Hybrid Search: Effectively Combining Keywords and Semantic Searches

The Scientific World Journal, 2015

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.

2014

We present an innovative system for semantic search, based on an ontology, called Search Ontology. The Search Ontology contains search terms, for which synonymous labels can be defined, and search concepts, specified by rules, that determine how search terms are combined with abstract NEAR or Boolean operators to describe corresponding concepts in documents. A search query can be generated from the ontological specification and executed on an information retrieval system such as Lucene afterwards. This approach has the advantage that the user can create powerful and complex queries by ontological specifications only, with minimal effort and without knowing the query syntax. The ontology itself is easily adaptable, extensible and reusable. No information contained in the ontology is used while preprocessing and indexing the documents, since the ontology is being constantly expanded by users of the system and changes in the ontology should not trigger new indexing and analysis for the...

Journal of Systems Integration, 2001

This paper presents a knowledge-based approach to effective document retrieval. This approach is based on a dual document model that consists of a document type hierarchy and a folder organization. A predicate-based document query language is proposed to enable users to precisely and accurately specify the search criteria and their knowledge about the documents to be retrieved. A guided search tool is developed as an intelligent natural language oriented user interface to assist users formulating queries. Supported by an intelligent question generator, an inference engine, a question base, and a predicate-based query composer, the guided search collects the most important information known to the user to retrieve the documents that satisfy users' particular interests. A knowledge-based query processing and search engine is devised as the core component in this approach. Algorithms are developed for the search engine to effectively and ef®ciently retrieve the documents that match the query.

The Semantic Web, 2007

Current information retrieval (IR) approaches do not formally capture the explicit meaning of a keyword query but provide a comfortable way for the user to specify information needs on the basis of keywords. Ontology-based approaches allow for sophisticated semantic search but impose a query syntax more diAEcult to handle. In this paper, we present an approach for translating keyword queries to DL conjunctive queries using background knowledge available in ontologies. We present an implementation which shows that this interpretation of keywords can then be used for both exploration of asserted knowledge and for a semantics-based declarative query answering process. We also present an evaluation of our system and a discussion of the limitations of the approach with respect to our underlying assumptions which directly points to issues for future work.

Semantic Search, 2008

2014 International Conference on Intelligent Computing Applications, 2014

Ontology based semantic search will lead to new generation of search based on the meaning of keyword rather than keyword and helps in finding correct information on the web. Here, ontology provides an explicit specification of conceptualization which helps to connect the information on the existing web pages with the background knowledge. Ontology based search overcomes the semantic gap between the keyword found in documents and those in query. This survey provides an introduction to ontology based semantic search and review the different details of selected ontology based search approaches and compare them by means of classification criteria. Based on this comparison, this survey attempts to identify the possible directions for future research.

Karbala International Journal of Modern Science, 2019

Traditional search mechanisms are based on the keyword search, which does not consider the semantic links between different concepts. This leads to the loss of relevant documents due to inaccurate query formulation or using contextually close words and concepts in the query. To solve the problems of formulating user queries and interdisciplinarity of concepts, it is suggested to use semantic search. The proposed method for implementing semantic search is applicable to large scopes of text data and is based on using a genetic algorithm. Unlike standard methods for information search, the suggested method allows us to consider the semantics of interrelationships between concepts and to handle interdisciplinary concepts correctly. By the aid of semantic tagging, documents contain concepts that are not present in the user's initial query but are semantically close to the requested concepts. Semantic tagging is performed for each document separately, which provides parallel tagging in several subject areas. By the time of the document ontological profile formation is completed, all semantic distances between pairs of distinguished concepts are calculated. Concepts are considered contextually close if their semantic proximity value is above a certain threshold value that is specified in the search parameters. Building a document ontological profile is a multicriteria task, since it depends on a lot of characteristics, so genetic algorithms can be used to solve it effectively. The developed genetic algorithm is intended for more accurate distribution of weight coefficients and estimation of semantic proximity of concepts.

Information Systems, 2009

We seek to leverage an expert user's knowledge about how information is organized in a domain and how information is presented in typical documents within a particular domain-specific collection, to effectively and efficiently meet the expert's targeted information needs. We have developed the semantic components model to describe important semantic content within documents. The semantic components model for a given collection (based on a general understanding of the type of information needs expected) consists of a set of document classes, where each class has an associated set of semantic components. Each semantic component instance consists of segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. This paper describes how the semantic components model can be used to improve an information retrieval system. We present experimental evidence from a large interactive searching study that compared the use of semantic components in a system with full text and keyword indexing, where we extended the query language to allow users to search using semantic components, to a base system that did not have semantic components. We evaluate the systems from a system perspective, where semantic components were shown to improve document ranking for precision-oriented searches, and from a user perspective. We also evaluate the systems from a sessionbased perspective, evaluating not only the results of individual queries but also the results of multiple queries during a single interactive query session.

Keyword based Search engines are not able to provide relevant search result because they suffer from the fact that they do not know the meaning of the terms and expression used in the web pages and the relationship between them. This paper compares the semantic search performance of both keyword-based and semantic web based search engines. Initially, two keyword based search engines (Google and Yahoo) and three semantic search engines (Hakia, DuckDuckGo and Bing) are selected to compare their search performance on the basis of precision ratio and how they handle natural language queries. Ten queries, from various topics was run on each search engine, the first twenty documents on each retrieval output was classified as being "relevant" or "non-relevant". Afterwards, precision ratios were calculated for the first 20 document retrieved to evaluate performance of these search engines. Also, comparison of some popular Semantic search engines is provided with their features.