Ontology-based information retrieval for e-Learning of computer science

Ahmed-Ouamer, Rachid; Hammache, Arezki

doi:10.1109/icmwi.2010.5648113

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…An ontology refers to an engineering artifact, constituted by a specific vocabulary used to describe a certain reality, plus an explicit assumption set regarding the intended meaning of the vocabulary words. In other words, an ontology defines a representational term set that we entitle concepts [20], providing potential terms for describing our knowledge about the domain. It is basically a way to represent and share knowledge about the domain and its concepts.…”

Section: A New Wine Ontologymentioning

confidence: 99%

Wine Ontology Influence in a Recommendation System

Oliveira

Silva

Bernardino

2021

BDCC

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Wine is the second most popular alcoholic drink in the world behind beer. With the rise of e-commerce, recommendation systems have become a very important factor in the success of business. Recommendation systems analyze metadata to predict if, for example, a user will recommend a product. The metadata consist mostly of former reviews or web traffic from the same user. For this reason, we investigate what would happen if the information analyzed by a recommendation system was insufficient. In this paper, we explore the effects of a new wine ontology in a recommendation system. We created our own wine ontology and then made two sets of tests for each dataset. In both sets of tests, we applied four machine learning clustering algorithms that had the objective of predicting if a user recommends a wine product. The only difference between each set of tests is the attributes contained in the dataset. In the first set of tests, the datasets were influenced by the ontology, and in the second set, the only information about a wine product is its name. We compared the two test sets’ results and observed that there was a significant increase in classification accuracy when using a dataset with the proposed ontology. We demonstrate the general applicability of the methodology to other cases, applying our proposal to an Amazon product review dataset.

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Section: A New Wine Ontologymentioning

confidence: 99%

Wine Ontology Influence in a Recommendation System

Oliveira

Silva

Bernardino

2021

BDCC

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“…For the evaluation of their proposed methodology, they used 145,316 documents obtained from CNN website. 1 Ahmed-Ouamer and Hammache [43] presented another ontology based information retrieval approach for e-learning. They used vector space model for both query and documents and calculated their similarity through cosine similarity measure.…”

Section: A Vector Space Based Information Retrievalmentioning

confidence: 99%

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

et al. 2019

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Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files. Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task. Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word. Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account. Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent. Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data. This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types. Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages. INDEX TERMS Ontology, text retrieval, multimedia retrieval, cross lingual retrieval.

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“…Since 1948, the date of the appearance of the search engines and until today several researches have resulted considerable developments in all search engines fields. In the case of e-learning there are several important works [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], most of them adopt metadata, classical, and semantic indexing techniques, where there are some gaps sometimes.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects

Guemmat

Ouahabi

2019

Int. J. Emerg. Technol. Learn.

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Educational search engines are important for users to find learning objects (LO). However, these engines have not reached maturity in terms of searching, they suffer from several worries like the deep extraction of notions which diminishes their performance. The purpose of this paper is to propose a new approach that allows depth extraction of LO’s notions to increase the relevance level of educational search engines. The proposed approach focuses on semi-automatic indexing of textual LO and more precisely the deeper relations of sentences that flesh out explanations. It based on linguistic structures and semantic distances between specific and generic notions according to OntOAlgO ontology. The notions obtained will be improved by learning object metadata (LOM) and will be represented semantically in final index. The tests performed on algorithmic LO, proving the usefulness of our approach to educational search engines. It increases the degree of precision and recall of notions extracted from LO.

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Ontology-based information retrieval for e-Learning of computer science

Cited by 10 publications

References 18 publications

Wine Ontology Influence in a Recommendation System

Wine Ontology Influence in a Recommendation System

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects

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