Study on Information Retrieval Sorting Algorithm in Network-Based Manufacturing Environment

2014 ◽  
Vol 484-485 ◽  
pp. 183-186 ◽  
Author(s):  
Ji Ying Yang ◽  
Bei Zhang ◽  
Yu Mao
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Nonsense Word ◽  
Sorting Algorithm ◽  
Retrieval Performance ◽  
The Core ◽  
Sorting Algorithms ◽  
Response Information ◽  
Pros And Cons ◽  
Core Problem

The core problem of information retrieval is concentrated in the document for the user to retrieve the most relevant sub-set of documents, relying on sorting algorithms on the search results according to relevance sort, sorted the results as the user asked the query response information retrieval performance is determined by many factors, such as to query expressions quality index stemmer nonsense word disabled, query expansion technology, but fundamentally it is determined by the sort function sort function in some Standards document query indicates the degree of matching with the user, and accordingly to make a document with respect to the user's judgment, then the document in accordance with the degree of correlation with respect to the user in descending order, and returns the ordered list of documents as a result of the retrieval the pros and cons of the sorting algorithm directly affect the efficiency of the retrieval.

scholarly journals An ensemble information retrieval method for the biomedical domain (Preprint)

2021 ◽  
Author(s):  
Zhiqiang Liu ◽  
Jingkun Feng ◽  
Zhihao Yang ◽  
Lei Wang
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Text Classification ◽  
Query Expansion ◽  
Ensemble Method ◽  
Classification Model ◽  
Retrieval Performance ◽  
Matching Model ◽  
Ranking List ◽  
Initial Retrieval

BACKGROUND With the development of biomedicine, the number of biomedical documents has increased rapidly, which brings a great challenge for researchers retrieving the information they need. Information retrieval aims to meet this challenge by searching relevant documents from abundant documents based on the given query. However, sometimes the relevance of search results needs to be evaluated from multiple aspects in some specific retrieval tasks and thereby increases the difficulty of biomedical information retrieval. OBJECTIVE This study aims to find a more systematic method to retrieve relevant scientific literature for a given patient. METHODS In the initial retrieval stage, we supplement query terms through query expansion strategies and apply query boosting to obtain an initial ranking list of relevant documents. In the re-ranking phase, we employ a text classification model and relevance matching model to evaluate documents respectively from different dimensions, then we combine the outputs through logistic regression to re-rank all the documents from the initial ranking list. RESULTS The proposed ensemble method contributes to the improvement of biomedical retrieval performance. Comparing with the existing deep learning-based methods, experimental results show that our method achieves state-of-the-art performance on the data collection provided by TREC 2019 Precision Medicine Track. CONCLUSIONS In this paper, we propose a novel ensemble method based on deep learning. As shown in the experiments, the strategies we used in the initial retrieval phase such as query expansion and query boosting are effective. The application of the text classification model and the relevance matching model can better capture semantic context information and improve retrieval performance.

An Information Retrieval Expansion Model Based on Wikipedia

2014 ◽  
Vol 977 ◽  
pp. 464-467
Author(s):  
Li Xin Gan ◽  
Wei Tu
Keyword(s):  
Information Retrieval ◽  
Classification Accuracy ◽  
Query Expansion ◽  
Semantic Network ◽  
Computational Cost ◽  
Retrieval Performance ◽  
Network Information ◽  
Key Technologies ◽  
Query Classification ◽  
Expansion Model

Query expansion is one of the key technologies for improving precision and recall in information retrieval. In order to overcome limitations of single corpus, in this paper, semantic characteristics of Wikipedia corpus is combined with the standard corpus to extract more rich relationship between terms for construction of a steady Markov semantic network. Information of the entity pages and disambiguation pages in Wikipedia is comprehensively utilized to classify query terms to improve query classification accuracy. Related candidates with high quality can be used for query expansion according to semantic pruning. The proposal in our work is benefit to improve retrieval performance and to save search computational cost.

scholarly journals KEBUNTUAN PEMAHAMAN TERHADAP POLEMIK WACANA ISLAM NUSANTARA: KAJIAN DARI PERSPEKTIF MAKNA PRAGMATIK

2020 ◽  
Vol 3 (2) ◽  
pp. 143-154
Author(s):  
Taufik Nurhadi
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Comparative Analysis ◽  
Analysis Method ◽  
The Core ◽  
Pros And Cons ◽  
Worship Practices ◽  
Symbolic Identity ◽  
Constant Comparative Analysis ◽  
Core Problem

This study is aimed at describing of the impasse understanding of the polemic of the Nusantara Islamic discourse. The data were in the form of Indonesian language used in the polemic of the pros and cons of the Nusantara Islam issue, which is published on social media, Youtube. Data collection uses the method of listening with tapping, SBLC, download, and note techniques. The data analysis method used is Constant Comparative Analysis. The results of the analysis showed that there was a deadlock in understanding the issues of the Nusantara Islamic discourse regarding 4 things, namely symbolic identity, rejection in terms of terms, classic rivalry between tribes, and culture as the core problem. The deadlock of understanding was triggered by long competition between the two circles of Muhammadiyah and Nahdatul Ulama in viewing worship practices from different perspectives.

Research on Bayesian Network Retrieval Model in Electronic Commerce

2012 ◽  
Vol 263-266 ◽  
pp. 2726-2731
Author(s):  
Shuang Zhao ◽  
Yong Min Lin
Keyword(s):  
Information Retrieval ◽  
Bayesian Network ◽  
Electronic Commerce ◽  
Theoretical Basis ◽  
Query Expansion ◽  
Posterior Probability ◽  
Expansion Method ◽  
Analysis Method ◽  
Retrieval Model ◽  
Retrieval Performance

On the analysis about the problem of information retrieval in the Electronic Commerce environment, this paper presents a Bayesian network retrieval model. This model adopts the topology of three layer nodes, and uses co-occurrence analysis method to mine relationships between the terms. A query expansion method according to domain ontology is used to extend the users query. Finally the similarity between the document and the query can be measured by calculating the posterior probability of relevance of the document. Experiments show that the model which will provide a theoretical basis for the problem of information retrieval in the Electronic Commerce environment can effectively improve the retrieval performance.

In a word, I’ve been there

2019 ◽  
pp. 246-256
Author(s):  
A. K. Zholkovsky
Keyword(s):  
The State ◽  
Female Characters ◽  
Political Situation ◽  
The Core ◽  
The Relationship ◽  
Core Problem ◽  
Typical Solution

In his article, A. Zholkovsky discusses the contemporary detective mini-series Otlichnitsa [A Straight-A Student], which mentions O. Mandelstam’s poem for children A Galosh [Kalosha]: more than a fleeting mention, this poem prompts the characters and viewers alike to solve the mystery of its authorship. According to the show’s plot, the fact that Mandelstam penned the poem surfaces when one of the female characters confesses her involvement in his arrest. Examining this episode, Zholkovsky seeks structural parallels with the show in V. Aksyonov’s Overstocked Packaging Barrels [Zatovarennaya bochkotara] and even in B. Pasternak’s Doctor Zhivago [Doktor Zhivago]: in each of those, a member of the Soviet intelligentsia who has developed a real fascination with some unique but unattainable object is shocked to realize that the establishment have long enjoyed this exotic object without restrictions. We observe, therefore, a typical solution to the core problem of the Soviet, and more broadly, Russian cultural-political situation: the relationship between the intelligentsia and the state, and the resolution is not a confrontation, but reconciliation.

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

PERANCANGAN IDENTITAS VISUAL REBRANDING CITRA PERUSAHAAN CEMARA CERAMICS (DESIGN OF CEMARA CERAMICS VISUAL REBRANDING IDENTITY)

2021 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Anak Agung Ngurah Gede Marhendra ◽  
Agung Eko Budiwaspada ◽  
Sangayu Ketut Laksemi Nilotama
Keyword(s):  
Design Thinking ◽  
Corporate Identity ◽  
Corporate Image ◽  
Visual Design ◽  
Stage Design ◽  
The Core ◽  
Visual Identity ◽  
The Creation ◽  
Strategy Design ◽  
Core Problem

<p>Abstract Design of Cemara Ceramics Visual Rebranding Identity aims to produce a concept strategy and visual rebranding of the Cemara Ceramics company and produce a Cemara Ceramics rebranding visual identity design in order to encourage the creation of a new identity image. The method in this design uses a 5-stage Design Thinking approach, namely Empathize, Define, Ideate, Prototype and Test. The result achieved is the design of the new Cemara Ceramics corporate identity. With the use of the design thinking method in this research, various problems related to the company image of Cemara Ceramics can be found. The core problem obtained is how to design a strategy and concept of visual identity rebranding to encourage the creation of a new corporate image of Cemara Ceramics.</p><p>Keywords: visual rebranding identity, concept strategy, design thinking</p><p>Abstrak Perancangan Identitas Visual Rebranding Citra Perusahaan Cemara Ceramics ini bertujuan untuk menghasilkan strategi konsep dan visual rebranding perusahaan Cemara Ceramics serta menghasilkan rancangan identitas visual rebranding Cemara Ceramics dalam rangka mendorong terciptanya citra identitas yang baru. Metode dalam perancangan ini menggunakan pendekatan 5 tahapan Design Thinking yaitu Empathize, Define, Ideate, Prototype dan Test. Hasil yang dicapai yaitu rancangan corporate identity Cemara Ceramics yang baru. Dengan adanya penggunaan metode design thinking pada penelitian ini dapat menemukan berbagai permasalahan terkait citra perusahaan Cemara Ceramics. Permasalahan inti yang didapat yaitu mengenai bagaimana merancang strategi dan konsep identitas visual rebranding untuk mendorong terciptanya citra baru perusahaan Cemara Ceramics.</p><p>Kata kunci: identitas visual rebranding, strategi konsep dan visual, design thinking</p>

Analisis Kinerja Algoritma Quick Double Merge Sort Paralel Menggunakan openMP

2020 ◽  
Vol 11 (2) ◽  
pp. 95-102
Author(s):  
I Nyoman Aditya Yudiswara ◽  
Abba Suganda
Keyword(s):  
Parallel Algorithms ◽  
The Other ◽  
Sorting Algorithm ◽  
Data Parallelism ◽  
Parallel Sorting ◽  
Sequential Algorithms ◽  
Sorting Algorithms ◽  
Merge Sort ◽  
Sort Algorithm ◽  
Quick Sort

Processor technology currently tends to increase the number of cores more than increasing the clock speed. This development is very useful and becomes an opportunity to improve the performance of sequential algorithms that are only done by one core. This paper discusses the sorting algorithm that is executed in parallel by several logical CPUs or cores using the openMP library. This algorithm is named QDM Sort which is a combination of sequential quick sort algorithm and double merge algorithm. This study uses a data parallelism approach to design parallel algorithms from sequential algorithms. The data used in this study are the data that have not been sorted and also the data that has been sorted is integer type which is stored in advance in a file. The parameter measured to determine the performance of the QDM Sort algorithm is speedup. In a condition where a large amount of data is above 4096 and the number of threads in QDM Sort is the same as the number of logical CPUs, the QDM Sort algorithm has a better speedup compared to the other parallel sorting algorithms discussed in this study. For small amounts of data it is still better to use sequential sorting algorithm.

Context Window Based Co-occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval

2015 ◽  
Vol 5 (4) ◽  
pp. 31-45 ◽  
Author(s):  
Jagendra Singh ◽  
Aditi Sharan
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Contextual Information ◽  
Optimal Combination ◽  
Query Term ◽  
Benchmark Data ◽  
First Pass ◽  
Baseline Approach ◽  
Pseudo Relevance Feedback

Pseudo-relevance feedback (PRF) is a type of relevance feedback approach of query expansion that considers the top ranked retrieved documents as relevance feedback. In this paper the authors focus is to capture the limitation of co-occurrence and PRF based query expansion approach and the authors proposed a hybrid method to improve the performance of PRF based query expansion by combining query term co-occurrence and query terms contextual information based on corpus of top retrieved feedback documents in first pass. Firstly, the paper suggests top retrieved feedback documents based query term co-occurrence approach to select an optimal combination of query terms from a pool of terms obtained using PRF based query expansion. Second, contextual window based approach is used to select the query context related terms from top feedback documents. Third, comparisons were made among baseline, co-occurrence and contextual window based approaches using different performance evaluating metrics. The experiments were performed on benchmark data and the results show significant improvement over baseline approach.

Sign in / Sign up

Export Citation Format

Share Document