Knowledge Sharing is a crucial issue in the Semantic Web: SW services expose and share knowledge content (expressed through ontologies and related knowledge bases) arising from distinct languages, locales, and personal perspectives; in this scenario, semantic alignment approaches play a pivotal role, providing viable solutions for integrating heterogeneous resources, still maintaining their local independence. We focus here on a 3-step approach to ontology mapping, which is strongly based on the exploitation of (monolingual and multilingual) linguistic resources for content publishing and discovery, and on a human intervention for supervising the process and assessing semantic links between mapped resources. Our methodology is also being supported by the development of dedicated tools for accompanying knowledge engineers and users across the different steps of creating and integrating ontology resources.

Abstract. In this paper we present an analysis and modeling case study for agent mediated knowledge management in educational environments: Help&Learn, an agent-based peer-to-peer helpdesk system to support extraclass interactions among students and teachers. Help&Learn expands the student’s possibility of solving problems, getting involved in a cooperative learning experience that transcends the limits of classrooms. To model Help&Learn, we have used Agent-Object-Relationship Modeling Language (AORML), an UML extension for agent-oriented modeling. The aim of this research is two-fold. On the one hand, we aim at exploring Help&Learn’s potential to support collaborative learning, discussing its knowledge management strategy. On the other hand, we aim at showing the expressive power and the modeling strengths of AORML. 1

Abstract. Changes in the characterization of instances in digital content are one of the rationales to evolve ontologies that support a domain. These changes can have impacts on one or more of interrelated ontologies. Before implementing changes, their impact on the target ontology, other dependent ontologies or dependent systems should be analysed. We investigate three concerns for the determination of impacts of changes in ontologies: representation of changes to ensure minimum impact, impact determination and integrity determination. Key elements of our solution are the operationalization of changes to minimize impacts, a parameterization approach for the determination of impacts, a categorization scheme for identified impacts, and prioritization technique for change operations based on the severity of impacts.

Service matching approaches trade precision for recall, creating the need for users to choose the correct services, which obviously is a major obstacle for automating the service discovery and aggregation processes. Our approach to overcome this problem, is to eliminate the appearance of false positives by returning only the correct services. As different users have different semantics for what is correct, we argue that the correctness of the matching results must be determined according to the achievement of users ’ goals: that only services achieving users ’ goals are considered correct. To determine such correctness, we argue that the matching process should be based primarily on the high-level functional specifications (namely goals, achievement contexts, and external behaviors). In this article, we propose models, data structures, algorithms, and theorems required to correctly match such specifications. We propose a model called G +, to capture such specifications, for both services and users, in a machine-understandable format. We propose a data structure, called a Concepts Substitutability Graph (CSG), to capture the substitution semantics of application domain concepts in a context-based manner, in order to determine the semantic-preserving mapping transformations required to match different G + models. We also propose a behavior matching approach that is able to match states in an m-to-n manner, such that behavior models with different numbers

Abstract: Information spreads rapidly across Web sites, Web logs and online forums. This paper describes the research framework of the IDIOM Project (Information Diffusion across Interactive Online Media), 1 which analyzes this process by identifying redundant content elements, mapping them to an ontological knowledge structure, and tracking their temporal and geographic distribution. Linguists define "idiom " as an expression whose meaning is different from the literal meanings of its component words. Similarly, the study of information diffusion promises insights that cannot be inferred from individual network elements. This paper presents underlying technology, initial results, and the future roadmap of investigating information diffusion based on ontological knowledge structures. Similar projects often focus on particular media, or neglect important aspects of the human language. This paper addresses these gaps to reveal fundamental mechanisms of information diffusion across media with distinct interactive characteristics.

In the case of tourism applications, it is particularly evident that geography is emerging as a fundamental principle for structuring Web resources. Recent improvements in semantic and geographic Web technology, often referred to as the Geospatial Web, acknowledge the relevance of adding location metadata to existing databases and accessing the vast amounts of information stored in these databases via geospatial services. This paper outlines the acquisition of geospatial context information, describes usage scenarios and real-world applications in the tourism industry, and presents an automated software tool for annotating large collections of Web documents automatically. The quality of this tool is tested based upon Web pages from the Austrian National Tourism Organization. Initial results are encouraging and help define a roadmap for fiirther improving the automated tagging of tourism resources.

With the advent of new web technology, Image Annotation and Classification has paved the way for invoking an efficient and effective research area as it is of immense importance in searching images from different categories of relevant images using keywords. This may be an impressive tool in describing image content as object or textual information to classify images. To serve this purpose, many techniques have been lunched for automatic image annotation and classification based on content and exit metadata. Automatic image annotation however, is highly difficult and challengeable. So users have to follow the annotation manually. In this paper, we applied fuzzy logic implication and fuzzy set operation for Historical image classification. We have compared the outcome of how fuzzy classification is better ontological image classification. Fuzzy logic plays an important rule so that the margin of the classification becomes more accurate. Here we imposed fuzzy matrix optimization for Spatial Image classification of Historical image data. Fuzzy matrix determines the optimal values of spatial data which are near about correct with less uncertainty. Fuzzy membership function also works to estimate the values before using in fuzzy matrix. We also pro- posed a manual method for image annotation based on IPTC metadata with a view to retrieving images with its corresponding information for automatic semantic ontological and fuzzy classification using linked data. We strived to experiment on about 400 images of different historical heritages.