Review of Tools for Semantics Extraction: Application in Tsunami Research Domain

Babič, František; Bureš, Vladimír; Čech, Pavel; Husáková, Martina; Mikulecký, Peter; Mls, Karel; Nacházel, Tomáš; Ponce, Daniela; Štekerová, Kamila; Triantafyllou, Ioanna; Tučník, Petr; Zanker, Marek

doi:10.3390/info13010004

Cited by 6 publications

(3 citation statements)

References 85 publications

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“…This review is also related to our previous research (Nacházel et al 2021 ) focused on analyzing tsunami-related data and datasets. At the same time, the possibilities of ontologies as perspective methods of representation and formal description of the observed domain are investigated (Babič et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Agent-based models of human response to natural hazards: systematic review of tsunami evacuation

et al. 2022

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This systematic review provides a comprehensive overview of tsunami evacuation models. The review covers scientific studies from the last decade (2012–2021) and is explicitly focused on models using an agent-based approach. The PRISMA methodology was used to analyze 171 selected papers, resulting in over 53 studies included in the detailed full-text analysis. This review is divided into two main parts: (1) a descriptive analysis of the presented models (focused on the modeling tools, validation, and software platform used, etc.), and (2) model analysis (e.g., model purpose, types of agents, input and output data, and modeled area). Special attention was given to the features of these models specifically associated with an agent-based approach. The results lead to the conclusion that the research domain of agent-based tsunami evacuation models is quite narrow and specialized, with a high degree of variability in the model attributes and properties. At the same time, the application of agent-specific methodologies, protocols, organizational paradigms, or standards is sparse. Supplementary Information The online version contains supplementary material available at 10.1007/s11069-022-05643-x.

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Section: Introductionmentioning

confidence: 99%

Agent-based models of human response to natural hazards: systematic review of tsunami evacuation

et al. 2022

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“…An ontology can be constructed from various sources. Domain-specific articles, existing ontologies, domain knowledge bases, terminological databases, dictionaries, lexical resources, HTML/XML documents, and text collections are among the primary sources for ontology learning (10) , (11) . Among the sources, textual documents are the important sources for building an ontology due to their rich knowledge.…”

Section: Introductionmentioning

confidence: 99%

Identifying Terms to Represent Concepts of a Work Process Ontology

Osman,

Noah,

Saad

2024

IJST

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Objectives: To identify ontology concepts from text documents for the construction of work process ontology. Methods: This study proposes a methodology to identify terms representing a work process ontology concept from a document. The methodology encompasses several key steps: document collecting, text pre-processing, term weighting and analysis, terms mapping, and domain expert relevance judgments. A comparison between the results of three different term weighting schemes, namely the Term Frequency (TF), Term Frequency-Inverse Document Frequency (TFIDF), and Mutual Information (MI) is made with the ontology concept that the domain expert has judged. Findings: The approaches adopted in this study have managed to extract ontological concepts from the targeted domain knowledge source. The findings of the comparison analysis suggest that the TFIDF term weighting scheme exhibits better results compared to the TF and MI weighting schemes. Novelty: A work process ontology is a structured knowledge describing daily operations in the government sector. However, there has been little to no effort in building the work process ontology. This study presents an integrated approach for identifying ontology concepts from documents within the domain of the work process. To the utmost extent of our understanding, this research initiative is the initial attempt to introduce a structured methodology for the semi-automatic extraction and evaluation of concepts and relationships within this domain. The findings can be utilised as a foundation for developing an ontology in the specific field. Keywords: Ontology, Work process, Text extraction, Natural language processing, Term weighting

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“…With the recent growing interest in the explicit modelling of events in structural conceptual models, ontology extraction has become one of the formidable trends and tools of use for explicitly representing events in structural models [7]. Ontologies are mainly perceived as a knowledge graph able to formally model different aspects of our real world [8]. A common issue with these ontologies is that, being manually crafted and maintained by domain experts, they tend to evolve relatively slow and become quickly outdated.…”

Section: Introductionmentioning

confidence: 99%

Models and Approaches for Comprehension of Dysarthric Speech Using Natural Language Processing: Systematic Review

Alaka¹,

Kasamani²

2023

JMIR Rehabil Assist Technol

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Background Speech intelligibility and speech comprehension for dysarthric speech has attracted much attention recently. Dysarthria is characterized by irregularities in the speed, strength, pitch, breath control, range, steadiness, and accuracy of muscle movements required for articulatory aspects of speech production. Objective This study examined the contributions made by other studies involved in dysarthric speech comprehension. We focused on the modes of meaning extraction used in generalizing speaker-listener underpinnings in light of semantic ontology extraction as a desired technique, applied method types, speech representations used, and databases sourced from. Methods This study involved a systematic literature review using 7 electronic databases: Cochrane Database of Systematic Reviews, Web of Science Core Collection, Scopus, PubMed, ACM, IEEE Xplore, and Google Scholar. The main eligibility criterion was the extraction of meaning from dysarthric speech using natural language processing or understanding approaches to improve on dysarthric speech comprehension. In total, out of 834 search results, 30 studies that matched the eligibility requirements were acquired following screening by 2 independent reviewers, with a lack of consensus being resolved through joint discussion or consultation with a third party. In order to evaluate the studies’ methodological quality, the risk of bias assessment was based on the Cochrane risk-of-bias tool version 2 (RoB2) with 23 of the studies (77%) registering low risk of bias and 7 studies (33%) raising some concern over the risk of bias. The overall quality assessment of the study was done using TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis). Results Following a review of 30 primary studies, this study revealed that the reviewed studies focused on natural language understanding or clinical approaches, with an increase in proposed solutions from 2020 onwards. Most studies relied on speaker-dependent speech features, while others used speech patterns, semantic knowledge, or hybrid approaches. The prevalent use of vector representation aligned with natural language understanding models, while Mel-frequency cepstral coefficient representation and no representation approaches were applied in neural networks. Hybrid representation studies aimed to reconstruct dysarthric speech or improve comprehension. Comprehensive databases, like TORGO and UA-Speech, were commonly used in combination with other curated databases, while primary data was preferred for specific or unique research objectives. Conclusions We found significant gaps in dysarthric speech comprehension characterized by the lack of inclusion of important listener or speech-independent features in the speech representations, mode of extraction, and data sources used. Further research is therefore proposed regarding the formulation of models that accommodate listener and speech-independent features through semantic ontologies that will be useful in the inclusion of key features of listener and speech-independent features for meaning extraction of dysarthric speech.

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Review of Tools for Semantics Extraction: Application in Tsunami Research Domain

Cited by 6 publications

References 85 publications

Agent-based models of human response to natural hazards: systematic review of tsunami evacuation

Agent-based models of human response to natural hazards: systematic review of tsunami evacuation

Identifying Terms to Represent Concepts of a Work Process Ontology

Models and Approaches for Comprehension of Dysarthric Speech Using Natural Language Processing: Systematic Review

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