Search engines or localized software systems developed for information searching, play an important role in knowledge discovery. Proliferation of data in the web and social media has posed significant challenges in finding relevant information efficiently even using those search engines or other software systems. Moreover, those systems or engines tend to collect large number of data, which could be useful for end users in various ways but have overlooked the meaning of the search phrases, hence generate irrelevant search results. A unit level searching i.e. searching information within a website or page is also not effective as they follow exact keyword matching techniques and ignore the semantic level matching of search phrases. In order to address those deficiencies, this research proposes a hybrid approach which use the semantics of data, community preferences as well as collaborative filtering techniques for semantic information retrieval. More specifically, Topic modeling based on Latent Dirichlet Allocation together with topic-driven based community detection methods are applied for identifying personalized search results generation and hence improve the relatedness of the research results. Based on the proposed hybrid approach a framework for semantic search that can easily be integrated to a software application has been implemented. The evaluation results confirm the effectiveness of search results which outperform benchmark approaches that follow traditional keyword search algorithms.

Semantic Information Retrieval based on Topic Modeling and Community Interests Mining

An IIR (intelligent information retrieval) is a machine learning system used by users to retrieve the massive volume of information available through the internet. IIR offers personalization and efficiency in the current internet development allowing users to examine and acquire appropriate information. The paper will focus on the keyword searching issues of the current digital library system. The proposal is to choose a model that solves the issue using metadata case base and concept-based approach. The aim is to introduce a set of categories and concepts in the domain field that demonstrates some relations between them. As a result, poor quality of information retrieval in digital library system will be solved. The experiment has demonstrated that the already developed domain-specific ontology can be efficient for query advancement. Many researchers have used semantic retrieval technology using concepts to solve problems that lack semantics in traditional retrieval technology. Using concepts in ontology enhances search results.

A Theoretical Approach For Developing A Semantic Analysis IIR Tool

The main goal of web Information Retrieval (IR) is to find highly relevant data for Internet users. Nowadays, huge billions of websites are populated day by day. The information growth is tremendous which is unbelievable. The current Information Retrieval (IR) system brings out very inadequate results because it is a keyword-based technique. The current IR system tools designed are very less capable to match exact document to the net users from the large web repository. Information ambiguity leads to poor information interconnection. It is impossible to handle many users and content to ensure trust at all levels. Hence to overcome these issues, in this novel proposes an Improve Firefly Heuristic Optimization Scheme (IFHOS) algorithm is used to identify the relevant document. The proposed first stage is pre-processing to remove stop words and tokenization etc. Then identify the web user interest using Density based Search Importance Rate (DSIR) technique. Later the proposed IFHOS technique identifies the appropriate information document from a user search. Thus, the proposed method achieves efficient performance than other comparative methods. 

Improve Firefly Heuristic Optimization Scheme for Web based Information Retrieval

/pdf/intelligent-information-retrieval-system-3a3md696.pdf

Intelligent Information Retrieval System

AbstractThe use of machine learning and semantic analysis in case law is the new trend in modern society. Case Based Reasoning tools are being used to analyze texts in courts to make and predict judicial decisions which are designed to base the outcomes of current court proceedings from past and or learning from the mistakes to make better decisions. Because of the accuracy and speed of this technology, researchers in the justice system have introduced Machine Learning to optimize the Case-Based Researching approach. This paper presents a study aimed to critically analyze semantic analysis in the context of machine learning and proposes a case-based reasoning information retrieval system. It will explore how CBR-IR is being used to improve legal case law information retrieval. The study covers the importance of semantic analysis. The study will discuss limitations and recommendations for improvement and future research. The study recommends that it is necessary to conduct further research in semantic analysis and how they can be used to improve information retrieval of Canadian maritime case law. 

Introducing Intelligence to the Semantic Analysis of Canadian Maritime Case Law: Case Based Reasoning Approach

Semantic Information Retrieval: A Survey

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Semantic Information Retrieval: A Survey