Building a wordnet for Turkish

This paper summarizes the development process of a wordnet for Turkish as part of the Balkanet project. After discussing the basic method-ological issues that had to be resolved during the course of the project, the paper presents the basic steps of the construction process in chronological order. Two applications using Turkish wordnet are summarized and links to resources for wordnet builders are provided at the end of the paper

An Ontology-Based Method for Semantic Integration of Business Components

Building new business information systems from reusable components is today an approach widely adopted and used. Using this approach in analysis and design phases presents a great interest and requires the use of a particular class of components called Business Components (BC). Business Components are today developed by several manufacturers and are available in many repositories. However, reusing and integrating them in a new Information System requires detection and resolution of semantic conflicts. Moreover, most of integration and semantic conflict resolution systems rely on ontology alignment methods based on domain ontology. This work is positioned at the intersection of two research areas: Integration of reusable Business Components and alignment of ontologies for semantic conflict resolution. Our contribution concerns both the proposal of a BC integration solution based on ontologies alignment and a method for enriching the domain ontology used as a support for alignment.Comment: IEEE New Technologies of Distributed Systems (NOTERE), 2011 11th Annual International Conference; ISSN: 2162-1896 Print ISBN: 978-1-4577-0729-2 INSPEC Accession Number: 12122775 201

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

Using background knowledge for ontology evolution

One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery. We perform a relation discovery experiment focusing on the use of WordNet and Semantic Web ontologies as sources of background knowledge. We back our experiment with a thorough analysis that highlights various issues on how to improve and validate relation discovery in the future, which will directly improve the task of automatically performing ontology changes during evolution

Dynamic Discovery of Type Classes and Relations in Semantic Web Data

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method