بایگانی برچسب برای: Information retrieval

An architectural design for effective information retrieval in semantic[taliem.ir]

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way .This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the preexisting domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.
Semantic web service discovery system for road traffic information[taliem.ir]

Semantic web service discovery system for road traffic information services

We describe a multi-agent platform for a traveller information system, allowing travellers to find the road traffic information web service (WSs) that best fits their requirements. After studying existing proposals for discovery of semantic WS, we implemented a hybrid matching algorithm, which is described in detail here. Semantic WS profiles are annotated semantically as an OWL-S and also the traveller request is represented as a OWL-S profile. The algorithm assigns different weights and measures to each advertised WS profile parameter, depending on their relevance, type and nature. To do this we have extended Paolucci’s Algorithm and adapted it to our scenario. We have added new similarity measures, in particular, the use of the ‘sibling’ relationship, to improve the recall, allowing relevant services to be discovered by the users yet not retrieved by other algorithms. Although we have increased the similarity concept relations, we have improved the run-time using a pre-process filter step that reduces the set of potentially useful WS. This improves the scalability of the semantic matching algorithm.