, semantic web search and interactive systems. However, automatic extraction has its challenges on account of ambiguity and the diverse nature of natural language and associated linguistic occurrences like speculations, negations etc., which commonly exist in biomedical texts and lead to erroneous elucidation. In the last decade, many strategies have been proposed in this field, using different paradigms like Biomedical natural language processing (BioNLP), machine learning and deep learning. Also, new parallel computing architectures like graphical processing units (GPU) have emerged as possible candidates to accelerate the event extraction pipeline. This paper reviews and provides a summarization of the key approaches in complex biomolecular big data event extraction tasks and recommends a balanced architecture in terms of accuracy, speed, computational cost, and memory usage towards developing a robust GPU-accelerated BioNLP system.

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Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts

Cited by 21 publications

References 0 publications

Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction

Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction

A Semi-automated Ontology Construction for Legal Question Answering

Biomolecular Event Extraction using Natural Language Processing

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