Fouad Zablith

Laying the foundations for a World Wide Argument Web

This paper lays theoretical and software foundations for a World Wide Argument Web (WWAW): a large-scale Web of inter-connected arguments posted by individuals to express their opinions in a structured manner. First, we extend the recently proposed Argument Interchange Format (AIF) to express arguments with a structure based on Waltons theory of argumentation schemes. Then, we describe an implementation of this ontology using the RDF Schema Semantic Web-based ontology language, and demonstrate how our ontology enables the representation of networks of arguments on the Semantic Web. Finally, we present a pilot Semantic Web-based system, ArgDF, through which users can create arguments using different argumentation schemes and can query arguments using a Semantic Web query language. Manipulation of existing arguments is also handled in ArgDF: users can attack or support parts of existing arguments, or use existing parts of an argument in the creation of new arguments. ArgDF also enables users to create new argumentation schemes. As such, ArgDF is an open platform not only for representing arguments, but also for building interlinked and dynamic argument networks on the Semantic Web. This initial public-domain tool is intended to seed a variety of future applications for authoring, linking, navigating, searching, and evaluating arguments on the Web.

Ontology evolution: a process-centric survey

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.

A novel approach to visualizing and navigating ontologies

There is empirical evidence that the user interaction metaphors used in ontology engineering toolkits are largely inadequate and that novel interactive frameworks for human ontology interaction are needed. Here we present a novel tool for visualizing and navigating ontologies, called KC Viz, which exploits an innovative ontology summarization method to support a ’middleout ontology browsing’ approach, where it becomes possible to navigate ontologies starting from the most information-rich nodes (i.e., key concepts). This approach is similar to map-based visualization and navigation in Geographical Information Systems, where, e.g., major cities are displayed more prominently than others, depending on the current level of granularity.

Evolva: A Comprehensive Approach to Ontology Evolution

Ontology evolution is increasingly gaining momentum in the area of Semantic Web research. Current approaches target the evolution in terms of either content, or change management, without covering both aspects in the same framework. Moreover, they are slowed down as they heavily rely on user input. We tackle the aforementioned issues by proposing Evolva, a comprehensive ontology evolution framework, which handles a complete ontology evolution cycle, and makes use of background knowledge for decreasing user input.

Ontology Evolution with Evolva

Ontology evolution is a painstaking and time-consuming process, especially in information rich and dynamic domains. While ontology evolution refers both to the adaptation of ontologies (e.g., through additions or updates possibly discovered from external data sources) and the management of these changes, no existing tools offer both functionalities. The Evolva framework aims to be a blueprint for a comprehensive ontology evolution tool that would cover both tasks. Additionally, Evolva proposes the use of background knowledge sources to reduce user involvement in the ontology adaptation step. This demo focuses on the initial, concrete implementation of our framework.

The OU linked open data: production and consumption

The aim of this paper is to introduce the current efforts toward the release and exploitation of The Open Universitys (OU) Linked Open Data (LOD). We introduce the work that has been done within the LUCERO project in order to select, extract and structure subsets of information contained within the OU data sources and migrate and expose this information as part of the LOD cloud. To show the potential of such exposure we also introduce three different prototypes that exploit this new educational resource: (1) the OU expert search system, a tool focused on finding the best experts for a certain topic within the OU staff; (2) the Social Study system, a tool that relies on Facebook information to identify common interest between a users profile and recommend potential courses within the OU; and (3) Linked OpenLearn, an application that enables exploring linked courses, Podcasts and tags to OpenLearn units. Its aim is to enhance the browsing experience for students, by detecting relevant educational resources on the fly while studying an OpenLearn unit.

Production and consumption of university Linked Data

Linked Data increases the value of an organisations data over the web by introducing explicit and machine processable links at the data level. We have adopted this new stream of data representation to produce and expose existing data within The Open University (OU) as Linked Data. We present in this paper our approach for producing the data, based on well-defined workflows at the organisation as well as the technical levels. We also discuss the data already available to consume, and show the potential improvements that Linked Data brings by presenting three applications: (1) the OU Expert Search system for finding experts at the OU based on a specified topic of interest, (2) the Social Study application to identify potential courses for students based on their Facebook profile information, and (3) the Linked OpenLearn application that helps students identify related media and courses to OpenLearn units at the OU. Before concluding the paper, we show the potential benefits and an approach towards interlinking data beyond The Open University with other universities, using a common categorisation scheme.

Using ontological contexts to assess the relevance of statements in ontology evolution

Ontology evolution tools often propose new ontological changes in the form of statements. While different methods exist to check the quality of such statements to be added to the ontology (e.g., in terms of consistency and impact), their relevance is usually left to the user to assess. Relevance in this context is a notion of how well the statement fits in the target ontology. We present an approach to automatically assess such relevance. It is acknowledged in cognitive science and other research areas that a piece of information flowing between two entities is relevant if there is an agreement on the context used between the entities. In our approach, we derive the context of a statement from online ontologies in which it is used, and study how this context matches with the target ontology. We identify relevance patterns that give an indication of relevance when the statement context and the target ontology fulfill specific conditions. We validate our approach through an experiment in three different domains, and show how our pattern-based technique outperforms a naive overlap-based approach.

Interconnecting and Enriching Higher Education Programs using Linked Data

Online environments are increasingly used as platforms to support and enhance learning experiences. In higher education, students enroll in programs that are usually formed of a set of courses and modules. Such courses are designed to cover a set of concepts and achieve specific learning objectives that count towards the related degree. However we observe that connections among courses and the way they conceptually interlink are hard to exploit. This is normal as courses are traditionally described using text in the form of documents such as syllabi and course catalogs. We believe that linked data can be used to create a conceptual layer around higher education programs to interlink courses in a granular and reusable manner. We present in this paper our work on creating a semantic linked data layer to conceptually connect courses taught in a higher education program. We highlight the linked data model we created to be collaboratively extended by course instructors and students using a semantic Mediawiki platform. We also present two applications that we built on top of the data to (1) showcase how learning material can now float around courses through their interlinked concepts in eLearning environments (we use moodle as a proof of concept); and (2) to support the process of higher education program reviews.

wayOU: linked data-based social location tracking in a large, distributed organisation

While the publication of linked open data has gained momentum in large organisations, the way for users of these organisations to engage with these data is still unclear. Here, we demonstrate a mobile application called wayOU (where are you at the Open University) which relies on the data published by The Open University (under data.open.ac.uk) to provide social, location-based services to its students and members of staff. An interesting aspect of this application is that, not only it consumes linked data produced by the University from various repositories, but it also contributes to it by creating new connections between people, places and other types of resources.