International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 08 | Aug 2024
p-ISSN: 2395-0072
www.irjet.net
Harvesting Knowledge: A Critical Review of Ontology-Based Information Extraction Across Diverse Scientific Domains Christeena Raphy1, Aparna Mohan2 1 M.Tech Student, Dept. of Computer Science, Malabar College of Engineering and Technology, Kerala, India
2 Assistant Professor, Dept. of Computer Science, Malabar College of Engineering and Technology, Kerala, India
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Abstract - Ontology-Based Information Extraction (OBIE)
The significance of OBIE lies in its ability to transform raw data into structured knowledge, which is essential for advancing research and innovation. By utilizing ontologies, OBIE systems can provide a more accurate and meaningful representation of data, facilitating better interoperability and more sophisticated querying capabilities. This is particularly important in scientific research, where the ability to integrate and analyze data from multiple sources is crucial for making informed decisions and driving discoveries.
has gained prominence as a pivotal technique for transforming unstructured data into structured knowledge, leveraging the semantic and organizational strengths of ontologies. This critical review explores the diverse applications and methodologies of OBIE across various scientific domains, emphasizing its role in enhancing data interoperability and precision. Key advancements include the integration with the Semantic Web, the development of expressive ontologies, and the implementation of machine learning and deep learning techniques. The literature highlights the versatility of OBIE, with applications ranging from academic knowledge repositories to monitoring online content and geological data exploration. Comparative analysis reveals the strengths and limitations of various OBIE methodologies, such as rule-based approaches, machine learning, semantic-based ontology mapping, and transformerbased learning. While rule-based methods are simple to implement, machine learning and advanced NLP techniques offer scalability and higher precision but require significant computational resources. The review suggests that future research should focus on hybrid models that combine these methodologies to enhance scalability, real-time processing, and interoperability. This comprehensive synthesis not only underscores the transformative potential of OBIE in scientific research but also sets the stage for further innovations in the field.
2. LITERATURE REVIEW Ontology-based information extraction (OBIE) has emerged as a pivotal method for harnessing and structuring knowledge from vast and diverse datasets across scientific domains. The utilization of ontologies facilitates the semantic interpretation of data, enhancing the precision and relevance of information extraction processes. This literature review synthesizes recent advancements and methodologies in OBIE, focusing on the integration with the Semantic Web, the development of expressive ontologies, and the application of OBIE in various scientific contexts. Martinez-Rodriguez et al. [1] provide a comprehensive survey on the intersection of information extraction (IE) and the Semantic Web. They discuss the evolution of techniques and tools that leverage semantic technologies to enhance IE processes. Their work highlights the critical role of ontologies in improving data interoperability and enabling more sophisticated semantic queries. This foundational understanding sets the stage for exploring specific applications and methodologies in OBIE.
Key Words: Data Interoperability, Information Extraction, Machine Learning , Ontology-Based Information Extraction (OBIE), Semantic Web, Structured Knowledge
1.INTRODUCTION
Petrucci emphasizes the importance of learning expressive ontologies for the Semantic Web. He presents methodologies that enhance the expressiveness of ontologies, enabling more nuanced and accurate information extraction [2]. His work underlines the challenges of balancing complexity and usability in ontology development, which is crucial for effective OBIE.
In the era of big data, the challenge of efficiently extracting and structuring vast amounts of information from diverse and often unstructured datasets is paramount. OntologyBased Information Extraction (OBIE) has emerged as a powerful method to address this challenge by leveraging the semantic richness and organizational capabilities of ontologies. OBIE enables the semantic interpretation of data, thereby enhancing the precision and relevance of information extraction processes across various scientific domains.
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Suganya and Porkodi provide a review of OBIE techniques, highlighting the benefits of using ontologies to improve the accuracy and efficiency of information extraction [3]. Their review covers various algorithms and frameworks
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