A Comprehensive Overview of Ontology: Fundamental and Research
Directions

Patel, Archana; Debnath, Narayan C.

doi:10.2174/2666145415666220914114301

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Importantly, ontologies are used to reason about the described concepts, i.e., to derive implicit knowledge from the existing explicit knowledge and automated inference utilizing reasoning engines [40]. In addition, ontologies are often used to provide a common understanding of a domain between different people or systems [41][42][43].…”

Section: Improving Data Lakes With Ontologiesmentioning

confidence: 99%

Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

Horvat,

Krtalić,

Akagić

et al. 2024

Electronics

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As landmines and other unexploded ordnances (UXOs) present a great risk to civilians and infrastructure, humanitarian demining is an essential component of any post-conflict reconstruction. This paper introduces the Minefield Observatory, a novel web-based datastore service that semantically integrates diverse data in humanitarian demining to comprehensively and formally describe suspected minefields. Because of the high heterogeneity and isolation of the available minefield datasets, extracting relevant information to determine the optimal course of demining efforts is time-consuming, labor-intensive and requires highly specialized knowledge. Data consolidation and artificial intelligence techniques are used to convert unstructured data sources and store them in an ontology-based knowledge database that can be efficiently accessed through a Semantic Web application serving as the Minefield Observatory user interface. The MINEONT+ ontology was developed to integrate diverse mine scene information obtained through non-technical surveys and remote sensing, such as aerial and hyperspectral satellite imagery, indicators of mine presence and absence, contextual data, terrain analysis information, and battlefield reports. The Minefield Observatory uses the Microdata API to embed this dataset into dynamic HTML5 content, allowing seamless usage in a user-centric web tool. A use-case example was provided demonstrating the viability of the proposed approach.

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Section: Improving Data Lakes With Ontologiesmentioning

confidence: 99%

Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

Horvat,

Krtalić,

Akagić

et al. 2024

Electronics

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Understanding Inflicted Injuries in Young Children: Toward an Ontology Based Approach

Maikore,

Mazumdar,

Offiah

et al. 2024

Lecture Notes in Computer Science

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Extending a Pretrained Language Model (BERT) using an Ontological Perspective to Classify Students’ Scientific Expertise Level from Written Responses

Wang,

Haudek,

Manzanares

et al. 2024

Preprint

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The complex and interdisciplinary nature of scientific concepts presents formidable challenges for students in developing their knowledge-in-use skills. The utilization of computerized analysis for evaluating students’ contextualized constructed responses offers a potential avenue for educators to develop personalized and scalable interventions, thus supporting the teaching and learning of science consistent with contemporary calls. While prior research in artificial intelligence has demonstrated the effectiveness of algorithms, including Bidirectional Encoder Representations from Transformers (BERT), in tasks like automated classifications of constructed responses, these efforts have predominantly leaned towards text-level features, often overlooking the exploration of conceptual ideas embedded in students’ responses from a cognitive perspective. Despite BERT’s performance in downstream tasks, challenges may arise in domain-specific tasks, particularly in establishing knowledge connections between specialized and open domains. These challenges become pronounced in small-scale and imbalanced educational datasets, where the available information for fine-tuning is frequently inadequate to capture task-specific nuances and contextual details. The primary objective of the present study is to investigate the effectiveness of a pretrained language model (BERT), when integrated with an ontological framework aligned with a contextualized science assessment, in classifying students’ expertise levels in scientific explanation. Our findings indicate that while pretrained language models such as BERT contribute to enhanced performance in language-related tasks within educational contexts, the incorporation of identifying domain-specific terms and extracting and substituting with their associated sibling terms in sentences through ontology-based systems can significantly improve classification model performance. Further, we qualitatively examined student responses and found that, as expected, the ontology framework identified and substituted key domain specific terms in student responses that led to more accurate predictive scores. The study explores the practical implementation of ontology in assessment evaluation to facilitate formative assessment and formulate instructional strategies.

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A Comprehensive Overview of Ontology: Fundamental and Research Directions

Cited by 3 publications

References 30 publications

Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

Understanding Inflicted Injuries in Young Children: Toward an Ontology Based Approach

Extending a Pretrained Language Model (BERT) using an Ontological Perspective to Classify Students’ Scientific Expertise Level from Written Responses

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