Language technologies and the evolution of the semantic web

The availability of huge amounts of semantic markup on the Web promises to enable a quantum leap in the level of support available to Web users for locating, aggregating, sharing, interpreting and customizing information. While we cannot claim that a large scale Semantic Web already exists, a number of applications have been produced, which generate and exploit semantic markup, to provide advanced search and querying functionalities, and to allow the visualization and management of heterogeneous, distributed data. While these tools provide evidence of the feasibility and tremendous potential value of the enterprise, they all suffer from major limitations, to do primarily with the limited degree of scale and heterogeneity of the semantic data they use. Nevertheless, we argue that we are at a key point in the brief history of the Semantic Web and that the very latest demonstrators already give us a glimpse of what future applications will look like. In this paper, we describe the already visible effects of these changes by analyzing the evolution of Semantic Web tools from smart databases towards applications that harness collective intelligence. We also point out that language technology plays an important role in making this evolution sustainable and we highlight the need for improved support, especially in the area of large-scale linguistic resources

Validation and Evaluation

In this technical report, we present prototypical implementations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge, developed according to the working plan outlined in Technical Report TR-B-12-04. The prototypes complete the next milestone on the path to an integral Corporate Semantic Web architecture based on the three pillars Corporate Ontology Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search, as envisioned in TR-B-08-09

Distributed Semantic Web data management in HBase and MySQL cluster

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C\u27s Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management

Expert knowledge management based on ontology in a digital library

The architecture of the future Digital Libraries should be able to allow any users to access available knowledge resources from anywhere and at any time and efficient manner. Moreover to the individual user, there is a great deal of useless information in addition to the substantial amount of useful information. The goal is to investigate how to best combine Artificial Intelligent and Semantic Web technologies for semantic searching across largely distributed and heterogeneous digital libraries. The Artificial Intelligent and Semantic Web have provided both new possibilities and challenges to automatic information processing in search engine process. The major research tasks involved are to apply appropriate infrastructure for specific digital library system construction, to enrich metadata records with ontologies and enable semantic searching upon such intelligent system infrastructure. We study improving the efficiency of search methods to search a distributed data space like a Digital Library. This paper outlines the development of a CaseBased Reasoning prototype system based in an ontology for retrieval information of the Digital Library University of Seville. The results demonstrate that the used of expert system and the ontology into the retrieval process, the effectiveness of the information retrieval is enhanced

Distributed Semantic Web Data Management in HBase and MySQL Cluster

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C's Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management.Comment: In Proc. of the 4th IEEE International Conference on Cloud Computing (CLOUD'11

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

The Semantic Portal for Supporting Research Community: a Review

Current state of the art of typical search engines like Google, Yahoo and others are delivering references in terms of web URL or links to the related website. As such the results did not deliver the right answers required to the users needs. In addition to that as soon as the users require a collection of the information obtained, these search engines failed to do so resulting in the human intervention in-combining the information from several sources. Due ot the advancement and the vast number of sites and information on the web, demands in providing higher precision results are required to aid users in obtaining the most relevant result to the search process. One of the promising areas of the Semantic Web is enhancing the query capabilities for information. Small vertical vocabularies and ontologies have emerged, and the community of people using these and generating data is growing daily. However queries or search mechanisms that utilizea the vasrt amount of vocabularies, ontologies and data in digital libraries is still very much lacking. Therefore searching over heteregoneous records, data in digital library community or the Web has become a well known problem to the mass public. As such a solution is needed for a federated search across multiple resources available. However it remains unclear on how Semantic Web or its technology is used in constructing a digital library system or aid in enhancing the quality of the search results performed. This leads to the current work proposed, as work will be conducted to provide possible components that will construct the semantic web portal. The work performed is essential to facilitate semantic searches for research community in large-scale distributed digital library system. The subject research community is chosen particularly to aid in ensuring hat result obtained are accordingly to the users relevant needs. The expected outcomes of the research are an architecture that utilizes the semantic technology that will promote semantic web portal in the digital library and a semantic search mechanism that will provide better results and a combination of useful results relevant to the users

A distributed software environment for collaborative web computing

Poster in the proceedingsThis paper describes an extensible core software element of a distributed, peer-to-peer system, which provides several facilities in order to help the implementation of collaborative, Web-based, distributed information storing and retrieval applications based on a decentralized P2P model. Moreover, after an architectural introduction of the core distributed software module, the Core Node, this paper describes a real application, named DART Node, based on it and designed and implemented within the DART (Distributed Agent-based Retrieval Tools) project, which carries out the idea of the design and implementation of a distributed, semantic and collaborative Web search engine, including mobile devices integration use cases.