Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology

Digital Repositories and the Semantic Web: Semantic Search and Navigation for DSpace

4th International Conference on Open RepositoriesThis presentation was part of the session : DSpace User Group PresentationsDate: 2009-05-21 08:30 AM – 10:00 AMIn many digital repository implementations, resources are often described against some flavor of metadata schema, popularly the Dublin Core Element Set (DCMES), as is the case with the DSpace system. However, such an approach cannot capture richer semantic relations that exist or may be implied, in the sense of a Semantic Web ontology. Therefore we first suggest a method in order to semantically intensify the underlying data model and develop an automatic translation of the flatly organized metadata information to this new ontology. Then we propose an implementation that provides for inference-based knowledge discovery, retrieval and navigation on top of digital repositories, based on this ontology. We apply this technique to real information stored in the University of Patras Institutional Repository that is based on DSpace, and confirm that more powerful, inference-based queries can indeed be performed

Using multiple related ontologies in a fuzzy information retrieval model.

With the Semantic Web progress many independently developed distinct domain ontologies have to be shared and reused by a variety of applications. The use of ontologies in information retrieval applications allows the retrieval of semantically related documents to an initial users´ query. This work presents a fuzzy information retrieval model for improving the document retrieval process considering a knowledge base composed of multiple domain ontologies that are fuzzy related. Each ontology can be represented independently as well as their relationships. This knowledge organization is used in a novel method to expand the user initial query and to index the documents in the collection. Experimental results show that the proposed model presents better overall performance when compared with another fuzzy-based approach for information retrieval.SBIA 2008

The quest for information retrieval on the semantic web

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal has been tested on corpora of significant size, showing promising results with respect to keyword-based search, and providing ground for further analysis and research

Using TREC for cross-comparison between classic IR and ontology-based search models at a Web scale

The construction of standard datasets and benchmarks to evaluate ontology-based search approaches and to compare then against baseline IR models is a major open problem in the semantic technologies community. In this paper we propose a novel evaluation benchmark for ontology-based IR models based on an adaptation of the well-known Cranfield paradigm (Cleverdon, 1967) traditionally used by the IR community. The proposed benchmark comprises: 1) a text document collection, 2) a set of queries and their corresponding document relevance judgments and 3) a set of ontologies and Knowledge Bases covering the query topics. The document collection and the set of queries and judgments are taken from one of the most widely used datasets in the IR community, the TREC Web track. As a use case example we apply the proposed benchmark to compare a real ontology-based search model (Fernandez, et al., 2008) against the best IR systems of TREC 9 and TREC 2001 competitions. A deep analysis of the strengths and weaknesses of this benchmark and a discussion of how it can be used to evaluate other ontology-based search systems is also included at the end of the paper