STELLAR (Semantic Technologies Enhancing the Lifecycle of Learning Resources): Jisc Final Report

[Project Summary] As one of the earliest distance learning providers The Open University (OU) has a rich heritage of archived learning materials. An ever increasing amount of that is in digital form and is being deposited with the University Archive. This growth has been driven by digitisation activity from projects such as AVA (Access to Video Assets) and the Fedora-based Open University Digital Library ‘a place to discover digital and digitised archival content from the OU Library, from videos and images to digitised documents’. Other digital content is being captured from web archiving activities, such as work to preserve Moodle Virtual Learning Environment course websites. An evidence based understanding is required to inform digital preservation policies, curation strategy and investment in digital library development. Following the Pre-enhancement, Enhancement and Post-enhancement methodology set out by Jisc, STELLAR adopted the model of a balanced scorecard to ascertain the value ascribed to the non-current learning materials. Four aspects were considered: Personal and professional perspectives of value; Value to the Higher Educational and academic communities; Value to internal processes and cultures; Financial perspectives of value. The outcomes of the survey indicated that stakeholders place a high value on the materials, and that they perceived them to have value in all areas evaluated. Three OU courses were chosen from the digital library for the transformation stage. These materials were enhanced and transformed into RDF, a process that required more extensive metadata expertise and effort than was expected. Following enhancement the RDF was accessed through a tool called DiscOU, created by a member of the project team from the OU’s Knowledge Media Institute. DiscOU uses both linked data and a semantic meaning engine to analyse the meaning of the text in a search query. This is matched against the meaning of the content derived from an index of the full-text of the digital library content. In the final stage stakeholders were asked through a survey and series of workshops to use the DiscOU proof-of-concept tool to assess their perception of the value of this transformation. This has revealed that overall, academics and other stakeholders in the university do believe that the value of the selected materials was positively impacted by the application of semantic technologies

Integrating semantic web in knowledge based institutions of higher education: issues, potentials and challenges / Sharin Sulaiman, Azlina Bujang and Azlina Narawi

Based on Rowley (2000) he states that universities and their staff must recognize and respond to their changing role in a knowledge-based society. Universities do have a significant level of knowledge management (KM) activities, and it is important to recognize these, and use them as foundations for further development. Both public and private universities play important roles in providing easy access to knowledge especially to their students. University knowledge needs differ from corporate needs where universities seek to share scholarly knowledge for the good of society whereas corporation seeks profit However, it is also important to note that universities have started to manage knowledge as intellectual property to be sold or bartered, as well as given away (Kennedy, 1998). Realizing the benefits that KM can offers, more learning institutions have attempts to incorporate KM practices in these four main areas: learning and teaching, scholarly research, academic publishing, and libraries. However, these learning institutions face issues and limitations in practicing KM concept that need to be encountered. Among the predicaments of practicing KM concept is on an organizational knowledge management systems (KMS)still remains underutilized and hardly recognizable by knowledge workers [Davenport, 2005; Maier, 2007, McAfee, 2006]. At the same time, knowledge workers are lack in number and increasingly need appropriate Information Technology (IT) solutions facilitating their daily work [McAfee, 2006]. Therefore, in order to overcome these KM issues, it is recommended for Knowledge based institution of higher education to integrate semantic web as a way to improve the effectiveness of KM practices. In short, Web semantic is an extension of the current web in which information is given well defined meaning, better enabling computers and people to work in co-operation (Bernes-Lee,, Handier,, Lassila,. (2001). There are a number of Web-based services and applicatiohs that demonstrate the foundations of Web 2.0 concept, and they are already being used to a certain extent in the field of education. These are not really technologies per se but services (or user processes) built using the building blocks of the technologies and open standards that underpin the Internet and the Web. These include blogs, wikis, and multimedia sharing services, content syndication, pddcasting and content tagging services. Unfortunately, there are challenges exists in integrating semantic web in the institution of higher learning which relate to knowledge assets management including the availability of content, ontology availability, development and evolution, scalability, multilingualism, visualization and stability of Semantic Web languages

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

A structured model metametadata technique to enhance semantic searching in metadata repository

This paper discusses on a novel technique for semantic searching and retrieval of information about learning materials. A novel structured metametadata model has been created to provide the foundation for a semantic search engine to extract, match and map queries to retrieve relevant results. Metametadata encapsulate metadata instances by using the properties and attributes provided by ontologies rather than describing learning objects. The use of ontological views assists the pedagogical content of metadata extracted from learning objects by using the control vocabularies as identified from the metametadata taxonomy. The use of metametadata (based on the metametadata taxonomy) supported by the ontologies have contributed towards a novel semantic searching mechanism. This research has presented a metametadata model for identifying semantics and describing learning objects in finer-grain detail that allows for intelligent and smart retrieval by automated search and retrieval software

Collaborative Creation of Teaching-Learning Sequences and an Atlas of Knowledge

The article is about a new online resource, a collaborative portal for teachers, which publishes a network of prerequisites for teaching/learning any concept or an activity. A simple and effective method of collaboratively constructing teaching­-learning sequences is presented. The special emergent properties of the dependency network and their didactic and epistemic implications are pointed. The article ends with an appeal to the global teaching community to contribute prerequisites of any subject to complete the global roadmap for an altas being built on similar lines as Wikipedia. The portal is launched and waiting for community participation at http://www.gnowledge.org.\u

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

Towards the Automatic Classification of Documents in User-generated Classifications

There is a huge amount of information scattered on the World Wide Web. As the information flow occurs at a high speed in the WWW, there is a need to organize it in the right manner so that a user can access it very easily. Previously the organization of information was generally done manually, by matching the document contents to some pre-defined categories. There are two approaches for this text-based categorization: manual and automatic. In the manual approach, a human expert performs the classification task, and in the second case supervised classifiers are used to automatically classify resources. In a supervised classification, manual interaction is required to create some training data before the automatic classification task takes place. In our new approach, we intend to propose automatic classification of documents through semantic keywords and building the formulas generation by these keywords. Thus we can reduce this human participation by combining the knowledge of a given classification and the knowledge extracted from the data. The main focus of this PhD thesis, supervised by Prof. Fausto Giunchiglia, is the automatic classification of documents into user-generated classifications. The key benefits foreseen from this automatic document classification is not only related to search engines, but also to many other fields like, document organization, text filtering, semantic index managing

A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements