scholarly journals Ontology-based workflow extraction from texts using word sense disambiguation

2016 ◽  
Author(s):  
Ahmed Halioui ◽  
Petko Valtchev ◽  
Abdoulaye Baniré Diallo
Keyword(s):  
Phylogenetic Analyses ◽  
Word Sense Disambiguation ◽  
Workflow Management ◽  
Extraction Process ◽  
Case Based Reasoning ◽  
Word Sense ◽  
Distributed Solutions ◽  
And Control ◽  
Grid Computation ◽  
Case Based

AbstractThis paper introduces a method for automatic workflow extraction from texts using Process-Oriented Case-Based Reasoning (POCBR). While the current workflow management systems implement mostly different complicated graphical tasks based on advanced distributed solutions (e.g.cloud computing and grid computation), workflow knowledge acquisition from texts using case-based reasoning represents more expressive and semantic cases representations. We propose in this context, an ontology-based workflow extraction framework to acquire processual knowledge from texts. Our methodology extends classic NLP techniques to extract and disambiguate tasks in texts. Using a graph-based representation of workflows and a domain ontology, our extraction process uses a context-based approach to recognize workflow components : data and control flows. We applied our framework in a technical domain in bioinformatics : i.e. phylogenetic analyses. An evaluation based on workflow semantic similarities on a gold standard proves that our approach provides promising results in the process extraction domain. Both data and implementation of our framework are available in :http://labo.bioinfo.uqam.ca/tgrowler.

A Cotton Disease Diagnosis Method Using a Combined Algorithm of Case-Based Reasoning and Fuzzy Logic

2020 ◽  
Author(s):  
Yuhong Dong ◽  
Zetian Fu ◽  
Stevan Stankovski ◽  
Yaoqi Peng ◽  
Xinxing Li
Keyword(s):  
Fuzzy Logic ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Cotton Production ◽  
Scoring Method ◽  
Disease Symptoms ◽  
Diagnosis Method ◽  
And Control ◽  
Combined Algorithm ◽  
Case Based

Abstract In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided symptoms do not fully meet the requirements of CBR. To address this problem, fuzzy logic is incorporated into CBR to allow for more flexible and accurate models. With the help of CBR and fuzzy reasoning, three diagnostic results can be obtained by the cotton disease diagnosis system (CDDS) constructed in this study: success, success but not exact and failure. To verify the reliability of the CDDS and its ability to diagnose cotton diseases, its diagnostic accuracy and stability were analyzed and compared with the results obtained by the traditional expert scoring method. The analysis results reveal that the CDDS can achieve a high diagnostic success rate (above 90%) and better diagnostic stability than the traditional expert scoring method when at least four disease symptoms are input. The CDDS provides an independent and objective source of information to assist farmers in the diagnosis and prevention of cotton diseases.

scholarly journals Emergency Response for COVID-19 Prevention and Control in Urban Rail Transit Based on Case-Based Reasoning Method

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yuning Wang ◽  
Yingzi Liang ◽  
Hui Sun ◽  
Yufei Yang
Keyword(s):  
Emergency Response ◽  
Prevention And Control ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Case Based Reasoning ◽  
Historical Case ◽  
Urban Rail ◽  
Emergency Responses ◽  
And Control ◽  
Case Based

As an important public travel mode, urban rail transit has the characteristics of crowded passengers and closed operation. Safe management of urban rail transit is an important research topic that attracted attention in recent years. This article proposes a decision analysis method based on case-based reasoning, which aims to solve the emergency response problems for the prevention and control of corona virus disease 2019 (COVID-19) in urban rail transit. In this method, first, the historical cases are extracted and filtered by calculating the similarity between the target case and the historical case. A set of similar historical cases is constructed by setting the similarity threshold in advance. Second, comprehensive utility value of emergency response of each similar case is calculated referring to the utility evaluation of emergency response effect and response cost of each similar historical case. On this basis, the emergency plan of the target case is generated by selecting the emergency plans of the similar historical cases corresponding to the maximum comprehensive utility values of the emergency responses. Finally, with the emergency responses of COVID-19 in Tianjin rail transit as the background, this paper explains the feasibility and effectiveness of the proposed method within a case study.

INTRODUCTION OF A MULTIAGENT PARADIGM TO OPTIMIZE A CASE-BASED REASONING SYSTEM DESIGNED TO PERSONALIZE THREE-DIMENSIONAL NUMERICAL REPRESENTATIONS OF HUMAN ORGANS

2014 ◽  
Vol 26 (05) ◽  
pp. 1450060 ◽  
Author(s):  
Julien Henriet ◽  
Christophe Lang
Keyword(s):  
Three Dimensional ◽  
Processing System ◽  
Case Based Reasoning ◽  
Distributed Architecture ◽  
Accidental Exposure ◽  
Human Organs ◽  
Reasoning System ◽  
Numerical Representations ◽  
And Control ◽  
Case Based

The case-based reasoning (CBR) approach consists of retrieving solutions from similar past problems and adapting them to new problems. Interpolation tools can easily be used as adaptation tools in CBR systems. The accuracies of interpolated results depend on the set of known solved problems with which the interpolation tools are previously trained. EquiVox is a CBR-based system designed for retrieving and adapting three-dimensional numerical representations of human organs called phantoms. EquiVox uses an interpolation tool as an adaptation process. These phantoms are used by radiation protection experts to establish dosimetric reports in case of accidental overexposure to radiation. In addition, medical physicians need these phantoms to compute and control exposure to radiation used to treat diseases such as cancerous tumors in hospitals. The present work aims at proposing a distributed architecture for EquiVox so that a user may find a solution as quickly as possible based on the most recent available knowledge of a given community. We have designed a distributed architecture based on a multiagent paradigm and studied the theoretical performance of the new version. The ability of the new system to quickly provide and adapt solutions using the most up-to-date knowledge has been analyzed from a probabilistic angle. In the case of limited and accidental exposure to radiation, the proposed parallel processing system improves the previous and sequential version of EquiVox. Improvements are also obtained in some cases of massive exposure to radiation.

scholarly journals Research on the model for tobacco disease prevention and control based on case-based reasoning and knowledge graph

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1947-1952 ◽  
Author(s):  
Lichuan Gu ◽  
Yingchun Xia ◽  
Xiaohui Yuan ◽  
Chao Wang ◽  
Jun Jiao
Keyword(s):  
Disease Prevention ◽  
Prevention And Control ◽  
Principal Component ◽  
Support Vector ◽  
Knowledge Graph ◽  
Case Based Reasoning ◽  
Long Time ◽  
And Control ◽  
Tobacco Disease ◽  
Case Based

Tobacco is one of the most important economic crops in China. The yield and quality of tobacco reduce severely because of long-time disease invasion. Currently, the main focus of researches on tobacco disease prevention and control is the diagnosis of disease that has occurred, which ignores to predict disease before it outbreaks. Therefore, in this paper, we follow the idea that prediction is used before disease prevention and control and study the model for tobacco disease prevention and control by using knowledge graph and case-based reasoning (CBR). In order to implement the model, we choose tobacco mosaic virus (TMV) as research object and follow the following methods to prevent occurrence of that. At first, a method to predicting environmental factors by using principal component analysis (PCA) and support vector machine (SVM) is proposed. According to the prediction result, knowledge graph and CBR are used to retrieve the most similarity case and finally determine the best solution. Experimental results demonstrate that our model can achieve high accuracy and give the most appropriate scheme for disease prevention and control.

Semantic Encoding of Electronic Documents

2001 ◽  
Vol 6 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Caroline Brun ◽  
Frédérique Segond
Keyword(s):  
Word Sense Disambiguation ◽  
Extraction Process ◽  
Word Sense ◽  
Electronic Documents ◽  
Client Server ◽  
Application Program ◽  
Electronic Dictionary ◽  
Sense Disambiguation ◽  
Linguistic Strategy ◽  
Server Architecture

This paper presents an unsupervised, all-words, word sense disambiguation system for English. The system associates a word with its meaning in a given context using an electronic dictionary as a tagged corpora in order to extract semantic disambiguation rules. The methodology attempts to avoid the data acquisition bottleneck observed in word sense disambiguation techniques. Semantic rules are used as input of a semantic application program encoding a linguistic strategy in order to select the best rule to apply. The semantic rule extraction process as well as the application program is described. The methodology is developed in a client/server architecture, which enables the treatment of large corpora. The evaluation of the system is then detailed and some possible extensions and perspectives are finally proposed.

scholarly journals Building Synsets for Indonesian WordNet using ROCK (Robust Clustering Using Links) Algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 165
Author(s):  
Mubaroq Iqbal ◽  
Moch. Arif Bijaksana ◽  
Widi Astuti
Keyword(s):  
Word Sense Disambiguation ◽  
Extraction Process ◽  
Word Sense ◽  
Lexical Database ◽  
Robust Clustering ◽  
Database Development ◽  
Sense Disambiguation ◽  
The Difference ◽  
Performance Calculation ◽  
F Measure

On the development of Indonesian WordNet, the synonym set is an important part that represents the similarity of meaning between words. Synonym sets are built using the Indonesian Thesaurus as the lexical database. After going through the extraction process from the Indonesian Thesaurus, we will get a synonym set that has a similarity or word sense between words. In general, the difference between WordNet and the dictionary is their main focus, in which the dictionary usually focuses on just one word, while in WordNet the focus is on the meaning of words and connectedness with other words. Explained in previous research, the constructions of synonym sets were done using several approaches, which is clustering to produce synonym sets and WSD (Word Sense Disambiguation). In this article, the approach used to produce synonym sets is the ROCK (Robust Clustering Using Links) algorithm, which uses similarity and link values. The resulting synonym sets will then be used for lexical database development. Therefore, the main focus of this article is to produce synonym sets through the clustering process and calculate their accuracy, using the F-Measure method involving the gold standard for performance calculation and evaluation.

Case-Based Reasoning Approaches for Gas Turbine Trip Diagnosis

2007 ◽  
Author(s):  
Catherine M. Graichen ◽  
William E. Cheetham
Keyword(s):  
Control System ◽  
Gas Turbines ◽  
Primary Sources ◽  
Initial Assessment ◽  
Case Based Reasoning ◽  
Additional Information ◽  
System A ◽  
And Control ◽  
Case Based

Effective maintenance, repair and design improvements of gas turbines require the classification of turbine automatic shutdown events into actionable categories. In particular, analysis is required at two distinct points in the life cycle of a shutdown event. The first evaluation is at the time of the shutdown when an initial assessment of the cause and the appropriate action must be decided as quickly as possible to return the turbine to service. At the time of the event, the primary sources of data are information collected from the sensors and control system. A second assessment is often performed as a post-event evaluation using additional information to validate the cause. General Electric created Case-Based Reasoning (CBR) tools to perform these classifications automatically. The first CBR tool, which works at the time of the event, was deployed in 2004. The second CBR tool was placed in operation in 2006.

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