Ontology Based Semantic Web Information Retrieval Enhancing Search Significance

The web contain huge amount of structured as well as unstructured data/information. This varying nature of data may yield a retrieval response that is expected to contain relevant response that is expected to contain relevant as well as irrelevant data while directing search. In order to filter out irrelevance in the search result, numerous methodologies have been used to extract more and more relevant search responses in retrieval. This work has adopted semantic search dealing directly with the knowledge base. The approach incorporates Query pattern evolution and semantic keyword matching with final detail to enhance significance of relevant data retrieval. The proposed method is implemented in open source computing tool environment and the result obtained thereof are compared with that of earlier used methodologies

Constructing a Multilingual E-Learning Ontology through Web Crawling and Scraping

The emergence of digital technologies has transformed the landscape of education, driving the exploration of innovative methods to improve the efficiency and effectiveness of university e- learning. This study focuses on leveraging network management principles in combination with web crawling trends to propose a novel approach: a web crawling and scraping-driven method for constructing a multilingual ontology tailored specifically for university e-learning. The primary goal of this research is to create a comprehensive and continuously updated knowledge repository by systematically gathering and extracting information from a wide range of online sources. By incorporating multilingual capabilities into the proposed ontology, the aim is to transcend language barriers and establish a globally accessible and inclusive e-learning environment. This approach recognizes the intricate relationship between technology and education, highlighting the potential of automated data retrieval and ontology construction in reshaping the future of university e-learning. This research contributes significantly to the rapidly growing field of educational technology by introducing a forward-thinking paradigm. It empowers both educators and learners with a versatile and personalized learning experience that transcends cultural and linguistic boundaries. As the digital era continues to evolve, this approach serves as a beacon of innovation, exemplifying the transformative power of integrating cutting-edge technology with pedagogical efforts. In essence, this study presents a groundbreaking approach to enhance university e- learning by harnessing the capabilities of web crawling, scraping, and multilingual ontology construction. It emphasizes the importance of adapting to the ever-evolving digital landscape to provide an inclusive and accessible education experience for learners worldwide. Ultimately, this research represents a significant step forward in the ongoing effort to revolutionize education through the integration of advanced technology and pedagogical innovation

Ontology-Based Semantic Retrieval for Education Management Systems

The traditional information retrieval technologies are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. As a new technology of information retrieval, semantic retrieval can retrieve information resource fully and precisely based on the knowledge understanding and knowledge reasoning. Ontology, which can well represent and reason about the domain knowledge, is proved to be very useful in the semantic retrieval. On this basis, in this paper, we propose a complete ontology-based semantic retrieval approach and framework for education management system. Firstly, we present some rules for constructing domain ontology from the education management system; Then, a semantic annotation method of the constructed ontology is given; Further, the ontologybased semantic retrieval algorithmis proposed; Finally, a complete framework is developed and some experiments are done. Conducted experiments show that our semantic retrieval model obtained comparable and better performance results than the traditional information retrieval technology for education management system

Lexicalização de ontologias : o relacionamento entre conteúdo e significado no contexto da recuperação da informação

Tese (doutorado)—Universidade de Brasília, Faculdade de Ciência da Informação, Programa de Pós-Graduação em Ciência da Informação, 2015.Investiga as tecnologias da Web Semântica e as técnicas de Processamento de Linguagem Natural para a elaboração semiautomática de uma base léxico-ontológica, em Português, circunscrita ao domínio de risco financeiro que, incorporada ao modelo de recuperação da informação, visa melhorar a precisão. Identifica teorias, ferramentas e técnicas que propiciam a automatização de procedimentos que extraem elementos ontológicos e léxicos de bases estruturadas e não estruturadas. Esses elementos combinados na forma de base de dados apoiam a geração de índice léxico-semântico que fornece insumos para a proposição de modelo de recuperação da informação semântica. A metodologia adotada se apresenta em: levantamento de fundamentos teóricos e metodológicos, ferramentas e bases de dados ontológicas e textuais; construção de ontologia e base léxico-ontológica com foco no risco financeiro; elaboração de modelo de recuperação da informação semântica; avaliação do modelo realizada num corpus de informação relacionada ao risco financeiro que foi indexado nos moldes tradicionais e contemplando a informação semântica para, então, medir a precisão nas duas situações. Os resultados alcançados demonstram a utilização da metodologia, no domínio de risco financeiro em Português, para a elaboração da ontologia OntoRisco, da base léxico-semântica RiscoLex e da proposta de modelo de recuperação da informação semântica que mostrou resultados superiores aos modelos de recuperação da informação tradicionais, nos testes realizados. Conclui que os resultados satisfatórios mostram a aplicabilidade da proposta metodológica para o domínio em questão e aponta para a possibilidade de expandir a outros domínios com as devidas adaptações dos recursos utilizados. O estudo contribui para a área de representação e organização da informação e do conhecimento na medida em que fornece metodologia, aporte teórico e insumos para que profissionais continuem promovendo o desenvolvimento da Ciência da Informação.This research investigates Semantic Web technologies, and Natural Language Processing techniques in order to semi automatically develop a lexicon-ontological database in Portuguese. This base is intended to improve the precision measurement in the financial risk domain by coupling it into an information retrieval model. It identi This research investigates Semantic Web technologies, and Natural Language Processing techniques in order to semi automatically develop a lexicon-ontological database in Portuguese. This base is intended to improve the precision measurement in the financial risk domain by coupling it into an information retrieval model. It identifies theories, tools and techniques that allow the automation of procedures that extract ontological and lexical elements from structured and non-structured databases. When these lexical and ontological elements are together in a database format, they give support to the generation of lexico-semantic index that can help the creation of semantic information retrieval models. The methodology used is as follows: surveys of methodological and theoretical fundamentals, tools, as well as ontological and textual databases; creation of an ontology and a lexicon-ontology base in the financial risk domain; elaboration of semantic information retrieval model; evaluation of the model using an information corpus related to financial risks – it was indexed by using traditional techniques, i.e. keywords, and also using semantic information; and then the precision of both situations was finally measured. The results achieved demonstrate the methodology used in the financial risk domain to elaborate the ontology, OntoRisco, using the lexico-semantic database, RiscoLex, and the semantic informational retrieval model proposed in this work. In the tests executed, the results were better than those presented by using traditional information retrieval models. It concludes that the results were quite satisfying since they show the applicability of the methodological proposal to the financial risk domain. In addition, it points the possibility of extending the proposal to other domains with only some adaptations.es theories, tools and techniques that allow the automation of procedures that extract ontological and lexical elements from structured and non-structured databases. When these lexical and ontological elements are together in a database format, they give support to the generation of lexico-semantic index that can help the creation of semantic information retrieval models. The methodology used is as follows: surveys of methodological and theoretical fundamentals, tools, as well as ontological and textual databases; creation of an ontology and a lexicon-ontology base in the financial risk domain; elaboration of semantic information retrieval model; evaluation of the model using an information corpus related to financial risks – it was indexed by using traditional techniques, i.e. keywords, and also using semantic information; and then the precision of both situations was finally measured. The results achieved demonstrate the methodology used in the financial risk domain to elaborate the ontology, OntoRisco, using the lexico-semantic database, RiscoLex, and the semantic informational retrieval model proposed in this work. In the tests executed, the results were better than those presented by using traditional information retrieval models. It concludes that the results were quite satisfying since they show the applicability of the methodological proposal to the financial risk domain. In addition, it points the possibility of extending the proposal to other domains with only some adaptations