Existing service matching approaches trade precision for recall, creating the need for humans to choose the correct services, which is a major obstacle for automating the service matching and the service aggregation processes. To overcome this problem, the matchmaker must automatically determine the correctness of the matching results according to the defined users' goals. That is, only service(s)-achieving users' goals are considered correct. This requires the high-level functional semantics of services, users, and application domains to be captured in a machine-understandable format. Also this requires the matchmaker to determine the achievement of users' goals without invoking the services. We propose the G+ model to capture the high-level functional specifications of services and users (namely goals, achievement contexts and external behaviors) providing the basis for automated goal achievement determination; also we propose the concepts substitutability graph to capture the application domains' semantics. To avoid the false negatives resulting from adopting existing constraint and behavior matching approaches during service matching, we also propose new constraint and behavior matching approaches to match constraints with different scopes, and behavior models with different number of state transitions. Finally, we propose two correctness-aware matching approaches (direct and aggregate) that semantically match and aggregate semantic web services according to their G+ models, providing the required theoretical proofs and the corresponding verifying simulation experiments.

The task of tagging, annotating or labelling image content automatically with semantic keywords is a challenging problem. To automatically tag images semantically based on the objects that they contain is essential for image retrieval. In addressing these problems, we explore the techniques developed to combine textual description of images with visual features, automatic region tagging and region-based ontology image retrieval. To evaluate the techniques, we use three corpora comprising: Lonely Planet travel guide articles with images, Wikipedia articles with images and Goats comic strips. In searching for similar images or textual information specified in a query, we explore the unification of textual descriptions and visual features (such as colour and texture) of the images. We compare the effectiveness of using different retrieval similarity measures for the textual component. We also analyse the effectiveness of different visual features extracted from the images. We then investigate the best weight combination of using textual and visual features. Using the queries from the Multimedia Track of INEX 2005 and 2006, we found that the best weight combination significantly improves the effectiveness of the retrieval system. Our findings suggest that image regions are better in capturing the semantics, since we can identify specific regions of interest in an image. In this context, we develop a technique to tag image regions with high-level semantics. This is done by combining several shape feature descriptors and colour, using an equal-weight linear combination. We experimentally compare this technique with more complex machine-learning algorithms, and show that the equal-weight linear combination of shape features is simpler and at least as effective as using a machine learning algorithm. We focus on the synergy between ontology and image annotations with the aim of reducing the gap between image features and high-level semantics. Ontologies ease information retrieval. They are used to mine, interpret, and organise knowledge. An ontology may be seen as a knowledge base that can be used to improve the image retrieval process, and conversely keywords obtained from automatic tagging of image regions may be useful for creating an ontology. We engineer an ontology that surrogates concepts derived from image feature descriptors. We test the usability of the constructed ontology by querying the ontology via the Visual Ontology Query Interface, which has a formally specified grammar known as the Visual Ontology Query Language. We show that synergy between ontology and image annotations is possible and this method can reduce the gap between image features and high-level semantics by providing the relationships between objects in the image. In this thesis, we conclude that suitable techniques for image retrieval include fusing text accompanying the images with visual features, automatic region tagging and using an ontology to enrich the semantic meaning of the tagged image regions.

Intergiciel Sémantique pour les Services de l'Informatique Diffuse

<p>Today, scientists in various biomedical fields rely on biological data sources in their research. Large amounts of information concerning, for instance, genes, proteins and diseases are publicly available on the internet, and are used daily for acquiring knowledge. Typically, biological data is spread across multiple sources, which has led to heterogeneity and redundancy.</p><p>The current thesis suggests grouping as one way of computationally managing biological data. A conceptual model for this purpose is presented, which takes properties specific for biological data into account. The model defines sub-tasks and key issues where multiple solutions are possible, and describes what approaches for these that have been used in earlier work. Further, an implementation of this model is described, as well as test cases which show that the model is indeed useful.</p><p>Since the use of ontologies is relatively new in the management of biological data, the main focus of the thesis is on how semantic similarity of ontological annotations can be used for grouping. The results of the test cases show for example that the implementation of the model, using Gene Ontology, is capable of producing groups of data entries with similar molecular functions.</p>

<p>The semantic web is a vision of the Internets future, there machines and humans can understand the same information. To make this possible, documents have to be provided with metadata in a general language. W3C has created Web Ontology Language (owl) for this purpose.</p><p>This report present the creation of a LaTeX package, which makes it possible to include metadata in pdf files. It also presents how you can create annotations, which are bound to the metadata that's been generated. With the help of this package it's easy to create pdf documents with automatically generated metadata and annotations.</p>

<p>There is a body of research on the effect of physical activity oncognition in the old adult population. Less research areconducted on adolescents. The aim for this study is to find out ifadolescents long-term memory is affected by physical activity.144 pupils were asked to rate their physical activity each week.Thereafter their long- term memory was tested through tests onepisodic- and semantic memory. The results showed that thosewho are physically active more than 4 hours had a better scoreon part of the semantic test but no effect was found in theepisodic test. This result indicates that physical activity not onlyaffects working memory, as was shown by previous research butalso has an effect in parts of the semantic long-term memory.</p>

This paper is about an implemented natural language interface that translates from English into semantic net relations and from semantic net relations back into English. The parser and companion generator were implemented for two reasons: (a) to enable experimental work in support of a theory of learning by analogy; (b) to demonstrate the viability of a theory of parsing and generation built on commutative transformations. The learning theory was shaped to a great degree by experiments that would have been extraordinarily tedious to perform without the English interface with which the experimental data base was prepared, revise, and revised again. Inasmuch as current work on the learning theory is moving toward a tenfold increase in data-base size, the English interface is moving from a facilitating role to an enabling one. The parsing and generation theory has two particularly important features: (a) the same grammar is used for both parsing and generation; (b) the transformations of the grammar are commutative. The language generation procedure converts a semantic network fragment into kernel frames, chooses the set of transformations that should be performed upon each frame, executes the specified transformations, combines the altered kernels into a sentence, performs a pronominalization process, and finally produces the appropriate English word string. Parsing is essentially the reverse of generation. The first step in the parsing process is splitting a given sentence into a set of kernel clauses along with a description of how those clauses hierarchically related to each other. The clauses are hierarchically related to each other. The clauses are used to produce a matrix embedded kernel frames, which in turn supply arguments to relation-creating functions. The evaluation of the relation-creating functions results in the construction of the semantic net fragments.

Omega is a description system for knowledge embedding which incorporates some of the attractive modes of expression in common sense reasoning such as descriptions, inheritance, quantification, negation, attributions and multiple viewpoints. A formalization of Omega is developed as a framework for investigations on the foundations of knowledge representation. As a logic, Omega achieves the goal of an intuitively sound and consistent theory of classes which permits unrestricted abstraction within a powerful logic system. Description abstraction is the construct provided in Omega corresponding to set abstraction. Attributions and inheritance are the basic mechanisms for knowledge structuring. To achieve flexibility and incrementality, the language allows descriptions with an arbitrary number of attributions, rather than predicates with a fixed number of arguments as in predicate logic. This requires a peculiar interpretation for instance descriptions, which in turn provides insights into the use and meaning of several kinds of attributions. The formal treatment consists in presenting semantic models for Omega, deriving an axiomatization and establishing the consistency and completeness of the logic.

We report on a study of how people look for information within email, files, and the Web. When locating a document or searching for a specific answer, people relied on their contextual knowledge of their information target to help them find it, often associating the target with a specific document. They appeared to prefer to use this contextual information as a guide in navigating locally in small steps to the desired document rather than directly jumping to their target. We found this behavior was especially true for people with unstructured information organization. We discuss the implications of our findings for the design of personal information management tools.

The COntext INterchange (COIN) strategy is an approach to solving the problem of interoperability of semantically heterogeneous data sources through context mediation. COIN has used its own notation and syntax for representing ontologies. More recently, the OWL Web Ontology Language is becoming established as the W3C recommended ontology language. We propose the use of the COIN strategy to solve context disparity and ontology interoperability problems in the emerging Semantic Web – both at the ontology level and at the data level. In conjunction with this, we propose a version of the COIN ontology model that uses OWL and the emerging rules interchange language, RuleML. / Singapore-MIT Alliance (SMA)