Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Creating Intelligent Linking for Information Threading in Knowledge Networks

Informledge System (ILS) is a knowledge network with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. In this paper, we aim to put forward the link dynamics involved in intelligent processing of information in ILS. There has been advancement in knowledge management field which involve managing information in databases from a single domain. ILS works with information from multiple domains stored in distributed way in the autonomous nodes termed as Knowledge Network Node (KNN). Along with the concept under consideration, KNNs store the processed information linking concepts and processors leading to the appropriate processing of information.Comment: 5 Pages, 6 Figures, 2 Tables, India Conference (INDICON), 201

Modelling decision support systems using conceptual constraints: linking process systems engineering and decision making models

This paper presents the use of a Conceptual Constraint (CC) Domain to systematize the construction of Decision Making Models (DMMs). The modelling systematics include the integration between the CC Domain and production systems as well as an identification procedure which contains some steps aimed at constraint identification using the CC Domain. The CC Domain consists of different modelling elements such as Conceptual Constraints (generic constraint types), Conceptual Components (pieces of a constraint), and Conceptual Component Elements (pieces of a conceptual component that may be connected to production systems). In this instance, the CC Domain is integrated with the Process Systems Engineering (PSE) Domain as a production system domain. The PSE Domain contains information from the multi-level functional hierarchical in an enterprise and it will be used to cover a wide range of scenarios related to hierarchical integration of DMMs. In addition, an integration step between the CC and PSE Domains is illustrated. The focus of the work is to show how these models should be developed in order to be properly integrated, and how they are used by different functionalities with an identification procedure.Postprint (author's final draft

DOOR: towards a formalization of ontology relations

In this paper, we describe our ongoing effort in describing and formalizing semantic relations that link ontolo- gies with each others on the Semantic Web in order to create an ontology, DOOR, to represent, manipulate and reason upon these relations. DOOR is a Descriptive Ontology of Ontology Relations which intends to define relations such as inclusion, versioning, similarity and agreement using ontological primitives as well as rules. Here, we provide a detailed description of the methodology used to design the DOOR ontology, as well as an overview of its content. We also describe how DOOR is used in a complete framework (called KANNEL) for detecting and managing semantic relations between ontologies in large ontology repositories. Applied in the context of a large collection of automatically crawled ontologies, DOOR and KANNEL provide a starting point for analyzing the underlying structure of the network of ontologies that is the Semantic Web

"Ontological Representation of Constraints for Geographical Reasoning"

We describe a framework that supports multiple types of constraint-based reasoning tasks on a geographic domain, by exploiting a semantic representation of the domain itself and of its constraints. Our approach is based on an abstract graph representation of a geographical area and of its relevant properties, for performing the reasoning tasks. As a test-bed, we consider the domain of Ecological Networks (ENs), which describe the structure of existing real ecosystems and help planning their expansion, conservation and improvement by introducing constraints on land use. While some previous work has been done about supporting the verification of compliance of fully specified ENs, we aim at taking a significant step further, by addressing the automatic suggestion of suitable aggregations of land patches into elements of the EN. This automated generation of EN elements is relevant to support the human planner in the design of public policies for land use because it leverages automated tools to carry out a possibly lengthy and error-prone task