An Ontology-Based Approach to Semi-Automate Systematic Literature Reviews

Ali, Asad; Gravino, Carmine

doi:10.1109/icosst.2018.8632205

“…The second most common exclusion category were articles that presented techniques or systems utilizing pre-extracted data (n=4). Ali and Gravino (2018), for example, proposed an ontology-based SLR system with semantic web technologies; however, the data (derived from a prior review conducted by the authors) were added to the ontology system after the manual extraction stage. Finally, articles were excluded due to exclusive application in medical/clinical research (n=2), or the proposed tool had not yet been implemented (n=2).…”

Section: Excluded Publicationsmentioning

confidence: 99%

(Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review

Legate,

Nimon,

Noblin

2024

F1000Res

0

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Background An abundance of rapidly accumulating scientific evidence presents novel opportunities for researchers and practitioners alike, yet such advantages are often overshadowed by resource demands associated with finding and aggregating a continually expanding body of scientific information. Data extraction activities associated with evidence synthesis have been described as time-consuming to the point of critically limiting the usefulness of research. Across social science disciplines, the use of automation technologies for timely and accurate knowledge synthesis can enhance research translation value, better inform key policy development, and expand the current understanding of human interactions, organizations, and systems. Ongoing developments surrounding automation are highly concentrated in research for evidence-based medicine with limited evidence surrounding tools and techniques applied outside of the clinical research community. The goal of the present study is to extend the automation knowledge base by synthesizing current trends in the application of extraction technologies of key data elements of interest for social scientists. Methods We report the baseline results of a living systematic review of automated data extraction techniques supporting systematic reviews and meta-analyses in the social sciences. This review follows PRISMA standards for reporting systematic reviews. Results The baseline review of social science research yielded 23 relevant studies. Conclusions When considering the process of automating systematic review and meta-analysis information extraction, social science research falls short as compared to clinical research that focuses on automatic processing of information related to the PICO framework. With a few exceptions, most tools were either in the infancy stage and not accessible to applied researchers, were domain specific, or required substantial manual coding of articles before automation could occur. Additionally, few solutions considered extraction of data from tables which is where key data elements reside that social and behavioral scientists analyze.

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“…In this sense, ontological models have important properties when it comes to capacity in terms of inference and computation time. In [44], the importance of using the Pellet, Jena, and Protégé tools.…”

Section: Pellet Reasonermentioning

confidence: 99%

Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients

Salazar

¹

,

Lanzarini

²

,

Gómez

³

et al. 2023

Healthcare

0

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The present work describes the development of a conceptual representation model of the domain of the theory of formal grammars and abstract machines through ontological modeling. The main goal is to develop an ontology capable of deriving new knowledge about the mood of an Alzheimer’s patient in the categories of wandering, nervous, depressed, disoriented or bored. The patients are from elderly care centers in Ambato Canton-Ecuador. The population consists of 147 individuals of both sexes, diagnosed with Alzheimer’s disease, with ages ranging from 75 to 89 years. The methods used are the taxonomic levels, the semantic categories and the ontological primitives. All these aspects allow the computational generation of an ontological structure, in addition to the use of the proprietary tool Pellet Reasoner as well as Apache NetBeans from Java for process completion. As a result, an ontological model is generated using its instances and Pellet Reasoner to identify the expected effect. It is noted that the ontologies come from the artificial intelligence domain. In this case, they are represented by aspects of real-world context that relate to common vocabularies for humans and applications working in a domain or area of interest.

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(Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review

Legate,

Nimon,

Noblin

2024

F1000Res

0

View full text Add to dashboard Cite

Background An abundance of rapidly accumulating scientific evidence presents novel opportunities for researchers and practitioners alike, yet such advantages are often overshadowed by resource demands associated with finding and aggregating a continually expanding body of scientific information. Data extraction activities associated with evidence synthesis have been described as time-consuming to the point of critically limiting the usefulness of research. Across social science disciplines, the use of automation technologies for timely and accurate knowledge synthesis can enhance research translation value, better inform key policy development, and expand the current understanding of human interactions, organizations, and systems. Ongoing developments surrounding automation are highly concentrated in research for evidence-based medicine with limited evidence surrounding tools and techniques applied outside of the clinical research community. The goal of the present study is to extend the automation knowledge base by synthesizing current trends in the application of extraction technologies of key data elements of interest for social scientists. Methods We report the baseline results of a living systematic review of automated data extraction techniques supporting systematic reviews and meta-analyses in the social sciences. This review follows PRISMA standards for reporting systematic reviews. Results The baseline review of social science research yielded 23 relevant studies. Conclusions When considering the process of automating systematic review and meta-analysis information extraction, social science research falls short as compared to clinical research that focuses on automatic processing of information related to the PICO framework. With a few exceptions, most tools were either in the infancy stage and not accessible to applied researchers, were domain specific, or required substantial manual coding of articles before automation could occur. Additionally, few solutions considered extraction of data from tables which is where key data elements reside that social and behavioral scientists analyze.

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An Ontology-Based Approach to Semi-Automate Systematic Literature Reviews

Cited by 3 publications

References 20 publications

(Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review

(Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review

Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients

(Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review

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