Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example

Developing an ontology schema for enriching and linking digital media assets

The abundance of digital media information coming from different sources, completely redefines approaches to media content production management and distribution for all contexts (i.e. technical, business and operational). Such content includes descriptive information (i.e. metadata) about an asset (e.g. a movie, song or game), as well as playable media (e.g. audio or video files). Metadata is organised following a variety of inconsistent structures and formats that are supplied by various content providers. Some challenges have been addressed in terms of standardising and enriching media assets metadata from a semantic perspective. Well known examples include Europeana and DBpedia. Nevertheless, due to the ongoing variability and evolution of digital contents, constant support and creation of new semantic representations are necessary. This article presents an ontology schema covering the requirements of users (content providers and content consumers) involved in the overall life cycle of a digital media asset, which has been designed and developed for a real scenario. The construction of this schema has been documented and evaluated following a methodology supported by quantitative and qualitative metrics. As part of the tangible results, the following outcomes were produced: (i) an RDF/XML schema available via Zenodo and GitHub; (ii) competence questions used for validation are published at GitHub; (iii) an exemplary ontology repository; and (iv) CRUD (Create, Read, Update and Delete) technologies for managing semantic repositories based on such schema. These results form an active part of the framework of a European project and other ongoing research initiatives.This research work has been partially funded by the European Commission under the 7th Framework Programme for Research and Technological Development, the Spanish Government, the Generalitat Valenciana and the University of Alicante thought the projects: SAM(FP7-611312); SIIA(PROMETEU/2018/089); TIN2015-65136-C2-2-R; RTI2018-094653-B-C22; RTI2018-094649-B-I00; GRE16-01; and Social-Univ 2.0 (ENCARGOINTERNO5-19EN)

Leveraging cloud computing for the semantic web: review and trends

Semantic and cloud computing technologies have become vital elements for developing and deploying solutions across diverse fields in computing. While they are independent of each other, they can be integrated in diverse ways for developing solutions and this has been significantly explored in recent times. With the migration of web-based data and applications to cloud platforms and the evolution of the web itself from a social, web 2.0 to a semantic, web 3.0 comes as the convergence of both technologies. While several concepts and implementations have been provided regarding interactions between the two technologies from existing research, without an explicit classification of the modes of interaction, it can be quite challenging to articulate the interaction modes; hence, building upon them can be a very daunting task. Hence, this research identifies and describes the modes of interaction between them. Furthermore, a “cloud-driven” interaction mode which focuses on fully maximising cloud computing characteristics and benefits for driving the semantic web is described, providing an approach for evolving the semantic web and delivering automated semantic annotation on a large scale to web applications

Development and Evaluation of a Holistic, Cloud-driven and Microservices-based Architecture for Automated Semantic Annotation of Web Documents

The Semantic Web is based on the concept of representing information on the web such that computers can both understand and process them. This implies defining context for web information to give them a well-defined meaning. Semantic Annotation defines the process of adding annotation data to web information for the much-needed context. However, despite several solutions and techniques for semantic annotation, it is still faced with challenges which have hindered the growth of the semantic web. With recent significant technological innovations such as Cloud Computing, Internet of Things as well as Mobile Computing and their various integrations with semantic technologies to proffer solutions in IT, little has been done towards leveraging these technologies to address semantic annotation challenges. Hence, this research investigates leveraging cloud computing paradigm to address some semantic annotation challenges, with focus on an automated system for providing semantic annotation as a service. Firstly, considering the current disparate nature observable with most semantic annotation solutions, a holistic perspective to semantic annotation is proposed based on a set of requirements. Then, a capability assessment towards the feasibility of leveraging cloud computing is conducted which produces a Cloud Computing Capability Model for Holistic Semantic Annotation. Furthermore, an investigation into application deployment patterns in the cloud and how they relate to holistic semantic annotation was conducted. A set of determinant factors that define different patterns for application deployment in the cloud were identified and these resulted into the development of a Cloud Computing Maturity Model and the conceptualisation of a “Cloud-Driven” development methodology for holistic semantic annotation in the cloud. Some key components of the “Cloud-Driven” concept include Microservices, Operating System-Level Virtualisation and Orchestration. With the role Microservices Software Architectural Patterns play towards developing solutions that can fully maximise cloud computing benefits; CloudSea: a holistic, cloud-driven and microservices-based architecture for automated semantic annotation of web documents is proposed as a novel approach to semantic annotation. The architecture draws from the theory of “Design Patterns” in Software Engineering towards its design and development which subsequently resulted into the development of twelve Design Patterns and a Pattern Language for Holistic Semantic Annotation, based on the CloudSea architectural design. As proof-of-concept, a prototype implementation for CloudSea was developed and deployed in the cloud based on the “Cloud-Driven” methodology and a functionality evaluation was carried out on it. A comparative evaluation of the CloudSea architecture was also conducted in relation to current semantic annotation solutions; both proposed in academic literature and existing as industry solutions. In addition, to evaluate the proposed Cloud Computing Maturity Model for Holistic Semantic Annotation, an experimental evaluation of the model was conducted by developing and deploying six instances of the prototype and deploying them differently, based on the patterns described in the model. This empirical investigation was implemented by testing the instances for performance through series of API load tests and results obtained confirmed the validity of both the “Cloud-Driven” methodology and the entire model

Development and Evaluation of a Holistic, Cloud-driven and Microservices-based Architecture for Automated Semantic Annotation of Web Documents

The Semantic Web is based on the concept of representing information on the web such that computers can both understand and process them. This implies defining context for web information to give them a well-defined meaning. Semantic Annotation defines the process of adding annotation data to web information for the much-needed context. However, despite several solutions and techniques for semantic annotation, it is still faced with challenges which have hindered the growth of the semantic web. With recent significant technological innovations such as Cloud Computing, Internet of Things as well as Mobile Computing and their various integrations with semantic technologies to proffer solutions in IT, little has been done towards leveraging these technologies to address semantic annotation challenges. Hence, this research investigates leveraging cloud computing paradigm to address some semantic annotation challenges, with focus on an automated system for providing semantic annotation as a service. Firstly, considering the current disparate nature observable with most semantic annotation solutions, a holistic perspective to semantic annotation is proposed based on a set of requirements. Then, a capability assessment towards the feasibility of leveraging cloud computing is conducted which produces a Cloud Computing Capability Model for Holistic Semantic Annotation. Furthermore, an investigation into application deployment patterns in the cloud and how they relate to holistic semantic annotation was conducted. A set of determinant factors that define different patterns for application deployment in the cloud were identified and these resulted into the development of a Cloud Computing Maturity Model and the conceptualisation of a “Cloud-Driven” development methodology for holistic semantic annotation in the cloud. Some key components of the “Cloud-Driven” concept include Microservices, Operating System-Level Virtualisation and Orchestration. With the role Microservices Software Architectural Patterns play towards developing solutions that can fully maximise cloud computing benefits; CloudSea: a holistic, cloud-driven and microservices-based architecture for automated semantic annotation of web documents is proposed as a novel approach to semantic annotation. The architecture draws from the theory of “Design Patterns” in Software Engineering towards its design and development which subsequently resulted into the development of twelve Design Patterns and a Pattern Language for Holistic Semantic Annotation, based on the CloudSea architectural design. As proof-of-concept, a prototype implementation for CloudSea was developed and deployed in the cloud based on the “Cloud-Driven” methodology and a functionality evaluation was carried out on it. A comparative evaluation of the CloudSea architecture was also conducted in relation to current semantic annotation solutions; both proposed in academic literature and existing as industry solutions. In addition, to evaluate the proposed Cloud Computing Maturity Model for Holistic Semantic Annotation, an experimental evaluation of the model was conducted by developing and deploying six instances of the prototype and deploying them differently, based on the patterns described in the model. This empirical investigation was implemented by testing the instances for performance through series of API load tests and results obtained confirmed the validity of both the “Cloud-Driven” methodology and the entire model