scholarly journals Using Ontologies in Failure Analysis

2021 ◽  
Author(s):  
Anna Safont-Andreu ◽  
Christian Burmer ◽  
Konstantin Schekotihin
Keyword(s):  
Information Retrieval ◽  
Text Classification ◽  
Fault Analysis ◽  
Software Systems ◽  
Complex Task ◽  
Analysis Process ◽  
Electrical Engineers ◽  
Workflow Verification ◽  
Knowledge Intensive ◽  
Classification Information

Abstract Fault analysis is a complex task that requires electrical engineers to perform various analyses to detect and localize a physical defect. The analysis process is very knowledge-intensive and must be precisely documented to report the issue to customers as well as to ensure the best possible reuse of the acquired experience in similar future analyses. However, writing unambiguous documentation can be complicated for many reasons, such as selecting details and results to be presented in a report, or the naming of terms and their definition. To avoid some of these issues, FA engineers must agree on a clearly defined terminology specifying methods, physical faults and their electrical signatures, tools, and relations between them. Moreover, to allow FA software systems to use this terminology, it must be stored in a format that can be interpreted similarly by both engineers and software. This paper presents an approach that solves these challenges by using an ontology describing FA-relevant terminology using a logic-based representation. The latter guarantees the same interpretation of the defined terms by engineers and software systems, which can use it to perform various tasks like text classification, information retrieval, or workflow verification.

ALBIS

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.

A new similarity measure for vector space models in text classification and information retrieval

2020 ◽  
pp. 016555152096805
Author(s):  
Mete Eminagaoglu
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Similarity Measure ◽  
Text Classification ◽  
Pearson Correlation ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Manhattan Distance ◽  
Vector Space Models ◽  
Classification Information

There are various models, methodologies and algorithms that can be used today for document classification, information retrieval and other text mining applications and systems. One of them is the vector space–based models, where distance metrics or similarity measures lie at the core of such models. Vector space–based model is one of the fast and simple alternatives for the processing of textual data; however, its accuracy, precision and reliability still need significant improvements. In this study, a new similarity measure is proposed, which can be effectively used for vector space models and related algorithms such as k-nearest neighbours ( k-NN) and Rocchio as well as some clustering algorithms such as K-means. The proposed similarity measure is tested with some universal benchmark data sets in Turkish and English, and the results are compared with some other standard metrics such as Euclidean distance, Manhattan distance, Chebyshev distance, Canberra distance, Bray–Curtis dissimilarity, Pearson correlation coefficient and Cosine similarity. Some successful and promising results have been obtained, which show that this proposed similarity measure could be alternatively used within all suitable algorithms and models for information retrieval, document clustering and text classification.

scholarly journals Towards Bengali Word Embedding: Corpus Creation, Intrinsic and Extrinsic Evaluations

2020 ◽  
Author(s):  
Md. Rajib Hossain ◽  
Mohammed Moshiul Hoque
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Language Processing ◽  
Text Classification ◽  
Feature Space ◽  
Vector Representation ◽  
Word Similarity ◽  
Corpus Creation ◽  
Classification Information ◽  
Correlation Accuracy

Distributional word vector representation orword embedding has become an essential ingredient in many natural language processing (NLP) tasks such as machine translation, document classification, information retrieval andquestion answering. Investigation of embedding model helps to reduce the feature space and improves textual semantic as well as syntactic relations.This paper presents three embedding techniques (such as Word2Vec, GloVe, and FastText) with different hyperparameters implemented on a Bengali corpusconsists of180 million words. The performance of the embedding techniques is evaluated with extrinsic and intrinsic ways. Extrinsic performance evaluated by text classification, which achieved a maximum of 96.48% accuracy. Intrinsic performance evaluatedby word similarity (e.g., semantic, syntactic and relatedness) and analogy tasks. The maximum Pearson (ˆr) correlation accuracy of 60.66% (Ssˆr) achieved for semantic similarities and 71.64% (Syˆr) for syntactic similarities whereas the relatedness obtained 79.80% (Rsˆr). The semantic word analogy tasks achieved 44.00% of accuracy while syntactic word analogy tasks obtained 36.00%

Using Association Rules for Query Reformulation

Data Mining ◽  
2013 ◽  
pp. 503-514
Author(s):  
Ismaïl Biskri ◽  
Louis Rompré
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Association Rules ◽  
Text Classification ◽  
Large Volume ◽  
Query Reformulation ◽  
Long Time ◽  
Hidden Knowledge

In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.

The Productivity Challenge Facing the Global Hospitality Industry

2016 ◽  
pp. 223-238
Author(s):  
Sigbjørn L. Tveteraas ◽  
Martin Falk
Keyword(s):  
Empirical Research ◽  
Hospitality Industry ◽  
Cost Minimization ◽  
Software Systems ◽  
Productivity Analysis ◽  
Measurement Issues ◽  
Hospitality Organizations ◽  
Research Findings ◽  
Knowledge Intensive ◽  
Hotel Managers

This chapter introduces the global productivity challenge facing the hospitality industry. Global competition in the hospitality industry has led to increasing pressure on profit levels. To leverage profits hotels increasingly are forced to evaluate their operational performance. Specifically, the global productivity challenge entails that hotel managers to a greater extent must encompass a cost minimization perspective. With the integration of productivity-enhancing software systems in hospitality organizations hotels are becoming increasingly knowledge intensive. This chapter discuss measurement issues, productivity analysis and relevant research findings from empirical research. The empirical research on hotel productivity shows that there are many factors to keep in mind for managers that wish to improve productivity in their organizations. Hopefully this chapter will contribute to clear up the meaning of concepts and broadened the perspective of how productivity are related to all parts of the hospitality enterprise.

scholarly journals Temporal specificity-based text classification for information retrieval

2018 ◽  
Vol 26 (6) ◽  
pp. 2916-2927 ◽  
Author(s):  
Shafiq Ur Rehman KHAN ◽  
Muhammad Arshad ISLAM ◽  
Muhammad ALEEM ◽  
Muhammad Azhar IQBAL
Keyword(s):  
Information Retrieval ◽  
Text Classification ◽  
Temporal Specificity

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.

scholarly journals processo de análise de assunto na catalogação em bibliotecas universitárias para a proposta de normalização

2014 ◽  
Vol 8 (1/2) ◽  
Author(s):  
Franciele Marques REDIGOLO
Keyword(s):  
Information Retrieval ◽  
Information Needs ◽  
Search Strategy ◽  
Ethnographic Research ◽  
Verbal Protocol ◽  
University Libraries ◽  
Subject Analysis ◽  
Analysis Process ◽  
The Subject ◽  
Selection Of

The subject analysis is an intellectual stage of subject cataloging process in which the cataloguer is subject to internal and external inferences. The result of the subject analysis process is reflected directly in information retrieval because it must be compatible with the user's information needs during the formulation of your search strategy for subject in the catalog. Despite the standardization have the function to clarify and systematize procedures performed by various professionals at the same time, it is observed that it is necessary to update them and improve them. Accordingly, there was observational study of the subject analysis process when cataloging in university libraries in order to provide support for the improvement and updating of standards and procedures the subject analysis, the documentary reading, the identification and selection of concepts. Observation was performed with individual verbal protocol application and ethnographic research in 16 university libraries, 12 in Brazil and 4 in Spain. And as a result it was observed that the absence of methodological tools causes the cataloging develop their own methods, such as the recovery of subjects recovered in cooperation catalogs.

scholarly journals Improving the Accuracy of Text Classification using Stemming Method, A Case of Non-formal Indonesian Conversation

2020 ◽  
Author(s):  
Rianto Rianto ◽  
Achmad Benny Mutiara ◽  
Eri Prasetyo Wibowo ◽  
Paulus Insap Santosa
Keyword(s):  
Support Vector Machine ◽  
Information Retrieval ◽  
Text Classification ◽  
Experimental Evaluation ◽  
Hate Speech ◽  
Text Processing ◽  
High Accuracy ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Text Data

Abstract Stemming has long been used in data pre-processing in information retrieval, which aims to make affix words into root words. However, there are not many stemming methods for non-formal Indonesian text processing. The existing stemming method has high accuracy for formal Indonesian, but low for non-formal Indonesian. Thus, the stemming method which has high accuracy for non-formal Indonesian classifier model is still an open-ended challenge. This study introduces a new stemming method to solve problems in the non-formal Indonesian text data pre-processing. Furthermore, this study aims to provide comprehensive research on improving the accuracy of text classifier models by strengthening on stemming method. Using the Support Vector Machine algorithm, a text classifier model is developed, and its accuracy is checked. The experimental evaluation was done by testing 550 datasets in Indonesian using two different stemming methods. The results show that using the proposed stemming method, the text classifier model has higher accuracy than the existing methods with a score of 0.85 and 0.73, respectively. In the future, the proposed stemming method can be used to develop the Indonesian text classifier model which can be used for various purposes including text clustering, summarization, detecting hate speech, and other text processing applications.

Sign in / Sign up

Export Citation Format

Share Document