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.