Amna Basharat

An adaptive E-learning Framework to supporting new ways of teaching and learning

To address the ever increasing need and challenges associated with improving the state of web-based education, a synergistic view of E-Learning and intelligent and adaptive tutoring is adapted which is the basic essence of the learner centered Adaptive E-learning Framework presented in this paper. This framework aims to provide a complete environment of online learning. The conceptual architecture of the framework presented in this paper is centered around the following core features: (1) Domain Specific Learning Services (2) Student Capability Analysis (3) Adaptive Lecture Authoring Tool and Notification Manager (4) Intelligent Assessment Engine (5) User Friendly E-Learning Portal. Altogether, this framework is truly aimed to be an integrated platform consistent with the emerging needs of E-learning and Distance Education.

Semantic Web Based Integrated Agriculture Information Framework

Information Technology in agriculture is intended to provide knowledge to the farmers in order to make more informed and valuable decisions. In this paper we present an Integrated Agriculture Information Framework (IAIF) to enable knowledge extraction from multiple domain related repositories. IAIF has been designed and developed with the core aim of meaningfully representing combining, merging and aggregating the data present in existing knowledge repositories through the use of metadata and domain ontologies. The domain ontology serves to link the resources with the domain knowledge with the help of respective metadata. A key feature of the semantics based approach adopted is that ontology also serves as the basis for capturing the needed information for accessing different types of data sources. We also present an Ontology engineering methodology for the domain that allows for formalized modeling of domain schema and resource knowledge. In addition, we discuss the methodology for linking multiple types of knowledge resources.

ERMOS: An Efficient Relational Mapping for Ontology Storage

Ontology has been emerged has rapidly growing technology for representing knowledge in a meaningful way. To store ontologies for web-applications XML is natural representation but it is unable to store huge knowledge in a saleable way. under the primary objective of our paper, we present ERMOS: An Efficient Relational Mapping for Ontology Storage, by extending Schema Aware Mapping for storing ontologies in RDBMS in order to enable easy and prompt retrieval of knowledge from ontologies for semantically affluent queries based on OWL 2.0 semantics in accompany with REMT: Restriction Mapping Technique for storing OWL 2.0 restrictions. Intended for this intention we introduce a set of rules, which describes our mapping technique for developing an Entity Relation (ER) Model with the endeavor of eventually facilitating towards a Relational Database Design. ERMOS offers main memory free in-system reasoning over A-Box data.

Semantic Agent Oriented Architecture for Researcher Profiling and Association (SemoRA)

Modern research is becoming increasingly distributed and networked. In this context collaboration among researchers is essential. This work develops an architectural solution aimed at providing a knowledge-based collaborative framework especially targeted for the research domain. The proposed architecture, called Semantic Agent-Oriented Architecture for Researcher Profiling and Association (SemoRA), is based on a multi-agent paradigm and adopts Semantic Web technologies for knowledge representation and retrieval. Through the combined use of agents, ontologies and logical inferencing, SemoRA provides an efficient and robust means of facilitating the development of distributed research networks.

Multi-agent collaboration based on enhanced cognitive awareness: an architecture for agents' profiling on the semantic web

This paper applies cognitive models, inspired by cognitive science, with the aim to propose architectural and knowledge-based requirements to structure ontological models for the cognitive profiling of agents. The proposed architecture aims to address the lack of flexibility that most agent-based collaborations are affected by. The resulting agents, equipped with advanced cognitive profiling, have an increased cognitive awareness of themselves and are more capable of interacting with other agents in a multi-agents based environment. In this research, cognitive awareness identifies the ability of the web agents to diagnose their processing limitations and to establish interactions with the external environment. The outcome is the enhanced flexibility, reusability and predictability of the agent behaviour; thus contributing towards minimizing human cognitive demands. The concept of cognitive profiling presented in this paper considers the semantic web as an action mediating space, where ontological models provide affordances for improving cognitive awareness through shared knowledge-base. The conceptual model for the cognitive profile architecture is developed with Protege Ontology editor to generate OWL Ontology and evaluated through a proof of concept. The results show that agents equipped with cognitive awareness can undertake complex tasks more dynamically.

An Agent-Oriented Architecture for Researcher Profiling and Association Using Semantic Web Technologies

Collaboration within the international scientific community has steadily increased over the years especially in the presence of complex interdisciplinary problems being investigated. At the same time the amount of research artifacts produced by the research community has grown exponentially making it difficult for individual researchers to filter and search through such information. In the presence of a vast amount of research information the problem of identifying potential project partners or collaborators with specific profiles can become extremely difficult. This paper presents a semantic multi-agent architecture (called SemoRA) aimed at tackling such a problem. The architecture combines agent and Semantic Web technologies in order to develop a framework capable of efficiently acquiring researcher information, making sense of it and giving meaning to it. The architecture ultimately enables the retrieval and matching of scored profiles aimed at enhancing collaborations among researchers – collaborations that can transcend both institutional and national boundaries.

4th Generation Flexible Query Language for Ontology Querying

Ontology has emerged as one of the rapidly growing method for meaningful knowledge representation. Realizing the potential of Semantic Web, Web Ontology Language (OWL) has provided a standard notation for creating Ontologies, while SWRL has provided the notation for defining rules to build new knowledge bases for the web applications. However, need for an efficient query Language for Ontologies is still unaddressed. In this paper we present ROQL, a Rule-based Ontology Query Language for creation and querying Ontologies based on the semantics of OWL 2.0 and syntax of SQL and SPARQL. With ROQL we provide abstract syntax to work with Ontologies. Compared with off-the-shelf query languages, we offer the support for creating, updating, and selecting knowledge from Ontology based knowledge bases with considerably more efficiently and flexibly. In addition to the clear and easy syntax, ROQL provides the possibility for defining rules in queries while selecting and inferring knowledge.