IRQX: A Framework for Information Retrieval Algorithms Using Query Expansion Techniques

The number of information retrieval users and their operations are continuously increasing with the rapid growth of internet technologies. Information Retrieval is one of the most prevalent operations that is frequently used by the Internet users. The process of Information Retrieval may cause two problems. First, the search engine may retrieve irrelevant documents and second it may fail to retrieve the relevant documents. Many approaches have been proposed to improve the query representation by reformulating the queries. Among them, Query Expansion (QE) is one of the most effective approaches. In Information Retrieval, Query Expansion is referred to as the techniques or algorithms that reformulate the original query by adding or modifying new terms into the query, in order to achieve better retrieval results. This paper contributed to the process of information retrieval algorithms using query expansion techniques to improve the precision and recall. The proposed framework Information Retrieval algorithms using Query Expansion (IRQX) facilitates the users to select their choice of algorithms based on their need

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

An experiment with ontology mapping using concept similarity

This paper describes a system for automatically mapping between concepts in different ontologies. The motivation for the research stems from the Diogene project, in which the project's own ontology covering the ICT domain is mapped to external ontologies, in order that their associated content can automatically be included in the Diogene system. An approach involving measuring the similarity of concepts is introduced, in which standard Information Retrieval indexing techniques are applied to concept descriptions. A matrix representing the similarity of concepts in two ontologies is generated, and a mapping is performed based on two parameters: the domain coverage of the ontologies, and their levels of granularity. Finally, some initial experimentation is presented which suggests that our approach meets the project's unique set of requirements