یک چارچوب مشترک برای به اشتراک گذاری اطلاعات در سیستم های مدیریت یادگیری الکترونیکی

Internet Learning Management Systems (LMSs) are powerful tools that help us in our daily teaching and learning activities. Most users and software are mainly focused in content dissemination and group works, but the possibilities that Internet LMSs could offer go further. Some recent approaches use semantic web to improve the capabilities and user experiences in e-learning by mean of artificial intelligence and knowledge management techniques. In this work, we develop a procedure to achieve the integration of different e-learning systems, and to give semantics to entities and relations in the database of LMSs by mean of ontologies. This integration could ease the dissemination of learning resources and knowledge from the databases of the Learning Management Systems. Moreover, the semantic interpretation of database schemes would allow to find precise information quickly.

Internet learning management systems are tools that teachers and learners are used to use since the last decade. In their early they provided features oriented only for content sharing, but they have evolved to give us a wide interaction between students and teachers, and a set of tools to ease the learning. Today most of LMSs allow us to share documents, media, forums, blogs, bookmarks, and portfolios. Recently, knowledge management tools have been used to improve e-learning activities (Lau & Tsui, 2009). The advances in Web 2.0 and XML-based technologies are changing our concept about WWW by mean of the inclusion of semantics in web documents and media. In the semantic web machines are able to talk in terms of the same concepts and to share information. This provides a higher organization in the Web and therefore a better user experience. Research regarding semantic web for e-learning has provided a wide variety of papers, but most of them converge in the use of ontologies for knowledge representation and semantic interpretation of concepts. The most common definition of ontology in computer science is the formal and explicit specification of a shared conceptualization of a domain ( Gruber, 1993). An ontology contains elements like classes, attributes, relations, and logic axioms to comprise the domain represented. A reasoner, in general terms, is a set of logic rules which may be used to infer or retrieve information about concepts or relations over the ontology, to provide new information or to validate/refuse an initial assumption. Nilsson, Palmr, and Naeve (2002) gives an overview of semantic web, the use of metadata, RDFs and ontologies for e-learning. It concludes that the good design of metadata could help in e-learning tasks like effective support for knowledge construction and access. Yli-Luoma et al. (2006) also discusses how semantic web could be used in e-learning, and describes tools that could be developed to support context, socialization, discussions and conceptual modelling. In Huang, Webster, Wood, and Ishaya (2006), it is proposed a process with four stages to improve learning personalization. In a first stage, a context-aware semantic information service is developed. At the second step information retrieval is applied for document access. Thirdly the psychological learning theory is used to control the knowledge flow in learning activities, and finally the learner personality is analyzed in order to provide a suitable self-learning. In Dietze, Gugliotta, and Domingue (2007), it is described the architecture of a service-based e-learning system using metadata for dynamic contexts. The works ( Dzbor et al., 2007 and Stutt and Motta, 2004) overview semantic web and its use for web learning, and propose a model to develop semantic services for learning web communities. Henze developed a framework for workspace personalization using RDF/S ( Lassila & Swick, 2004) and a service-oriented architecture in Henze, 2005a and Henze, 2005b. The works ( Jovanović et al., 2007 and Torniai et al., 2008) offer a system to provide teachers with feedback about the interaction between students and learning resources. Recently, the approach in Dunkel, Bruns, and Ossowski (2006) builds an ontology with the language DAML + OIL and fulfills the integration within an e-learning platform to give semantics for the contents. After that, a multi-agent architecture is applied over the e-learning system and a reasoning engine provides learners with intelligent recommendations for their tasks. Despite the efforts to apply semantic web in e-learning, a gap is found when we try to integrate and give semantics to information inside the databases of LMSs. There has been much proposals to make ontology and database matching since the 90’s. The main purpose of researchers in this area has been to give semantics to database relational models. Most of papers propose a set of heuristic rules either to do the matching or to infer an ontology that represents the database. For example, in Li, Du, and Wang (2005), the authors propose a heuristic method to build OWL ontologies from data in relational databases. In Lee and Whangbo (2007), the authors provide a method to extract an ontology from data in a database and to match the extracted ontology with a previously knows domain ontology. In Astrova, 2005 and Astrova and Stantic, 2004, a reverse engineering method is proposed to migrate data existing in relational databases to ontologies, by mean of the information obtained from web forms analysis. The work (Tijerino, Embley, Lonsdale, Ding, & Nagy, 2005) develops TANGO, a software system to study semantics among database tables using WordNet. The method proposed firstly generates small ontologies from tables, and then make semantic mappings between these ontologies with the purpose of creating a new major general/application ontology. Recently, in Sonia and Khan (2008), it is proposed a method to transform the information from a database into an ontology, in absence of tables and database metadata. To achieve this goal, the authors provide a collection of rules to infer the metadata on the fly, and then to identify class hierarchies and relations in an ontology. In Juric and Skocir (2007), the authors propose a set of rules to transform a database into an ontology. To achieve a higher standardization and semantic enrichment, the approach is supported by mean of mappings of WordNet terms into OWL concepts. The work we have found closer to our approach in the literature is explained in An, Borgida, and Mylopoulos (2005), which describes a method to map a relational database into an ontology using simple logic formulas automatically. However, the resulting mapping could suffer of ambiguity. A survey about ontologies, databases and methods for ontology and database mappings may be found in Martínez Cruz, Blanco, and Vila (2009). Our contribution focuses in this context. The goal we pursue is to fulfill the semantic integration of the information existing in the databases of different and distributed e-learning sites. The benefits of such integration could offer advantages like extended online knowledge dissemination. Moreover, the search of concrete learning material could be easily fulfilled due to the semantic interpretation of entities and relations of LMSs databases. Our approach may be resumed in two stages: • Firstly, an ontology for e-learning environments is developed. • Secondly, we make a matching between the ontology classes and properties, and relational databases of e-learning Internet systems. The data from the databases could be imported and saved as ontology class/property instances. In this step, we obtain a common framework for data sharing between different e-learning systems. Moreover, the database is given with semantics, which provides the advantages of semantic web. This article is organized as follows: Section 2 shows the main design of the ontology to model the knowledge embedded in an e-learning system. After that, Section 3 describes a model to give semantics to entities and relations in the database of a LMS. Section 4 provides a procedure to map the data from the database into ontology class instances and slots. Section 5 shows a case study over a LMS as a proof-of-concept and the implementation details. Finally, Section 6 describes the conclusions and further work.

In the last decade, there has been a huge increase in the use of learning management systems for teaching and knowledge dissemination across the Web. There have been approaches to give semantics to documents and content in this area, and to improve the users experience. However, a gap is found when we try to integrate and give semantics to information inside the databases of LMSs. In this work, we have proposed a model for the database integration of these systems using ontologies. The approach described is semi-automatic: Firstly, an expert makes the association between classes and properties in the ontology with the corresponding tables and attributes in the database. After that, an automatic procedure is applied to map the data from the database into ontology class and property instances. The advantages of our proposal encompass that the previous normal data are given with semantics, being associated with ontology concepts. Furthermore, the new storage ease the access to the content and the information sharing between the LMSs used.