Knowledge Acquisition Techniques Research Articles

Clustering policy for urban competitiveness: One‐size‐fits‐all? Evidence from Tehran

Urban competitiveness is commonly attributed to necessitating particular types of socio‐spatial frameworks such as clustering on the basis of models of Marshall–Arrow–Romer (MAR), Porter, or Jacobs, since proximity is known as a key perquisite for knowledge spillover, innovation, and hence urban competitiveness. Nevertheless, it is unjustifiable to unconditionally endorse the ‘clustering’ policy. Consequently, this research aims to examine the spatiality and geography of competition and innovation contingent upon varying spatial prerequisites and contextual circumstances. In this regard, two questions have been raised: under what circumstances urban competitiveness depends on spatial agglomeration, and whether this agglomeration follows a clustering development pattern. Through the implementation of a causal research design utilizing both in‐depth interviews and social survey methodology, this study has determined the innovative characteristics and techniques for knowledge acquisition, as well as spatial behaviors and preferences of competitive industries located within Tehran. The spatially constrained multivariate clustering method was employed to group competitive industries according to their distinct substantive dissimilarities, socio‐spatial behaviors, and characteristics. Then, on the basis of the structural equation modeling, the most appropriate spatial development frameworks of competitiveness were extracted with regard to the specific conditions of Tehran. Findings show that innovation, and hence competitiveness, are not always the result of clustering; clusters, as a spatial policy‐making tool, have been found to be highly conducive to collaborative and intensive knowledge production, as they provide access to spillover knowledge, thereby increasing competitiveness. Despite complex economic dependencies and collaborations, some competitive industries have greater mobility and are footloose due to their Schumpeterian linear knowledge source and virtual nature. What matters for this elusive, light, fluid category free from the shackles of place is institutional and organizational proximity, which proves its conditions are up to the government. As the main finding of the research, the geography and spatiality of innovation, and therefore the competitiveness, depend on two factors, including: (a) the relationships and interactions, that is, types of activity units and (b) the source and method of acquiring knowledge required for innovation and competitive advantage. In some activities, on the basis of their knowledge intensity, interaction type, and knowledge source, competitiveness is not related to clusters; in others, it depends on a specific location; and some are independent of location.

Named Entity Recognition for English Language Using Deep Learning Based Bi Directional LSTM-RNN

The NER has been important in different applications like data Retrieval and Extraction, Text Summarization, Machine Translation, Question Answering (Q-A), etc. While several investigations have been carried out for NER in English, a high-accuracy tool still must be designed per the Literature Survey. This paper suggests an English Named Entities Recognition methodology using NLP algorithms called Bi-Directional Long short-term memory-based recurrent neural network (LSTM-RNN). Most English Language NER systems use detailed features and handcrafted algorithms with gazetteers. The proposed model is language-independent and has no domain-specific features or handcrafted algorithms. Also, it depends on semantic knowledge from word vectors realized by an unsupervised learning algorithm on an unannotated corpus. It achieved state-of-the-art performance in English without the use of any morphological research or without using gazetteers of any sort. A little database group of 200 sentences includes 3080 words. The features selection and generations are presented to catch the Name Entity. The proposed work is desired to forecast the Name Entity of the focus words in a sentence with high accuracy with the benefit of practical knowledge acquisition techniques.

A combined approach for improving humanoid robots autonomous cognitive capabilities

Recent technologies advancements promise to change our lives dramatically in the near future. A new different living society is progressively emerging, witnessed from the conception of novel digital ecosystems, where humans are expected to share their own spaces and habits with machines. Humanoid robots are more and more being developed and provided with enriched functionalities; however, they are still lacking in many ways. One important goal in this sense is to enrich their cognitive capabilities, to make them more “intelligent” in order to better support humans in both daily and special activities. The goal of this research is to set a step in bridging the gap between symbolic AI and connectionist approaches in the context of knowledge acquisition and conceptualization. Hence, we present a combined approach based on semantics and machine learning techniques for improving robots cognitive capabilities. This is part of a wider framework that covers several aspects of knowledge management, from representation and conceptualization, to acquisition, sharing and interaction with humans. Our focus in this work is in particular on the development and implementation of techniques for knowledge acquisition. Such techniques are discussed and validated through experiments, carried out on a real robotic platform, showing the effectiveness of our approach. The results obtained confirmed that the combination of the approaches gives superior performance with respect to when they are considered individually.

Free-form CAD model retrieval approach for engineering reuse based on local feature segmentation

For engineering applications, an innovative design can be developed by reusing and modifying existing models with similar features and manufacturing properties, and accurately searching CAD models has become a valuable knowledge acquisition technique in product design. Although there are many retrieval technologies, most methods focus on the global shape of models. In this paper, a CAD model retrieval framework based on local feature segmentation is proposed, offering users a free-form way to choose any subpart as a query from a given model. First, a novel model segmentation method based on the vertex-neighbor extension is proposed to divide a target CAD model into overlapping local features. The chosen subpart with arbitrary boundaries is also successfully described for retrieval by combining local features. Then, a composite descriptor considering both shapes and attribute characteristics is established, which is proved effective for distinguishing local features. Finally, the problem of matching free-form queries to CAD models in the dataset is transformed into descriptor set measurement and is implemented using the bag-of-word algorithm. The proposed model retrieval approach outperforms many existing approaches in matching performance and provides a user-friendly query mode.

Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems

Non-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence breaks conventional wave propagation symmetries, leading to asymmetric reflection and symmetric transmission with respect to the wave propagation direction. Here, we use supervised and unsupervised learning techniques for knowledge acquisition in non-Hermitian systems which accelerate the inverse design process. In particular, we construct a deep learning model that relates the transmission and asymmetric reflection in non-conservative settings and propose sub-manifold learning to recognize non-Hermitian features from transmission spectra. The developed deep learning framework determines the feasibility of a desired spectral response for a given structure and uncovers the role of effective gain-loss parameters to tailor the spectral response. These findings offer a route for intelligent inverse design and contribute to the understanding of physical mechanism in general non-Hermitian systems.

Knowledge elicitation as a route to understanding the decision making landscape in monoclonal antibody manufacturing

This paper considers a strategy for identifying the decisions and bottlenecks that arise in monoclonal antibody development in the progression from a multitude of potential candidate molecules to a prioritised sub-set that are taken through development and possess a good chance of succeeding in the clinic. While the steps involved are common across the industry, the need to ‘fail fast’ challenges existing decision making. In any development there are numerous technical and business focused staff involved with different skill sets and areas and scales of focus. As potential products could fail in any part of the development programme, failing ‘fast’ requires understanding of the requirements of those in latter stages by technical and business decision makers in as early development as possible. The focus of the paper is the use of the Knowledge Acquisition Technique to elicit the expertise of numerous specialists and to develop an interpretable decision-making landscape across development to facilitate decision making. The effectiveness of the elicitation is considered and the richness of information it delivers to challenge conventional wisdom addressed. The technique is shown to deliver a broader appreciation of the requirements throughout the development supply chain.

Practical Techniques to Adapt Surgical Resident Education to the COVID-19 Era.

Practical Techniques to Adapt Surgical Resident Education to the COVID-19 Era.

An Analytical Game for Knowledge Acquisition for Maritime Behavioral Analysis Systems

The use of Bayesian networks for behavioral analysis is gaining attention. The design of such algorithms often makes use of expert knowledge. The knowledge is collected and organized during the knowledge acquisition design task. In this paper, we discuss how analytical games can be exploited as knowledge acquisition techniques in order to collect information useful to intelligent systems design. More specifically, we introduce a recently developed method, called the MARISA (MARItime Surveillance knowledge Acquisition) Game. The aim of this game is to ease the elicitation from domain experts of a considerable amount of conditional probabilities to be encoded into a maritime behavioral analysis service based on a multi-source dynamic Bayesian network. The game has been deployed in two experiments. The main objectives of such experiments are the validation of the network structure, the acquisition of the conditional probabilities for the network, and the overall validation of the game method. The results of the experiment show that the objectives have been met and that the MARISA Game proved to be an effective and efficient approach.

Exception Included, Ordered Rule Induction from the Set of Exemplars (ExIORISE)

An expert system is one which uses collection of data comprising the knowledge to offer guidance or make inferences. Its work in most cases can be seen as classification which is basically the task of assigning objects to different categories or classes, determined by the properties of those objects. Numerous research works have been and being done to develop efficient knowledge acquisition techniques for expert systems. Some state-of-the-art algorithms are great performers but need extensive learning whereas older rule / decision- tree based algorithms perform pretty well with small data sets. Moreover, co-existence of learners of different levels of expertise and accuracy is believed to be encouraged to achieve a cumulative intelligence just like the human beings have. RISE is one such algorithm that infuses both instance-based learning and rule induction. It proved to be quite efficient handling binary and multi-class classification problems for small data sets in terms of accuracy and cost as well. In this work, features like exclusion of inefficient rules, inclusion of exceptions in the rule set and ordering of the rules using weights beforehand are integrated with the classical RISE algorithm to develop a more efficient classifier system named as ExIORISE. Empirical study shows that ExIORISE outperforms RISE, C4.5 and CN2 significantly.

This Month in Anesthesiology.

This Month in Anesthesiology.

Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems

Powerful knowledge acquisition tools and techniques have the ability to increase both the quality and the quantity of knowledge-based systems for real-world problems. In this paper, we designed a hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm denoted as CFCSA for feature selection problems of medical diagnosis. In the proposed CFCSA framework, the crow search algorithm adopts the global optimization technique to avoid the sensitivity of local optimization. The fuzzy c-means (FCM) objective function is used as a cost function for the chaotic crow search optimization algorithm. The proposed algorithm CFCSA is benchmarked against the binary crow search algorithm (BCSA), chaotic ant lion optimization algorithm (CALO), binary ant lion optimization algorithm (BALO) and bat algorithm relevant methods. The proposed CFCSA algorithm vs. BCSA, CALO, BALO and bat algorithm is tested on diabetes, heart, Radiopaedia CT liver, breast cancer, lung cancer, cardiotocography, ILPD, liver disorders, hepatitis and arrhythmia. Experimental results show the proposed method CFCSA is better against comparative models in feature selection on the medical diagnosis data sets.

Special issue on knowledge graphs and semantics in text analysis and retrieval

Knowledge graphs are an efective way to store semantics in a structured format that is easily used by computer systems. In the past few decades, work across diferent research communities led to scalable knowledge acquisition techniques for building large-scale knowledge graphs. The result is the emergence of large publicly available knowledge graphs (KGs) such as DBpedia (Lehmann et al. 2014), Freebase (Bollacker et al. 2008), and others. While knowledge graphs are designed to support a wide set of diferent applications, this special issue focuses on the use case of text retrieval and analysis. Utilizing knowledge graphs for text analysis requires efective alignment techniques that associate segments of unstructured text with entries in the knowledge graph, for example using entity extraction and linking algorithms (Carmel et al. 2014; Mendes et al. 2011; Blanco et al. 2015). A wide range of approaches that combine query-document representations and machine learning repeatedly demonstrate signifcant improvements for such tasks across diverse domains (Dalton et al. 2014; Liu and Fang 2015; Hasibi et al. 2015; Xiong and Callan 2015; Raviv et al. 2016; Ensan and Bagheri 2017; Xiong et al. 2017). The goal of this special issue is to summarize recent progress in research and practice in constructing, grounding, and utilizing knowledge graphs and similar semantic resources for text retrieval and analysis applications. The scope includes acquisition, alignment, and utilization of knowledge graphs and other semantic resources for the purpose of optimizing end-to-end performance of information retrieval systems.

The application of restructuring of knowledge in civil engineering

In this paper, it is shown how knowledge theories and knowledge acquisition techniques are integrated by contextualisation to lead to the drawing of concept maps that can be used in civil engineering design, and to analyse and record specific experience. The concept maps form part of concept-based ontologies that are analysed to identify problems and constraints. Solutions to these problems and constraints create new knowledge and can be reported and linked to the world-wide-web. This linkage is made possible by utilising the Top-Level-Ontologies or Upper-Level-Ontologies to link to existing or new ontologies on the world-wide-web. The logic base acts as a procedure to lead and integrate all the above-mentioned aspects into three modules. These modules of the logic base are described and simple examples are given of how the logic base functions. The logic base is a technique to bring knowledge closer to the practising engineer, and facilitates thinking processes that will greatly assist in systematising knowledge, the analysis thereof and making it accessible on the word-wide-web.

Contribution of artificial intelligence and machine learning to the assessment of the safety of critical software used in railway transport

As part of the process of certification and commissioning of a new guided or automated rail transport system, the domain experts and in particular the National Safety Authority are responsible for reviewing the safety of the system to ensure that the safety level of the new transport system is at least equivalent to the railway systems already in service and deemed safe. This critical task of evaluating safety essentially concerns all the safety files prepared by the manufacturer and in particular safety studies such as the Preliminary Hazard Analysis (PHA), the functional safety analysis (FSA), the analysis of failure modes, their effects and of their criticality (AFMEC) or Software Error Effect Analysis (SEEA). The study presented in this paper is part of the SEEA analysis. To respect the completeness and consistency of this safety analysis (SEEA), the experts carry out complementary analyses of safety. They are brought to imagine new scenarios of potential accidents to perfect the exhaustiveness of the safety studies. In this process, one of the difficulties then consists in finding the abnormal scenarios being able to lead to a particular potential accident. This is the fundamental point that motivated this work. To help experts in this complex process of evaluating safety studies, we agreed to use artificial intelligence techniques and in particular machine learning to systematize, streamline and strengthen conventional approaches to safety analysis and critical software certification. The approach which was adopted in order to design and implement an assistance tool for safety analysis involved the following two main activities: – Extracting, formalizing and storing hazardous situations to produce a library of standard cases which covers the entire problem. This process entailed the use of knowledge acquisition techniques; – Exploiting the stored historical knowledge in order to develop safety analysis know-how which can assist experts to judge the thoroughness of the manufacturer’s suggested safety analysis. This second activity involves the use of machine learning techniques in particular the use of case-based reasoning (CBR).

A Fibrosis Diagnosis Clinical Decision Support System Using Fuzzy Knowledge

Liver cirrhosis, the end stage of chronic liver disease, is one of the major risk factors for the development of liver cancer, and may result in premature death. This research proposes a fuzzy fibrosis decision support (F2DS) system. It is a fuzzy knowledge-based expert system for liver fibrosis stage prediction. F2DS is carefully based on a set of knowledge acquisition and machine learning techniques. In addition, the system depends on domain expert knowledge for designing the membership functions and validating the fuzzy knowledge base. It depends on a suitable list of 17 symptoms, and laboratory test features that can accurately and significantly describe fibrosis patients. The experimental results of the expert system were obtained using a real dataset from the Liver Institute, Mansoura University, Egypt, of 119 patients infected by chronic viral hepatitis C. The performance of the system was evaluated with many metrics, achieving a testing accuracy of 95.7%. The evaluation of proposed fuzzy expert system shows its capability of diagnosing the stages of liver fibrosis with a high degree of accuracy, and it can be embedded as a component in a healthcare system to assist physicians in their daily practice. In addition, students training in medicine can benefit from this system.

Scalable bootstrap attribute reduction for massive data

Attribute reduction is one of the fundamental techniques for knowledge acquisition in rough set theory. Traditional attribute reduction algorithms have to load the whole dataset into the memory at a time, however, it is unfeasible for attribute reduction of the massive decision table due to hard limitation. To solve this problem, we propose the bag of little bootstraps attribute reduction algorithm (BLBAR), which combines the bag of little bootstraps with attribute discernibility. Specifically, the algorithm first samples from the original decision table to generate a number of decision sub-tables; and then finds the reducts of bootstrap samples of each sub-table through attribute discernibility; finally, all of the reducts are integrated as the reduct of the original massive decision table. Experimental results demonstrate that BLBAR leads to the improved feasibility, scalability and efficiency for attribute reduction on massive decision table.

Scalable bootstrap attribute reduction for massive data

Attribute reduction is one of the fundamental techniques for knowledge acquisition in rough set theory. Traditional attribute reduction algorithms have to load the whole dataset into the memory at a time, however, it is unfeasible for attribute reduction of the massive decision table due to hard limitation. To solve this problem, we propose the bag of little bootstraps attribute reduction algorithm (BLBAR), which combines the bag of little bootstraps with attribute discernibility. Specifically, the algorithm first samples from the original decision table to generate a number of decision sub-tables; and then finds the reducts of bootstrap samples of each sub-table through attribute discernibility; finally, all of the reducts are integrated as the reduct of the original massive decision table. Experimental results demonstrate that BLBAR leads to the improved feasibility, scalability and efficiency for attribute reduction on massive decision table.

An experimental investigation of knowledge acquisition techniques

PurposeKnowledge is a key driver for the competitive success of organizations, but about 90 percent of organizational knowledge is inside employees’ minds with personal essence; therefore, this paper provides valuable vision for managers by exploring knowledge acquisition (KA) techniques and personality type. The purpose of this paper is to examine KA techniques and explore the impact of personality type on the KA process in the aerospace industry.Design/methodology/approachThis paper examines KA techniques through an empirical study involving 83 participants to take part in KA sessions. For exploring techniques, a questionnaire was used, and also the Myers-Briggs Type Indicator was used to identify participants’ personality type. The impact of personality type on KA processes was determined by correlation analysis.FindingsAnalyses confirmed some association between the type of personality and KA process. In addition, the findings of exploring questionnaire items showed that participants gave the laddering technique the highest rating.Originality/valueThe paper may be of high value to researchers in the field of KA, especially in aerospace industries, because there is very little experimental investigation of KA, and it also provides valuable information and guidelines that hopefully will help researchers to select appropriate KA techniques.

Experts community memory for entity similarity functions recommendation

Similarity search (or similar entity search) is the process of finding all entities similar to a given entity (e.g., a person, a document, or an image). Although many techniques for similarity analysis have been proposed in the past, little work has been done on the question of which of the presented techniques are most suitable for a given similarity search task. Knowing the right similarity function is important as the task is highly domain- and data-dependent. In this article, we provide an approach for recommending which similarity functions (e.g., edit distance or jaccard similarity) should be used for measuring the similarity between two entities. The approach employs an incremental knowledge acquisition technique for capturing domain experts’ knowledge about similarity functions and their usage contexts (e.g., entity class, attribute name and some keywords). In addition, for situations where domain experts have little or no knowledge about datasets, we analyze the features of the datasets and then suggest similarity functions based on the identified features. We also demonstrate the feasibility and effectiveness of our proposed approach on several real-world datasets from different domains.

Towards knowledge modeling and manipulation technologies: A survey

A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000–2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.