Information Retrieval by Semantic Analysis and Visualization of the Concept Space of D-Lib® Magazine

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available in the internet quickly. Even for a specific domain like college or university website it may be difficult for a user to browse through all the links to get the relevant answers quickly. Objective:: In this scenario, design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper a novel conversational interface chat-bot application with information retrieval and text summariza-tion skill is designed and implemented. Firstly this chat-bot has a simple dialog skill when it can understand the user query intent, it responds from the stored collection of answers. Secondly for unknown queries, this chat-bot can search the internet and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning tech-niques of Latent Semantic Analysis(LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and Tex-tRank are reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot im-proves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers and patents etc. more effi-ciently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.

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Contribution to Semantic Analysis of Arabic Language

Advances in Artificial Intelligence ◽

10.1155/2012/620461 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Anis Zouaghi ◽

Mounir Zrigui ◽

Georges Antoniadis ◽

Laroussi Merhbene

Keyword(s):

Information Retrieval ◽

Semantic Analysis ◽

String Matching ◽

Ambiguous Word ◽

Arabic Language ◽

New Approach ◽

Matching Algorithm ◽

Ambiguous Words ◽

Lesk Algorithm ◽

Context Of Use

We propose a new approach for determining the adequate sense of Arabic words. For that, we propose an algorithm based on information retrieval measures to identify the context of use that is the closest to the sentence containing the word to be disambiguated. The contexts of use represent a set of sentences that indicates a particular sense of the ambiguous word. These contexts are generated using the words that define the senses of the ambiguous words, the exact string-matching algorithm, and the corpus. We use the measures employed in the domain of information retrieval, Harman, Croft, and Okapi combined to the Lesk algorithm, to assign the correct sense of those proposed.

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Kernel latent semantic analysis using an information retrieval based kernel

Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09 ◽

10.1145/1645953.1646214 ◽

2009 ◽

Cited By ~ 1

Author(s):

Laurence A.F. Park ◽

Kotagiri Ramamohanarao

Keyword(s):

Information Retrieval ◽

Latent Semantic Analysis ◽

Semantic Analysis

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BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

Journal of Science and Technology - IUH ◽

10.46242/jst-iuh.v47i05.761 ◽

2021 ◽

Vol 47 (05) ◽

Author(s):

NGUYỄN CHÍ HIẾU

Keyword(s):

Information Retrieval ◽

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Question Answering ◽

Semantic Analysis ◽

Knowledge Graph ◽

Question Answering Systems ◽

Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents. We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

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ALBIS

Sociotechnical Enterprise Information Systems Design and Integration ◽

10.4018/978-1-4666-3664-4.ch012 ◽

2013 ◽

pp. 188-206

Author(s):

Lerina Aversano ◽

Carmine Grasso ◽

Maria Tortorella

Keyword(s):

Information Retrieval ◽

Software Maintenance ◽

Business Processes ◽

Semantic Analysis ◽

Software Systems ◽

Complex Task ◽

Process Performance ◽

Definition Of ◽

Support Software

The evaluation of the alignment level existing between a business process and the supporting software systems is a critical concern for an organization, as the higher the alignment level is, the better the process performance is. Monitoring the alignment implies the characterization of all the items it involves and definition of measures for evaluating it. This is a complex task, and the availability of automatic tools for supporting evaluation and evolution activities may be precious. This chapter presents the ALBIS Environment (Aligning Business Processes and Information Systems), designed to support software maintenance tasks. In particular, the proposed environment allows the modeling and tracing between business and software entities and the measurement of their alignment degree. An information retrieval approach is embedded in ALBIS based on two processing phases including syntactic and semantic analysis. The usefulness of the environment is discussed through two case studies.

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Local and Global Latent Semantic Analysis for Text Categorization

International Journal of Information Retrieval Research ◽

10.4018/ijirr.2014070101 ◽

2014 ◽

Vol 4 (3) ◽

pp. 1-13

Author(s):

Khadoudja Ghanem

Keyword(s):

Information Retrieval ◽

High Precision ◽

Latent Semantic Analysis ◽

Classification Accuracy ◽

Text Categorization ◽

Semantic Analysis ◽

Experimental Results ◽

Semantic Approach ◽

Document Categorization ◽

Second Use

In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved.

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Mining the treasure of palm leaf manuscripts through information retrieval techniques

Digital Library Perspectives ◽

10.1108/dlp-07-2019-0026 ◽

2019 ◽

Vol 35 (3/4) ◽

pp. 146-156

Author(s):

Bhupendra Singh ◽

Neelu Jyoti Ahuja

Keyword(s):

Information Retrieval ◽

Semantic Analysis ◽

Systematic Research ◽

Content Type ◽

Computer Scientists ◽

The Government ◽

To Come ◽

Opening Up ◽

Simple Search ◽

Palm Leaf

Purpose This paper aims to popularize information retrieval from palm leaf manuscripts among computer scientists to make available the guidance of the age-old heritage in shaping the future. Design/methodology/approach With computer technology penetrating every aspect of life, information retrieval algorithms can be exploited to help build a system which can dig into the ocean of knowledge from these manuscripts. Findings The knowledge in them covers all aspects of life. Be it religious beliefs, literature, science, mathematics, or any other. However, due to discontinuation of practice of copying their content on fresh leaves, they now possess a fragile life which needs to be preserved at the earliest. The modern means of digitization can help in their preservation. Research limitations The Government of India and other organizations are doing commendable job of preserving and safeguarding country’s heritage and age-old knowledge system through the movement of digitization. In the years to come, the agonizing problem of manuscripts degradation will be eradicated completely. However, next when it will come to mining the knowledge treasure out of these manuscripts, we would be confronted with another helpless situation. Practical implications The digitization process would capture the manuscripts from present physical palm leaf to digital image form by clicking high-quality pictures. All the text in a palm leaf will be available in the form of images, but on these images, a simple search for any word would not be possible. Originality/value Working towards mining the treasure of knowledge from the palm leaf manuscripts, hordes of challenges have been outlined. Over and above the problem of preventing decay to palm leaf manuscripts is the challenge of deciphering text, image analysis, information retrieval and search. Search is further associated with issues of meaningful and useful extraction through semantic analysis. This paper advocates the dire need for systematic research to be undertaken in this field opening up avenues for past knowledge to guide future prospects in several domains.

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The Research of Information Retrieval Technology Based on Semantic Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.2160 ◽

2014 ◽

Vol 926-930 ◽

pp. 2160-2163

Author(s):

Ming Xu ◽

Yun Ke

Keyword(s):

Information Retrieval ◽

Optimization Algorithm ◽

Semantic Analysis ◽

Retrieval Model ◽

Common Information ◽

Search Terms ◽

Sorting Algorithms ◽

Key Points ◽

The Common ◽

Semantic Expansion

The common information retrieval technology is mainly based on keyword matching and this kind of method only focuse on the optimization algorithm, ignoring the semantic research. This does not solve the fundamental semantic multiplicity, retrieve diversity, related web undetected, sort unstandardized. This paper is a study of these problems arise for the current proposed MIRSA information retrieval model based on semantic analysis. This model consists of the following four main key points: disambiguation method, semantic expansion algorithm, the search terms match strategy, web sorting algorithms. This model can effectively solve the problem of semantic multiplicity, avoid missed relevant pages and reasonably improve the sor of related pages.

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