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

Abstract

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.

A Proposed Multi-Domain Approach for Automatic Classification of Text Documents

“…Building such an ontology depends on either the documents in the dataset (training or testing ones), a readymade ontology or both. We can find this type in [3,6,14], but they differ in the construction method. The dependence in [6] is on the training documents to construct the ontology.…”

“…This category ontology is then used to classify documents by subjecting the ontology of each new document to it. The construction of ontology in [3,14] depends on a readymade ontology, which in case of [3] is the WordNet and in [14] is the Wikipedia. In [14] an RDF ontology constructed from a full English version of Wikipedia, on which the documents are being subjected in order to make a semantic graph of each document based on the hierarchy in the ontology.…”

“…In [14] an RDF ontology constructed from a full English version of Wikipedia, on which the documents are being subjected in order to make a semantic graph of each document based on the hierarchy in the ontology. In [3] however the WordNet is not the main source for constructing the ontology. The WordNet is used to expand the query into a list of semantic instances which are then integrated with the keywords from documents, annotated by an expert, and the result is the construction of a hybrid ontology.…”

“…Semi-supervised technique is applied in [5], where both labeled and unlabeled documents exist, and the classifier's decision criterion is learnt based on expectation maximization algorithm. The unsupervised technique used in [3,9,10,13,14,16,19], where ontology is used, to compensated for the absence of the training dataset, with the help of the unlabeled documents, the query or both.…”

“…In [13], fuzzy relations formed between two ontologies also used in query expansion. A fuzzy matching technique used in [3] to construct a hybrid ontology from the integrated list between the document and the query which is then used in classifying documents. The similarity between document keywords and the ontology is measured in [1,9] using also fuzzy theory.…”