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CACDA: A knowledge graph for a context-aware cognitive design assistant
CACDA: یک گراف دانش برای دستیار طراحی شناختی زمینه آگاه-2021 The design of complex engineered systems highly relies on a laborious zigzagging between computeraided design (CAD) software and design rules prescribed by design manuals. Despite the emergence of
knowledge management techniques (ontology, expert system, text mining, etc.), companies continue to
store design rules in large and unstructured documents. To facilitate the integration of design rules and
CAD software, we propose a knowledge graph that structures a large set of design rules in a computable
format. The knowledge graph organises entities of design rules (nodes), relationships among design rules
(edges), as well as contextual information. The categorisation of entities and relationships in four subcontexts: semantic, social, engineering, and IT – facilitates the development of the data model, especially
the definition of the “design context” concept. The knowledge graph paves the way to a context-aware
cognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cognitive design assistant will capture the design context in near real time and run reasoning operations
on the knowledge graph to extend traditional CAD capabilities, such as the recommendation of design
rules, the verification of design solutions, or the automation of design routines. Our validation experiment shows that the current version of the context-aware cognitive design assistant is more efficient
than the traditional document-based design. On average, participants using an unstructured design rules
document have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precision
score. Finally, designers supported by the design assistant spend more time designing than searching for
applicable design rules compared to the traditional design approach.
keywords: قانون طراحی | نمودار دانش | مدیریت دانش | آگاهی متقابل | دستیار شناختی | Design rule | Knowledge graph | Knowledge management | Context-awareness | Cognitive assistant |
مقاله انگلیسی |
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Text mining of industry 4:0 job advertisements
استخراج متن آگهی های شغلی صنعت 4:0-2020 Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements
is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this
paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job
advertisements, which are often used as a channel for collecting relevant information about the required
knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements,
related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements.
Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior
level management positions and mainly came from the United States and Germany. Text mining analysis resulted
in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to
Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production
design and production control. The second group of job profiles was focused on more general knowledge areas,
which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software.
Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher
educational institutions, human resources professionals, as well as experts that are already employed or aspire to
be employed in Industry 4.0 organizations, would benefit from the results of our analysis. Keywords: Human resource management | Text mining | Job profiles | Big data analytics | Industry 4.0 | Education | Smart factory |
مقاله انگلیسی |
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Emotional Text Mining: Customer profiling in brand management
متن کاوی عاطفی: نمایه سازی مشتری در مدیریت برند-2020 The widespread use of the Internet and the constant increase in users of social media platforms has made a large amount of textual data available. This represents a valuable source of information about the changes in people’s opinions and feelings. This paper presents the application of Emotional Text Mining (ETM) in the field of brand management. ETM is an unsupervised procedure aiming to profile social media users. It is based on a bottom-up approach to classify unstructured data for the identification of social media users’ representations and sentiments about a topic. It is a fast and simple procedure to extract meaningful information from a large collection of texts. As customer profiling is relevant for brand management, we illustrate a business application of ETM on Twitter messages concerning a well-known sportswear brand in order to show the potential of this procedure, high- lighting the characteristics of Twitter user communities in terms of product preferences, representations, and sentiments. Keywords: Emotional Text Mining | Brand management | Twitter | Network analysis | Customer profiling |
مقاله انگلیسی |
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An empirical case study on Indian consumers sentiment towards electric vehicles: A big data analytics approach
یک مطالعه موردی تجربی در مورد احساسات مصرف کنندگان هندی نسبت به وسایل نقلیه برقی: یک رویکرد تحلیل داده های بزرگ-2020 Today, climate change due to global warming is a significant concern to all of us. Indias rate of greenhouse gas
emissions is increasing day by day, placing India in the top ten emitters in the world. Air pollution is one of the
significant contributors to the greenhouse effect. Transportation contributes about 10% of the air pollution in
India. The Indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles.
But, success depends on consumers sentiment, perception and understanding towards Electric Vehicles (EV).
This case study tried to capture the feeling, attitude, and emotions of Indian consumers towards electric vehicles.
The main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for
them), marketers (for determining what features should be advertised) and manufacturers (for deciding what
features should be improved) using Deep Learning techniques (e.g Doc2Vec Algorithm, Recurrent Neural
Network (RNN), Convolutional Neural Network (CNN)). Due to the very nature of social media data, big data
platform was chosen to analyze the sentiment towards EV. Deep Learning based techniques were preferred over
traditional machine learning algorithms (Support Vector Machine, Logistic regression and Decision tree, etc.)
due to its superior text mining capabilities. Two years data (2016 to 2018) were collected from different social
media platform for this case study. The results showed the efficiency of deep learning algorithms and found CNN
yield better results in-compare to others. The proposed optimal model will help consumers, designers and
manufacturers in their decision-making capabilities to choose, design and manufacture EV. Keywords: Electric vehicles | Deep learning | Big data | Sentiment analysis | India |
مقاله انگلیسی |
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Business analytics for strategic management: Identifying and assessing corporate challenges via topic modeling
تجزیه و تحلیل کسب و کار برای مدیریت استراتژیک: شناسایی و ارزیابی چالش های شرکت از طریق مدل سازی موضوع-2020 Strategic management requires an assessment of a firms internal and external environments. Our work extends the body of management tools (e.g., SWOT analysis or growth-share matrix) by proposing an automated text mining framework. Here we draw on narrative materials from firms (e.g., financial disclosures) and perform topic modeling so as to identify the key issues faced by an organization. We then quantify the use of language along two dimensions: risk and optimism. This reveals a firms strengths and weaknesses by identifying business units, activities, and processes subject to risk, while also comparing it with competitors or the market. Keywords: Business analytics | Text mining | Firm performance | Topic modeling | Latent Dirichlet allocation | Strategic management |
مقاله انگلیسی |
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How can the maritime industry meet Sustainable Development Goals? An analysis of sustainability reports from the social entrepreneurship perspective
صنعت دریایی چگونه می تواند اهداف توسعه پایدار را برآورده کند؟ تجزیه و تحلیل گزارش های پایداری از دیدگاه کارآفرینی اجتماعی-2020 Sustainability Development Goals (SDGs) are a comprehensive agenda agreed upon globally that aims to stimulate actions towards economic, environmental and social sustainability. Being one of the key stakeholders, the international maritime industry plays an important role in contributing to global sustainability. By applying the concept of social entrepreneurship (SE), this study aims to examine (1) the basic and extended responsibilities (SDG 1–SDG 16) and (2) the potential collaborations within the value chain (SDG 17) concerning SDG implementation in maritime industry. To achieve these, we conduct a content analysis of sustainability reports published by container shipping liners and terminal operators from 2016 to 2019. More specifically, manual text classification is adopted to categories the text content of sustainability reports based on 17 SDGs, and automatic text mining is employed to further identify the key roles of maritime industry related to each SDG. A unified framework is proposed, which points to varied motives and levels of comprehensiveness of the sustainability efforts by the maritime industry. This framework reveals the theoretic process of maritime industrys transitional involvement in sustainability from the SE perspective. It also creates managerial implications regarding the re- source allocation strategies by maritime industry in meeting SDGs. Keywords: Sustainable Development Goals (SDGs) | Maritime Container shipping | Social entrepreneurship | Limited/extended view of corporate citizenship | Text mining |
مقاله انگلیسی |
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What can the news tell us about the environmental performance of tourist areas? A text mining approach to China’s National 5A Tourist Areas
چه خبرهایی می تواند از عملکرد زیست محیطی مناطق توریستی به ما بگوید؟ یک رویکرد استخراج متن به مناطق گردشگری ملی 5A در چین-2020 This study aims to evaluate the environmental performance status of tourist areas and explore the influencing
factors using text mining of web news. As the leading tourist attractions in China, the National 5A Tourist Areas
face severe environmental challenges, and were hence chosen to exemplify the rapid assessment approach in the
big data era. This study used over 1,300,000 words from online news sources and assessed the environmental
performance of 120 National 5A Tourist Areas to conclude that (1) water is the most impacted environmental
resource; (2) tourist area environmental performance can be classified into (a) environmental pollution, (b)
ecological and resource pressure, (c) landscape character issues and (d) others; and (3) the primary factors
influencing the environment are tourism and business operating activities, with the tourist areas’ environmental
performance types being strongly related to their spatial locations and weakly related to their resource types. By
comparing the environmental performance types in this paper with related research the effectiveness of this
study’s approach is validated. These conclusions and this approach can provide guidelines and tools for environmental
assessment and promote tourist area management Keywords: Environmental impact assessment | Environmental pollution | Tourist environment | Web content | Text mining | China’s National 5A Tourist Areas |
مقاله انگلیسی |
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Data mining for smart legal systems
داده کاوی برای سیستمهای حقوقی هوشمند-2019 Smart legal systems carry immense potential to provide legal community and public with valuable insights using legal data. These systems can consequently help in analyzing and mitigating various social issues. In Pakistan, since last couple of years, courts have been reporting judgments online for public consumption. This public data, once processed, can be utilized for betterment of society and policy making in Pakistan. This study takes the first step to realize smart legal system by extracting various entities such as dates, case numbers, reference cases, person names, etc. from legal judgments. To automatically ex- tract these entities, the primary requirement is to construct dataset using legal judgments. Hence, firstly annotation guidelines are prepared followed by preparation of annotated dataset for extraction of various legal entities. Experiments conducted using variety of datasets, multiple algorithms and annotation schemes, resulted into maximum F1-score of 91.51% using Conditional Random Fields Keywords: Information extraction | Named Entity Recognition | Legal data | Text mining | Civil law proceeding |
مقاله انگلیسی |
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Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer
بررسی عوارض جانبی دارویی در خلاصه تخلیه سوابق پزشکی الکترونیکی با استفاده از Readpeer-2019 Background: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts
to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through
all discharge summaries to find cases of drug-adverse event (AE) relations.
Purpose: The objective of this paper is to develop a natural language processing (NLP) framework to detect drug-
AE relations from unstructured hospital discharge summaries.
Basic procedures: An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE)
terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms
and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert
review of discharge summaries from a tertiary hospital in Singapore in 2011.
Main findings: A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries)
by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries
were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and
AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600
records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations.
Principal conclusions: Adverse drug reactions are a significant contributor to health care costs and utilization.
Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an
important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug
products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for
performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting
the results of the algorithm as part of an iterative machine learning process, is an important step towards use of
hospital discharge summaries for an active pharmacovigilance program Keywords: Pharmacovigilance | Text mining | Electronic medical records | Expert system | Adverse drug reaction |
مقاله انگلیسی |
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Validation of text-mining and content analysis techniques using data collected from veterinary practice management software systems in the UK
اعتبارسنجی تکنیک های استخراج متن و تجزیه و تحلیل محتوا با استفاده از داده های جمع آوری شده از سیستم های نرم افزار مدیریت دامپزشکی در انگلستان-2019 Electronic patient records from practice management software systems have been used extensively in medicine
for the investigation of clinical problems leading to the creation of decision support frameworks. To date,
technologies that have been utilised for this purpose such as text mining and content analysis have not been
employed significantly in veterinary medicine.
The aim of this research was to pilot the use of content analysis and text-mining software for the synthesis and
analysis of information extracted from veterinary electronic patient records. The purpose of the work was to be
able to validate this approach for future employment across a number of practices for the purposes of practice
based research. The approach utilised content analysis (Prosuite) and text mining (WordStat) software to aggregate
the extracted text. Text mining tools such as Keyword in Context (KWIC) and Keyword Retrieval (KR)
were employed to identify specific occurrences of data across the records. Two different datasets were interrogated,
a bespoke test dataset that had been set up specifically for the purpose of the research, and a functioning
veterinary clinic dataset that had been extracted from one veterinary practice.
Across both datasets, the KWIC analysis was found to have a high level of accuracy with the search resulting
in a sensitivity of between 85.3–100%, a specificity of between 99.1–99.7%, a positive predictive value between
93.5–95.8% and a negative predictive value between 97.7–100%. The KR search, based on machine learning,
was utilised for the clinic-based dataset and was found to perform slightly better than the KWIC analysis.
This study is the first to demonstrate the application of content analysis and text mining software for validation
purposes across a number of different datasets for the purpose of search and recall of specific information
across electronic patient records. This has not been demonstrated previously for small animal veterinary epidemiological
research for the purposes of large scale analysis for practice-based research. Extension of this work
to investigate more complex diseases across larger populations is required to fully explore the use of this approach
in veterinary practice. Keywords: Text mining | Content analysis | Veterinary practice | Practice based research |
مقاله انگلیسی |