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

LODE: Linking Digital Humanities Content to the Web of Data

Numerous digital humanities projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of the LinkedHumanities project is to build digital humanities tools that work "out of the box," enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With this paper, we report on the Linked Open Data Enhancer (LODE) framework developed as part of the LinkedHumanities project. With LODE we support non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. LODE links and enhances the local RDF repository without compromising the quality of the data. In particular, LODE supports the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high-quality linking candidates using tailored matching algorithms. We hope that the LODE framework will be useful to digital humanities scholars complementing other digital humanities tools

Intelligent Personalized Searching

Search engine is a very useful tool for almost everyone nowadays. People use search engine for the purpose of searching about their personal finance, restaurants, electronic products, and travel information, to name a few. As helpful as search engines are in terms of providing information, they can also manipulate people behaviors because most people trust online information without a doubt. Furthermore, ordinary users usually only pay attention the highest-ranking pages from the search results. Knowing this predictable user behavior, search engine providers such as Google and Yahoo take advantage and use it as a tool for them to generate profit. Search engine providers are enterprise companies with the goal to generate profit, and an easy way for them to do so is by ranking up particular web pages to promote the product or services of their own or their paid customers. The results from search engine could be misleading. The goal of this project is to filter the bias from search results and provide best matches on behalf of users’ interest

Multiple terminologies : an obstacle to information retrieval

An issue currently at the forefront of digital library research is the prevalence of disparate terminologies and the associated limitations imposed on user searching. It is thought that semantic interoperability is achievable by improving the compatibility between terminologies and classification schemes, enabling users to search multiple resources simultaneously and improve retrieval effectiveness through the use of associated terms drawn from several schemes. This article considers the terminology issue before outlining various proposed methods of tackling it, with a particular focus on terminology mapping

Ontology ranking based on the analysis of concept structures

In view of the need to provide tools to facilitate the reuse of existing knowledge structures such as ontologies, we present in this paper a system, AKTiveRank, for the ranking of ontologies. AKTiveRank uses as input the search terms provided by a knowledge engineer and, using the output of an ontology search engine, ranks the ontologies. We apply a number of classical metrics in an attempt to investigate their appropriateness for ranking ontologies, and compare the results with a questionnaire-based human study. Our results show that AKTiveRank will have great utility although there is potential for improvement