bookmarks  32

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    The World Wide Web contains huge amounts of information created by many different organizations, communities and individuals for many different reasons. Users of the Web can easily access this information by specifying URI addresses, searching, and following links to find other related resources. The simplicity of usage is a key aspect that made Web so popular; so popular in fact, it is hard to imagine life without it anymore.
    9 years ago by @mapet
     
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      Blog Eintrag zu MySpace und Semantic Web
      9 years ago by @mapet
       
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        Alles was man über semantische Suche wissen will! Projekte, Lösungen und Events!
        9 years ago by @mapet
         
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          Typical electronic business applications need to make queries about products and services made available by sellers and serviceproviders through the Web. Unfortunately, it can be a very time consuming task to discover information as simple as those, since every site has its own metaphore. The so-called search engines can facilitate such task because a client provides some keywords and obtain a list of Uniform Resource Locators (URL) that point to the target sites, but it does not prevent from interacting with each site. We propose and define a novel kind of service for the Web: the semantic search engine. Differently from traditional search engines, a semantic search engine stores semantic information about Web resources and is able to solve complex queries, considering as well the context where the Web resource is targeted. We show, through examples, how a semantic search engine may be employed in order to permit clients obtain information about commercial products and services, as well as about sellers and service providers which can be hierarchically organized. Semantic search engines may seriously contribute to the development of electronic business applications since it is based on strong theory and widely accepted standards.
          9 years ago by @mapet
           
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            Abstract. The envisioned Semantic Web aims to provide richly annotated and explicitly structured Web pages in XML, RDF, or description logics, based upon underlying ontologies and thesauri. Ideally, this should enable a wealth of query processing and semantic reasoning capabilities using XQuery and logical inference engines. However, we believe that the diversity and uncertainty of terminologies and schema-like annotations will make precise querying on a Web scale extremely elusive if not hopeless, and the same argument holds for large-scale dynamic federations of Deep Web sources. Therefore, ontology-based reasoning and querying needs to be enhanced by statistical means, leading to relevanceranked lists as query results. This paper presents steps towards such a “statistically semantic”Web and outlines technical challenges.We discuss how statistically quantified ontological relations can be exploited in XML retrieval, how statistics can help in making Web-scale search efficient, and how statistical information extracted from users’ query logs and click streams can be leveraged for better search result ranking. We believe these are decisive issues for improving the quality of next-generation search engines for intranets, digital libraries, and the Web, and they are crucial also for peer-to-peer collaborative Web search.
            9 years ago by @mapet
             
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              A tutorial on Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI), its advantages, applications and limitations. Covers LSI myths and misconceptions from search engine marketers.
              9 years ago by @mapet
               
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                Die Adaption von Techniken und Verfahren des Semantic Web für Inhouse-Lösungen adressiert neben dem Themenkreis Enterprise Information Integration (EII) zumeist neue Handlungsoptionen für das Wissensmanagement, die über den derzeit am öftesten diskutierten Anwendungsfall „intelligente Suchmaschine“ beträchtlich hinausgehen. In diesem Beitrag werden die vielfältigen Einsatzmöglichkeiten semantischer Technologien im betrieblichen Kontext systematisch anhand der Architektur eines integrierten Wissensmanagement-Systems diskutiert und hinsichtlich ihrer Einsatzszenarien untersucht.
                9 years ago by @mapet
                 
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                  Das JoinVision-Matching ist ein semantisches Suchverfahren, das Job- bzw. Projektangebote nach dem Grad der Übereinstimmung mit den Suchbegriffen in den unten stehenden Suchfeldern anzeigt.
                  9 years ago by @mapet
                   
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                    Das Internet und die darüber verbundenen neuen Technologien bieten zahlreiche interessante Möglichkeiten, den Transfer-Prozess zwischen Wissensproduzenten (Autoren, Dozenten, Ausbilder) und Wissenskonsumenten (Studierende, Lernende, Auszubildende) gezielt zu unterstützen. Die Forschungsgruppe Cooperation & Management hat in den vergangenen Jahren ein Internet-basiertes Wissenstransfer-System entwickelt, das sie erfolgreich im täglichen Ausbildungsbetrieb einsetzt. Anhand dieser Lösung und den damit gesammelten Erfahrungen wird die Nutzung semantischer Netze zur Unterstützung der Suche nach Schulungsmaterial aufgezeigt. Hierbei wird nicht nur aufgezeigt, wie die strukturierte Datenbasis des Wissenstransfersystems durchsucht werden kann, sondern auch, wie sich diese Suche auf das gesamte Internet ausdehnen lässt.
                    9 years ago by @mapet
                     
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                      Suchen von Informationen in kurzer Zeit: Projektbeschreibung bzw. Anforderung: Der Agent muss die Suche in 15.000 Dokumenten im Rahmen des Telefonates durchführen. Hier hat er gar nicht die Zeit für komplexere Suchformulierungen. Der anrufende Bürger verwendet eine komplett andere Sprache („Bürgerdeutsch“) als sie in den Dokumenten zu finden ist („Amtsdeutsch“). Entweder der Agent übernimmt die Übersetzung, oder die Maschine hat die beiden Sprachen und ihre Verbindung gelernt.
                      9 years ago by @mapet
                       
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                        Mit der Entwicklung des Internet zum Massenmedium, gefördert durch die dezentrale Struktur des Mediums als auch die niedrige Einstiegsbarriere, steigt die Anzahl der zur Verfügung stehenden Informationen exponentiell an. Das neue Medium ermöglicht es uns, ohne Rücksicht auf soziale Barrieren auf Wissen zuzugreifen(zumindest in den westlichen Industriestaaten) und gleichzeitig aktiv an der Gestaltung der Informationsgesellschaft teilzunehmen. Bibliotheken und ihre Stichwortkataloge erlaubten uns bisher mehr oder weniger effektiv auf das bereits indizierte und archivierte Wissen zuzugreifen. Das exponentielle Wachstum des Internet und seine flüchtige Struktur mit wesentlich geringeren Wissenshalbwertzeiten kann jedoch von traditionellen Indexsystemen nur schwerlich dargestellt werden – der Aufwand für die Pflege dieser Kataloge würde ins Unermessliche steigen.
                        9 years ago by @mapet
                         
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                          Es werden anhand der Anwendungen des betrieblichen Informationsmanagements die verschiedenen Wissens- und Informationsprozesse dargestellt, die bei der Entwicklung semantischer Anwendungen grundlegend sind.Ziel ist es aus der Entwicklungsperspektive der einzelnen Anwendungen die Notwendigkeitund den Nutzen aufzuzeigen, der sich aus dem Einsatz von semantischen Technologien ergibt. Die vorgestellten Fallbeispiele veranschaulichen die Einsatzgebiete von semantischen Technologien in der betrieblichen Anwendung.
                          9 years ago by @mapet
                           
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                            IEEE PAPER: As a matter of fact, many so-called semantic search algorithms are derived from the traditional indexterm- based search models. In this paper, we survey the traditional information retrieval models by categorizing them into three main classes and eleven subclasses, and analyse their benefits and issues of them.
                            9 years ago by @greenredhohi
                             
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                              IEEE PAPER: Web services integrate various business application systems to provide worldwide platform to serve customers directly over the Internet. As the increasing number of business applications joining into the integration, the service activities are involving more and more complex situations. The enormous workload and the complex business processes involved by a request cannot be dealt with the simple request-answer model. It is highly desirable that Web services system can support vast clients’ requests efficiently, effectively and promptly. This paper provides a searching mechanism to serve clients intelligently. It utilizes an efficient matchmaker to discover clients’ requests by reusing past experiences. The matchmaker is enhanced by an automated discovery algorithm. It uses not only OWL-S for identifying different matching levels by related domain ontologies but also is enhanced with reuse mechanism of case-based reasoning (CBR) with a formula for similarity. The proposed matchmaker takes advantages of semantics and CBR techniques to improve the efficiency and effectiveness of Web services searching.
                              9 years ago by @greenredhohi
                               
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                                IEEE PAPER: To make ECommerce information searching across Internet more efficient, ECommerce information searching becomes more and more important. In this paper, ECommerce Information Model (EIM) and a novel EIM-based semantic similarity algorithm are presented. This semantic similarity algorithm takes advantage of ECommerce-based information content and edge-based distance in calculating conceptual similarity. According to EIM, a semantic eigenvector, which consists of the semantic similarity values of a given document, is used to represent the semantic content of the document. The semantic eigenvectors and EIM-based similarity function can be applied to ECommerce information retrieval. Experimental results show that the performance of the proposed method is much improved when compared with that of the traditional Information retrieval techniques.
                                9 years ago by @greenredhohi
                                 
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                                  IEEE PAPER: A lot of high quality and wealthy data are hidden in backend database and search engines can not index this page, which is called Deep Web. It is mostly accessible through query interfaces. SDWS, a semantic search engine for Deep Web is presented. We are studying and implementing Semantic Web technology to the each process of Deep Web information integrated, and expertise in Deep Web discovering, annotating query results and integrating information. The novel approach promise better access to Deep Web.
                                  9 years ago by @greenredhohi
                                   
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                                    IEEE PAPER: The paper brings forth a semantic search engine framework based on Ontology. the technology overcomes traditional search engine’s shortcomings such as poor semantic processing capability and understandingcapability because of the adoption of text retrieval and greatly lifts the retrieval efficiency.
                                    9 years ago by @greenredhohi
                                     
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                                      IEEE PAPER: In order to solve the problems of the low query precision and the shortness in understanding user’s query intention that occur in traditional search engine, a framework of semantic search engine based on ontology is brought forwards. It need to extract information after the information crawled by the spider, and an algorithm of information extraction based on ontology is proposed. By using semantic reasoning which based on ontology, it helps the search engine to understand user’s query intention. A prototype of search engine is developed by using of lucene, and the search result is better than that of common search engine.
                                      9 years ago by @greenredhohi
                                       
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                                        IEEE PAPER: Semantic search requires a search engine to properly interpret the meaning of a user's query and the inherent relations among the terms that a document contains with respect to a specific domain. We present the framework of such a search engine based on domain ontologies. In this framework, a search request, which can be either a keyword list as in traditional search methods or a query in complex form containing various restrictions to the search, is first processed by a query parser which then finds qualified RDF triples in domain ontologies. Web documents relevant to the requested concepts and individuals specified in these triples are then retrieved by a document retriever. And finally, the retrieved documents are ranked according to their relevance to the user's query. An extended term-document matrix is built to reflect the relevance between documents, concepts/individuals, and terms. Such a matrix makes it possible for the search engine to work even in case that there are no available domain ontologies for user requests.
                                        9 years ago by @greenredhohi
                                         
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