Semantic web technology for web-based teaching and learning: A roadmap

The World-Wide Web has become the predominant platform for computer-aided instruction. Contentorientation, access and interactive features have made the Web a successful technology. The Web, however, is still evolving. We expect in particular Semantic Web technology to substantially impact Web-based teaching and learning. In this paper, we examine the potential of this technology and how we expect it to influence content representation and the work of the instructor and the learner

Modelling the Semantic Web using a Type System

We present an approach for modeling the Semantic Web as a type system. By using a type system, we can use symbolic representation for representing linked data. Objects with only data properties and references to external resources are represented as terms in the type system. Triples are represented symbolically using type constructors as the predicates. In our type system, we allow users to add analytics that utilize machine learning or knowledge discovery to perform inductive reasoning over data. These analytics can be used by the inference engine when performing reasoning to answer a query. Furthermore, our type system defines a means to resolve semantic heterogeneity on-the-fly

Applications of semantic web technology to support learning content development

The Semantic Web is based on ontology technology – a knowledge representation framework – at its core to make meaning explicit and more accessible to automatic processing. We discuss the potential of this technology for the development of content for learning technology systems. We survey seven application types demonstrating different forms of applications of ontologies and the Semantic Web in the development of learning technology systems. Ontology technologies can assist developers, instructors, and learners to organise, personalise, and publish learning content and to discover, generate, and compose learning content. A conceptual content development and deployment architecture allows us to distinguish and locate the different applications and to dis-cuss and assess the potential of the underlying technologies

Revising Knowledge Discovery for Object Representation with Spatio-Semantic Feature Integration

In large social networks, web objects become increasingly popular. Multimedia object classification and representation is a necessary step of multimedia information retrieval. Indexing and organizing these web objects for the purpose of convenient browsing and search of the objects, and to effectively reveal interesting patterns from the objects. For all these tasks, classifying the web objects into manipulable semantic categories is an essential procedure. One important issue for classification of objects is the representation of images. To perform supervised classification tasks, the knowledge is extracted from unlabeled objects through unsupervised learning. In order to represent the images in a more meaningful and effective way rather than using the basic Bag-of-words (BoW) model, a novel image representation model called Bag-of-visual phrases(BoP) is used. In this model visual words are obtained using hierarchical clustering and visual phrases are generated by vector classifier of visual words. To obtain the Spatio-semantic correlation knowledge the frequently co-occurring pairs are calculated from visual vocabulary. After the successful object representation, the tags, comments, and descriptions of web objects are separated by using most likelihood method. The spatial and semantic differentiation power of image features can be enhanced via this BoP model and likelihood method. DOI: 10.17762/ijritcc2321-8169.15065

Improving Document Representation Using Retrofitting

Data-driven learning of document vectors that capture linkage between them is of immense importance in natural language processing (NLP). These document vectors can, in turn, be used for tasks like information retrieval, document classification, and clustering. Inherently, documents are linked together in the form of links or citations in case of web pages or academic papers respectively. Methods like PV-DM or PV-DBOW try to capture the semantic representation of the document using only the text information. These methods ignore the network information altogether while learning the representation. Similarly, methods developed for network representation learning like node2vec or DeepWalk, capture the linkage information between the documents but they ignore the text information altogether. In this thesis, we proposed a method based on Retrofit for learning word embeddings using a semantic lexicon, which tries to incorporate both the text and network information together while learning the document representation. We also analyze the optimum weight for adding network information that will give us the best embedding. Our experimentation result shows that our method improves the classification score by 4% and we also introduce a new dataset containing both network and content information