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Automatic Composition of Semantic Web Services
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This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.
One of the major concerns about the process of developing and maintaining a meeting software application is the complexity and robustness of the architecture and the underlying data model. That sometimes creates a problem with the need of restructuring the interface or the business logic, due to changes in requirements. Therefore, we need a flexible architecture framework, one that will provide easy and fast model modification. In this paper we present our ongoing work for developing an online meeting application, which is based on a flexible architecture framework. The application development is based on the following concepts: a data model based on ontologies and semi-structured data stored in RDF format, a service oriented architecture with the capability of automatic composition of semantic web services, and a user interface based on a guided natural language input, dynamic forms, and a graphic data representation tool, both based on ontologies.
Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party’s subjective view of a domain. In this paper we show how ontologies can be contextualized, thus
acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized
or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other
ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation
of contextual ontologies.
Richly interlinked, machine-understandable data constitute the basis for the Semantic Web. We provide a framework, S-CREAM,
that allows for creation of metadata and is trainable for a specific domain. Annotating web documents is one of the major
techniques for creating metadata on the web. The implementation of S-CREAM, OntoMat-Annotizer supports now the semi-automatic
annotation of web pages. This semi-automatic annotation is based on the information extraction component Amilcare. OntoMat-Annotizer
extract with the help of Amil-care knowledge structure from web pages through the use of knowledge extraction rules. These
rules are the result of a learning-cycle based on already annotated pages.
Although OWL, an emerged standard of expressing ontologies in the Semantic Web, is a very expressive ontology language, the OWL datatyping formalism is far not expressive enough to generally represent user-defined datatypes (such as XML Schema derived datatypes), not to mention user-defined datatype predicates (such as > for non-negative integers) in Semantic Web ontologies and applications. In this paper, we show how to extend OWL datatype formalism to provide a general way to represent user-defined datatypes and datatype predicates, based on datatype groups. The result is OWL-E, a language extending OWL DL with the datatype group-based class constructors to allow the use of datatype expressions in class restrictions. The novelty of OWL-E is that it enhances OWL DL with much more datatype expressiveness and it is still decidable. Keyword: ontology language, OWL, datatype
The KIM platform provides a novel Knowledge and Information Management framework and services for automatic semantic annotation, indexing, and retrieval of documents. It provides a mature and semantically enabled infrastructure for scalable and customizable information extraction (IE) as well as annotation and document management, based on GATE. 1 Our understanding is that a system for semantic annotation should be based upon a simple model of real-world entity concepts, complemented with quasi-exhaustive instance knowledge. To ensure efficiency, easy sharing, and reusability of the metadata we introduce an upper-level ontology. Based on the ontology, a large-scale instance base of entity descriptions is maintained. The knowledge resources involved are handled by use of state-of-the-art Semantic Web technology and standards, including RDF(S) repositories, ontology middleware and reasoning. From a technical point of view, the platform allows KIM-based applications to use it for automatic semantic annotation, for content retrieval based on semantic queries, and for semantic repository access. As a framework, KIM also allows various IE modules, semantic repositories and information retrieval engines to be plugged into it. This paper presents the KIM platform, with an emphasis on its architecture, interfaces, front-ends, and other technical issues.
