The Representation of Causality and Causation with Ontologies: A Systematic Literature Review

Sawesi, Suhila; Rashrash, Mohamed; Dammann, Olaf

doi:10.5210/ojphi.v14i1.12577

Cited by 2 publications

(1 citation statement)

References 52 publications

(51 reference statements)

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“…Finally, in other research [44], researchers were looking for the ontology of causality in the eld of medicine, and a review study on the articles of 8 knowledge bases of IEEE, PubMed, ACM, Scopus, web of science, OBO, Ontobee and Bioportal was performed. From 1970 until the time of writing the article, the articles in this eld have been monitored, selected and reviewed by human experts.…”

Section: Casualty Recognition Methodsmentioning

confidence: 99%

A Knowledge-Based Approach for Recognizing Textual Entailments with a Focus on Causality and Contradiction

Rahimi,

ShamsFard

2024

Preprint

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There is usually implicit information in natural language texts that can be deduced from the text using background or common-sense knowledge. Recognition of textual entailments falls into this category. It involves receiving two pieces of text and deciding whether the second one entails or contradicts the first one, or if there is no such relation between the two. One of the types of entailment is causality, where the event or situation in one text can cause the event or situation in another text. The aim of this paper is to improve textual entailment by identifying causality and contradiction relationships between pieces of text using knowledge sources. To achieve this, we employ a knowledge-based method that recognizes textual entailments through the use of linguistic and common-sense knowledge (ontologies), with a particular emphasis on implicit or ambiguous causal and contradictory relationships. For this purpose, we first developed two ontologies, including causality and contradiction relations among concepts. Then, two concurrent paths in a pipeline are traversed. In the first path, a rule-based system is utilized to identify simple contradiction relations. The rules are extracted semi-automatically through data mining methods. In the second path, the provided knowledge of causality and contradiction is injected into language models using prompt engineering. Subsequently, the enriched language models undergo fine-tuning with entailment datasets to carry out textual entailment tasks. The results indicate that the combination of the rule-based method and enriched models with causality and contradiction knowledge yields the best performance. Our leading system demonstrates an accuracy of 87.5% on RTE tasks, trained and tested on the FarsTail dataset. This reflects a notable 3.5% improvement over baselines and state-of-the-art systems.

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Section: Casualty Recognition Methodsmentioning

confidence: 99%

A Knowledge-Based Approach for Recognizing Textual Entailments with a Focus on Causality and Contradiction

Rahimi,

ShamsFard

2024

Preprint

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Electronic knowledge books (eK-Books) as a medium to capitalise on and transfer scientific, engineering, operational, technological and craft knowledge

Baudrit,

Fernandez,

Couteaux

et al. 2024

PLoS ONE

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The capitalisation on and transfer of technological, engineering and scientific knowledge associated with empirical know-how is an important issue for the sustainability and development of manufacturing. Indeed, certain sectors of industry are facing the increasing ageing of the labour force, recruitment difficulties and high staff turnover, leading to a loss of knowledge and know-how. In a context of numerical and digital transition and the migration of processes to industry 4.0, one of major challenges manufacturers face today is their capacity to build intelligent platforms for acquiring, storing and transferring their know-how and knowledge. It is crucial to create new media and tools for staff training and development capable of capturing knowledge and reusing it to create a project history through expertise and data collection. This paper presents the methodology and guidelines for implementing electronic knowledge books (eK-Books), along with their uses. The eK-Book is a semantic web-based hypertext medium (channel) allowing stakeholders to capitalise on, structure and transfer knowledge by using concept maps, process maps, influence graphs, downloadable documents, web pages and hypermedia knowledge sheets. They are intended for engineers, expert or novice technicians, manufacturers, sector coordinators and plant managers, as well as trainers and learners. They are usable and manageable in all types of environments and with different levels of accessibility. This paper highlights (1) the transfer knowledge capacity of eK-Books and (2) their usability in two agri-food sectors namely (1) the cheese sector with protected designation of origin (PDO) and protected geographical indication (PGI), and (2) the butchery and cold meat sectors.

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The Representation of Causality and Causation with Ontologies: A Systematic Literature Review

Cited by 2 publications

References 52 publications

A Knowledge-Based Approach for Recognizing Textual Entailments with a Focus on Causality and Contradiction

A Knowledge-Based Approach for Recognizing Textual Entailments with a Focus on Causality and Contradiction

Electronic knowledge books (eK-Books) as a medium to capitalise on and transfer scientific, engineering, operational, technological and craft knowledge

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