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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
IEEE Paper:
While semantic search technologies have been proven
to work well in specific domains, they still have to confront
two main challenges to scale up to the Web in its
entirety. In this work we address this issue with a novel
semantic search system that a) provides the user with the
capability to query Semantic Web information using
natural language, by means of an ontology-based Question
Answering (QA) system [14] and b) complements the
specific answers retrieved during the QA process with a
ranked list of documents from the Web [3]. Our results
show that ontology-based semantic search capabilities
can be used to complement and enhance keyword search
technologies.