With the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Nevertheless, because of their general-purpose approach, it is always less uncommon that obtained result sets provide a burden of useless pages. The next-generation Web architecture, represented by the Semantic Web, provides the layered architecture possibly allowing overcoming this limitation. Several search engines have been proposed, which allow increasing information retrieval accuracy by exploiting a key content of semantic Web resources, that is, relations. However, in order to rank results, most of the existing solutions need to work on the whole annotated knowledge base. In this paper, we propose a relation-based page rank algorithm to be used in conjunction with semantic Web search engines that simply relies on information that could be extracted from user queries and on annotated resources. Relevance is measured as the probability that a retrieved resource actually contains those relations whose existence was assumed by the user at the time of query definition.

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A Relation-Based Page Rank Algorithm for Semantic Web Search Engines

Semantic Web search based on ontological conjunctive queries

The recognition of action-related sounds and words activates motor regions, reflecting the semantic grounding of these symbols in action information; in addition, motor cortex exerts causal influences on sound perception and language comprehension. However, proponents of classic symbolic theories still dispute the role of modality-preferential systems such as the motor cortex in the semantic processing of meaningful stimuli. To clarify whether the motor system carries semantic processes, we investigated neurophysiological indexes of semantic relationships between action-related sounds and words. Event-related potentials revealed that action-related words produced significantly larger stimulus-evoked (Mismatch Negativity-like) and predictive brain responses (Readiness Potentials) when presented in body-part-incongruent sound contexts (e.g., “kiss” in footstep sound context; “kick” in whistle context) than in body-part-congruent contexts, a pattern reminiscent of neurophysiological correlates of semantic priming. Cortical generators of the semantic relatedness effect were localized in areas traditionally associated with semantic memory, including left inferior frontal cortex and temporal pole, and, crucially, in motor areas, where body-part congruency of action sound–word relationships was indexed by a somatotopic pattern of activation. As our results show neurophysiological manifestations of action-semantic priming in the motor cortex, they prove semantic processing in the motor system and thus in a modality-preferential system of the human brain.

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Somatotopic Semantic Priming and Prediction in the Motor System

To cope with society's demand for transparency and corruption prevention, the Brazilian Office of the Comptroller General (CGU) has carried out a number of actions, including: awareness campaigns aimed at the private sector; campaigns to educate the public; research initiatives; and regular inspections and audits of municipalities and states. Although CGU has collected information from hundreds of different sources - Revenue Agency, Federal Police, and others - the process of fusing all this data has not been efficient enough to meet the needs of CGU's decision makers. Therefore, it is natural to change the focus from data fusion to knowledge fusion. As a consequence, traditional syntactic methods must be augmented with techniques that represent and reason with the semantics of databases. However, commonly used approaches fail to deal with uncertainty, a dominant characteristic in corruption prevention. This paper presents the use of Probabilistic OWL (PR-OWL) to design and test a model that performs information fusion to detect possible frauds in procurements involving Federal money. To design this model, a recently developed tool for creating PR-OWL ontologies was used with support from PR-OWL specialists and careful guidance from a fraud detection specialist from CGU.

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Probabilistic ontology and knowledge fusion for procurement fraud detection in Brazil

The semantic Web is an extension of the World Wide Web with new technologies and standards that enable interpretation and processing of data and useful information for extraction by a computer. The World Wide Web Consortium (W3C) recommends XML, XML schema, RDF, RDF schema and Web Ontology Language (OWL) as standards and tools for the implementation of the semantic Web. Ontologies work as the main component in knowledge representation for the semantic Web. It is a data model that represents a set of concepts and the relationships between those concepts within a domain. Building an ontology starting from scratch is not an easy task since it makes heavy demands on time in addition to expert knowledge related to the domain. However, we can use the existing ontologies to develop semantic Web applications. But, there are a large number of ontologies available and the ontology search engine will generate a bulk of results with different ontologies for search queries. Therefore, ranking of ontologies is needed to find the most appropriate and relevant ontologies. We consider the ranking techniques and algorithms attached to the semantic Web: (i) Swoogle Ranking (ii) Ontokhoj Ranking (iii) OntoQA Ranking (iv) AKTiveRank (v) OntoSearch Ranking (vi) content-based ontology ranking (vii) SemSearch Ranking (viii) ReConRank. Our effort considers most popularly used ranking techniques and algorithms attached to the semantic Web. We analyze the above ontology ranking techniques with algorithms and then mainly categorize into two groups. One group is based on the semantic Web link structure and the other one is based on internal structure of the ontology. We identify that some features are not addressed in ranking of ontologies selected by the above ranking techniques and algorithms. Therefore, we propose a ranking method that considers both internal structure and semantic Web link structure of ontologies to improve the ranking of ontologies. We finally evaluate the proposed ranking method. According to the results with evaluation, we allocate more weighting for internal structure and low weighting for semantic Web link structure to get the best ranking results.

Internal Structure and Semantic Web Link Structure Based Ontology Ranking

To help human users and software agents find relevant knowledge on the Semantic Web, the Swoogle search engine discovers, indexes, and analyzes the ontologies and facts that are encoded in Semantic Web documents.

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P. Reddivari

Papers

Search on the Semantic Web