scholarly journals Criminal Records Information Retrieval System: A Conceptual Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 1473-1476
Author(s):  
Nitin Sonawale
Keyword(s):  
Information Retrieval ◽  
Problem Solving ◽  
Conceptual Model ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Density Of State ◽  
Criminal Records ◽  
System A ◽  
Time Period ◽  
Crime Trend

This model helps to increase communication between Police and public. It will reduce time & increase the problem solving efficiency in time period it will be more helpful. In this admin is key person, user(police) is also have secure registration & public can communicate with all other users through mail. Here we are going to use clustering technique because it more powerful to forming accurate cluster, speed of creating cluster, identifying crime trend & crime zone ,crime density of state.

Information Retrieval

2016 ◽  
Author(s):  
Qiaozhu Mei ◽  
Dragomir Radev
Keyword(s):  
Information Retrieval ◽  
Digital Libraries ◽  
Web Search ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Information Need ◽  
System A ◽  
Recent Developments ◽  
Text Information ◽  
Text Information Retrieval

This chapter is a basic introduction to text information retrieval. Information Retrieval (IR) refers to the activities of obtaining information resources (usually in the form of textual documents) from a much larger collection, which are relevant to an information need of the user (usually expressed as a query). Practical instances of an IR system include digital libraries and Web search engines. This chapter presents the typical architecture of an IR system, an overview of the methods corresponding to the design and the implementation of each major component of an information retrieval system, a discussion of evaluation methods for an IR system, and finally a summary of recent developments and research trends in the field of information retrieval.

Semantic Similarity Based on Ontology Technology and its Application in Mobile Information Retrieval for Product Design

2009 ◽  
Vol 419-420 ◽  
pp. 741-744
Author(s):  
Yong Jun Zheng ◽  
Zhong Ming Ren ◽  
Dai Zhong Su ◽  
Leslie Arthur
Keyword(s):  
Information Retrieval ◽  
Product Design ◽  
Retrieval System ◽  
Product Information ◽  
Collaborative Work ◽  
Communication Technologies ◽  
Information Retrieval System ◽  
Semantic Relationships ◽  
System A

With recent advances in wireless communication technologies, the world of mobile computing is flourishing with a variety of applications. This paper presents a mobile product information retrieval system that supports collaborative work among remote users. With the development of the system, a knowledge representation framework has been adopted which accommodates semantic relationships and similarity of product data. To illustrate the system developed, a case study in information retrieval for product design is presented．

scholarly journals An Ontology Based Information Retrieval System

2020 ◽  
Vol 9 (2) ◽  
pp. 638-643
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Retrieval System ◽  
Keyword Search ◽  
Domain Ontology ◽  
Information Retrieval System ◽  
Search System ◽  
Domain Specific ◽  
System A ◽  
Schema Graph

Ontology provide a structured way of describing knowledge. Ontology's are usually repositories of concepts and relations between them, so using them in information retrieval seems to be a reasonable goal. The main objective in this report is to provide efficient means to move from keyword-based to concept-based information retrieval utilizing ontology's for conceptual definitions [1]. In this paper, we present the skeleton of such an IR system which works on a collection of domain specific documents and exploits the use of a domain specific ontology to improve the overall number of relevant documents retrieved. In this system, a user enters a query from which the meaningful concepts are extracted; using these concepts and domain ontology, query expansion is performed. We propose a system that matches the query terms in the ontology/schema graph and exploits the surrounding knowledge to derive an enhanced query. The enhanced query is given to the underlying basic keyword search system LUCENE [2]. In this approach we try to make use of more ontological Knowledge than IS-A and HAS-A relationships and synonyms for information retrieval.

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