On Generalized Vector Space Model in Information Retrieval

1985 ◽  
Vol 8 (2) ◽  
pp. 253-267
Author(s):  
S.K.M. Wong ◽  
Wojciech Ziarko
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
A Priori ◽  
Vector Space Model ◽  
Smart System ◽  
Space Model ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Index Terms ◽  
Minimal Modification

In information retrieval, it is common to model index terms and documents as vectors in a suitably defined vector space. The main difficulty with this approach is that the explicit representation of term vectors is not known a priori. For this reason, the vector space model adopted by Salton for the SMART system treats the terms as a set of orthogonal vectors. In such a model it is often necessary to adopt a separate, corrective procedure to take into account the correlations between terms. In this paper, we propose a systematic method (the generalized vector space model) to compute term correlations directly from automatic indexing scheme. We also demonstrate how such correlations can be included with minimal modification in the existing vector based information retrieval systems.

scholarly journals Temu Kembali Informasi pada Soal Ujian dengan Rencana Pembelajaran Menggunakan Vector Space Model

2021 ◽  
Vol 5 (1) ◽  
pp. 63-68
Author(s):  
Amalia Beladinna Arifa ◽  
Gita Fadila Fitriana ◽  
Ananda Rifkiy Hasan
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Space Model ◽  
Learning Plan ◽  
Model Method ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Calculation Results ◽  
The Subject

One way to find out the quality of exam questions is by looking at the rules for writing exam questions made based on the subject or discussion contained in the learning plan document. Therefore, the exam questions that are arranged must be adjusted to the main material in each subject learning achievement. This study discusses the implementation of the concept in information retrieval systems using the Vector Space Model method. The Vector Space Model method has an advantage in query matching because it is able to match only part of the query with existing documents. In addition, the Vector Space Model method is also easy to adapt by adjusting parameters, including weighting parameters. The weighting calculation for each term that appears in the document uses TF-IDF. The purpose of this study is to design an information retrieval system to find the suitability of the exam question query with the subject contained in the learning plan document. The suitability is sorted based on the similarity value of the calculation results, from the largest value to the smallest value in the form of a percentage.

scholarly journals Web-Based Information Search System Development Using a Semantic Network

2021 ◽  
pp. 347-352
Author(s):  
Joko Samodra ◽  
Primardiana Hermilia Wijayati ◽  
. Rosyidah ◽  
Andika Agung Sutrisno
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Semantic Network ◽  
Vector Space Model ◽  
Latent Semantic Indexing ◽  
Search System ◽  
Web Based ◽  
Space Model ◽  
Retrieval Systems ◽  
Information Retrieval Systems

Finding information from a large collection of documents is a complicated task; therefore, we need a method called an information retrieval system. Several models that have been used in information retrieval systems include the Vector Space Model (VSM), DICE Similarity, Latent Semantic Indexing (LSI), Generalized Vector Space Model (GVSM), and semantic-based information retrieval systems. The purpose of this study was to develop a semantic network-based search system that will find information based on keywords and the semantic relationship of keywords provided by users. This cannot be done by most search systems that only work based on keyword matching or similarities. The Waterfall development model was used, which divides the development stages into five steps, namely: (1) requirements analysis and definition; (2) system and software design; (3) implementation and unit testing; (4) integration and system testing; and (5) operation and maintenance. The developed system/application was tested by trying to find information based on various combinations of keywords provided by the user. The results showed that the system can find information that matches the keyword, and other relevant information based on the semantic relationships of these keywords. Keywords: information retrieval, search system, semantic network, web-based application

A Unified Model in Information Retrieval1

1987 ◽  
Vol 10 (1) ◽  
pp. 35-55
Author(s):  
S.K.M. Wong ◽  
Wojciech Ziarko
Keyword(s):  
Information Retrieval ◽  
Boolean Algebra ◽  
Vector Space ◽  
Vector Space Model ◽  
Retrieval Model ◽  
Space Model ◽  
Vector Processing ◽  
New Information ◽  
Unified View ◽  
Index Terms

We introduce a new information retrieval model, the generalized vector space model (GVSM). In the GVSM, Boolean-like queries can be transformed into vectors spanned by the atoms of the free Boolean algebra generated by the index terms. Documents can, therefore, be ranked with respect to a query as in the conventional vector space model. Most significantly, the GVSM provides a unified view of the Boolean retrieval and vector-processing systems.

Aplikasi Deteksi Kemiripan Tugas Paper

2017 ◽  
Vol 15 (2) ◽  
pp. 5
Author(s):  
Anthony Anggrawan ◽  
Azhari
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Mean Average Precision ◽  
Average Precision ◽  
Information Searching ◽  
Space Model ◽  
Model Method

Information searching based on users’ query, which is hopefully able to find the documents based on users’ need, is known as Information Retrieval. This research uses Vector Space Model method in determining the similarity percentage of each student’s assignment. This research uses PHP programming and MySQL database. The finding is represented by ranking the similarity of document with query, with mean average precision value of 0,874. It shows how accurate the application with the examination done by the experts, which is gained from the evaluation with 5 queries that is compared to 25 samples of documents. If the number of counted assignments has higher similarity, thus the process of similarity counting needs more time, it depends on the assignment’s number which is submitted.

Aplikasi Rekomendasi Buku Pada Katalog Perpustakaan Universitas Multimedia Nusantara Menggunakan Vector Space Model

2018 ◽  
Vol 9 (2) ◽  
pp. 97-105
Author(s):  
Richard Firdaus Oeyliawan ◽  
Dennis Gunawan
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
Vector Model ◽  
Library Management ◽  
Space Model ◽  
Library Management System ◽  
Index Terms ◽  
Library Catalogue ◽  
Language Sample ◽  
F Measure

Library is one of the facilities which provides information, knowledge resource, and acts as an academic helper for readers to get the information. The huge number of books which library has, usually make readers find the books with difficulty. Universitas Multimedia Nusantara uses the Senayan Library Management System (SLiMS) as the library catalogue. SLiMS has many features which help readers, but there is still no recommendation feature to help the readers finding the books which are relevant to the specific book that readers choose. The application has been developed using Vector Space Model to represent the document in vector model. The recommendation in this application is based on the similarity of the books description. Based on the testing phase using one-language sample of the relevant books, the F-Measure value gained is 55% using 0.1 as cosine similarity threshold. The books description and variety of languages affect the F-Measure value gained. Index Terms—Book Recommendation, Porter Stemmer, SLiMS Universitas Multimedia Nusantara, TF-IDF, Vector Space Model

scholarly journals The Performance of Boolean Retrieval and Vector Space Model in Textual Information Retrieval

2017 ◽  
Vol 11 (1) ◽  
pp. 33 ◽  
Author(s):  
Budi Yulianto ◽  
Widodo Budiharto ◽  
Iman Herwidiana Kartowisastro
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Experimental Results ◽  
Inverted Index ◽  
Exact Results ◽  
Textual Information ◽  
Space Model ◽  
Corpus Data

Boolean Retrieval (BR) and Vector Space Model (VSM) are very popular methods in information retrieval for creating an inverted index and querying terms. BR method searches the exact results of the textual information retrieval without ranking the results. VSM method searches and ranks the results. This study empirically compares the two methods. The research utilizes a sample of the corpus data obtained from Reuters. The experimental results show that the required times to produce an inverted index by the two methods are nearly the same. However, a difference exists on the querying index. The results also show that the numberof generated indexes, the sizes of the generated files, and the duration of reading and searching an index are proportional with the file number in the corpus and thefile size.

scholarly journals Analysis of a Vector Space Model, Latent Semantic Indexing and Formal Concept Analysis for Information Retrieval

2012 ◽  
Vol 12 (1) ◽  
pp. 34-48 ◽  
Author(s):  
Ch. Aswani Kumar ◽  
M. Radvansky ◽  
J. Annapurna
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Formal Concept Analysis ◽  
Vector Space Model ◽  
Latent Semantic Indexing ◽  
Concept Analysis ◽  
Formal Concept ◽  
Semantic Indexing ◽  
Space Model ◽  
Classical Vector

Abstract Latent Semantic Indexing (LSI), a variant of classical Vector Space Model (VSM), is an Information Retrieval (IR) model that attempts to capture the latent semantic relationship between the data items. Mathematical lattices, under the framework of Formal Concept Analysis (FCA), represent conceptual hierarchies in data and retrieve the information. However, both LSI and FCA use the data represented in the form of matrices. The objective of this paper is to systematically analyze VSM, LSI and FCA for the task of IR using standard and real life datasets.

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