Leyla Zhuhadar

Semantic Information Retrieval for Personalized E-Learning

We present an approach for personalized retrieval in an e-learning platform, that takes advantage of semantic Web standards to represent the learning content and the user/learner profiles as ontologies, and that re-ranks search results/lectures based on how the contained terms map to these ontologies. One important aspect of our approach is the combination of an authoritatively supplied taxonomy by the colleges, with the data driven extraction (via clustering) of a taxonomy from the documents themselves, thus making it easier to adapt to different learning platforms, and making it easier to evolve with the document/lecture collection. Our experimental results show that the learners context can be effectively used for improving the precision and recall in e-learning content retrieval, particularly by re-ranking the search results based on the learners past activities.

Multi-model Ontology-Based Hybrid Recommender System in E-learning Domain

This paper introduces a multi-model ontology-based framework for semantic search of educational content in E-learning repository of courses, lectures, multimedia resources, etc. This hybrid recommender system is driven by two types of recommendations: content-based (domain ontology model) and rule-based (learner’s interest-based and cluster-based). The domain ontology is used to represent the learning materials. In this context, the ontology is composed by a hierarchy of concepts and sub-concepts. Whereas, the learner’s ontology model represents a subset of the domain ontology, and the cluster-based recommendations are added as additional semantic recommendations to the model. Combining the content-based with the rule-based provides the user with hybrid recommendations. All of them influenced the re-ranking of the retrieved documents with different weights. Our proposed approach has been implemented on the HyperManyMedia1 platform.

Improving Recall and Precision of a Personalized Semantic Search Engine for E-learning

The main objective of this paper is to propose and evaluate an architecture that provides, manages, and collects data that permit high levels of adaptability and relevance to the user profiles. In addition, we implement this architecture on a platform called HyperManyMedia. To achieve this objective, an approach for personalized search is implemented that takes advantage of the semantic Web standards (RDF and OWL) to represent the content and the user profiles. The framework consists of the following phases: (1) building the semantic E-learning domain using the known college and course information as concept and sub-concept, (2) generating the semantic user profiles as ontologies, (3) clustering the documents to discover more refined sub-concepts, (4) reranking the user’s search results based on his/her profile, and (5) providing the user with semantic recommendations. The implementation of the ontologies models is separate from the design and implementation of the information retrieval system, thus providing a modular framework that is easy to adapt and port to other platforms. Finally, the experimental results show that the user context can be effectively used for improving the precision and recall in E-learning search, particularly by re-ranking the search results based on the user profiles.

Personalized cluster-based semantically enriched web search for e-learning

We present an approach for personalized search in an e-learning platform, that takes advantage of semantic Web standards (RDF and OWL) to represent the content and the user profiles. Personalizing the finding of needed information in an e-learning environment based on context requires intelligent methods for representing and matching the learning needs and the variety of learning contexts. Our framework consists of the following phases: (1) building the semantic e-learning domain using the known college and course information as concepts and sub-concepts in a lecture ontology, (2) generating the semantic learners profile as an ontology from navigation logs that record which lectures have been accessed, (3) clustering the documents to discover more refined sub-concepts (top terms in each cluster) than provided by the available college and course taxonomy, (4) re-ranking the learners search results based on the matching concepts in the learning content and the user profile, and (5) providing the learner with semantic recommendations during the search process, in the form of terms from the closest matching clusters of their profile. One important aspect of our approach is the combination of an authoritatively supplied taxonomy by the colleges, with the data driven extraction (via clustering) of a taxonomy from the documents themselves, thus making it easier to adapt to different learning platforms, and making it easier to evolve with the document/lecture collection. Our experimental results show that the learners context can be effectively used for improving the precision and recall in e-learning search, particularly by re-ranking the search results based on the learners past activities.

Visual Ontology-Based Information Retrieval System

The Semantic Web has been evolved as a key technology, bringing new insights and solutions to well documented problems in E-learning. The research field of Semantic E-learning covers a wide range of research problems. In this paper, we present an approach to visualize an ontology driven information retrieval system in the context of E-learning. This approach has been implemented on the HyperManyMedia platform, and is already being used by online students at WKU. The design of this work is based on the following steps: (1) presenting the HyperManyMedia domain as a tree consisting of concepts and subconcepts where the leaves are the documents (lectures), (2) embedding Nutch search engine into the platform, (3) reconfiguring Nutch search engine to accommodate the semantic search, (4) using Prefuse as a visual tool to present the ontology as a graph, and (5) embedding the graph into the platform as an applet so that learners can navigate visually through the ontology of the domain by clicking on nodes.

Visual knowledge representation of conceptual semantic networks

This article presents methods of using visual analysis to visually represent large amounts of massive, dynamic, ambiguous data allocated in a repository of learning objects. These methods are based on the semantic representation of these resources. We use a graphical model represented as a semantic graph. The formalization of the semantic graph has been intuitively built to solve a real problem which is browsing and searching for lectures in a vast repository of colleges/courses located at Western Kentucky University (http://HyperManyMedia.wku.edu). This study combines Formal Concept Analysis (FCA) with Semantic Factoring to decompose complex, vast concepts into their primitives in order to develop knowledge representation for the HyperManyMedia [we proposed this term to refer to any educational material on the web (hyper) in a format that could be a multimedia format (image, audio, video, podcast, vodcast) or a text format (HTML webpages, PHP webpages, PDF, PowerPoint)] platform. Also, we argue that the most important factor in building the semantic representation is defining the hierarchical structure and the relationships among concepts and subconcepts. In addition, we investigate the association between concepts using Concept Analysis to generate a lattice graph. Our domain is considered as a graph, which represents the integrated ontology of the HyperManyMedia platform. This approach has been implemented and used by online students at WKU (http://www.wku.edu).

Multi-language Ontology-Based Search Engine

One of the first Multi-Language Information Retrieval (MLIR) systems was implemented in 1969 by Gerard Salton who enhanced his SMART system to retrieve multilingual documents in two languages, English and German. However, the research field of MLIR is still struggling since the majority of information retrieval systems are monolingual and more precisely English-based, even though only 6% of the world’s population native language have as English [14]. This paper presents a Multi-Language Information Retrieval (MLIR) approach that falls into the area of Domain Specific Information Retrieval (E-learning being the domain). The approach we followed is a synergistic approach between (1) Thesaurus-based Approach and (2) Corpus-based Approach. This research has been implemented on a real platform called HyperManyMedia1 at Western Kentucky University.

Toward the Design of a Recommender System: Visual Clustering and Detecting Community Structure in a Web Usage Network

The identification of community structure is one of the fundamental questions in the analysis of large scale complex networks. In this work, we propose a novel approach to extracting communities within a large network of cyber learners and learning resources. The technique used is a heuristic which initially performs clustering using force-based visualization algorithms and then relies on network modularity to select good decompositions from those found visually. Through testing, we have determined appropriate parameters for optimal performance. Finally, we use the community detection method to design a visual recommender system to recommend learning resources to cyber learners within the same community.

A synergistic strategy for combining thesaurus-based and corpus-based approaches in building ontology for multilingual search engines

Cross-language search engine as a tool for co-learning multilingual ontology.The methodology for building cross-language search engine.Combining thesaurus-based and corpus-based approaches.Application of a query translation to retrieve multilingual documents.How to evaluate a multilingual information retrieval system. In this article we illustrate a methodology for building cross-language search engine. A synergistic approach between thesaurus-based approach and corpus-based approach is proposed. First, a bilingual ontology thesaurus is designed with respect to two languages: English and Spanish, where a simple bilingual listing of terms, phrases, concepts, and subconcepts is built. Second, term vector translation is used - a statistical multilingual text retrieval techniques that maps statistical information about term use between languages (Ontology co-learning). These techniques map sets of t f id f term weights from one language to another. We also applied a query translation method to retrieve multilingual documents with an expansion technique for phrasal translation. Finally, we present our findings.

Cyberlearners and learning resources

The discovery of community structure in real world networks has transformed the way we explore large systems. We propose a visual method to extract communities of cyberlearners in a large interconnected network consisting of cyberlearners and learning resources. The method used is heuristic and is based on visual clustering and a modularity measure. Each cluster of users is considered as a subset of the community of learners sharing a similar domain of interest. Accordingly, a recommender system is proposed to predict and recommend learning resources to cyberlearners within the same community. Experiments on real, dynamic data reveal the structure of community in the network. Our approach used the optimal discovered structure based on the modularity value to design a recommender system.