Knowledge Acquisition Research Research Articles

Exploration of talent mining based on machine learning and the influence of knowledge acquisition

ABSTRACT Under the background of economic globalisation, to promote the sustainable development of enterprises, sustainable innovation performance is explored for enterprises from the perspective of knowledge acquisition. Here, high-tech industry practitioners in Jiangsu Province are recruited for research of knowledge acquisition and continuous innovation using a QS (Questionnaire Survey). A total of 360 QSs are issued, and the QS items are developed based on a comprehensive collation of domestic and foreign literature and are mostly quoted from existing data. Consequently, a talent mining method is proposed for technological innovation based on the machine learning multi-layer perceptron model. The results show that there is a significant correlation between complementary knowledge and knowledge acquisition. Knowledge acquisition is significantly related to continuous innovation. Complementary knowledge is significantly related to continuous innovation. The high-tech industry has realised that knowledge will become the key to the success of the high-tech industry in the future.

Are Social Ties Always Valuable to Knowledge Search? Contextualizing Knowledge Search by Foreign Subsidiary Executives in an Emerging Economy

The extant research of cross-border knowledge acquisition by multinational enterprises often assumes away the role of local contexts within which knowledge acquisition occurs. To extend this line of research, this study contextualizes the knowledge search by foreign subsidiary executives to examine the contingency value of social ties. The results based on the multilevel analyses of both micro (433 dyad ties) and macro effects of the regional knowledge environment (26 provinces) provide support for the hypotheses that, though close social ties facilitated knowledge search as predicted by social capital theory, the utility value of social ties for knowledge search tends to be attenuated in the regions with high level of social capital and FDI density.

Impact Evaluation of Customer Knowledge Process On Customer Knowledge Acquisition

This paper examines how Jordanian banks use the Customer Knowledge process to support Customer Knowledge Acquisition (CKA) and how they foster it. The empirical study is based on a sample of the data collected from 165 respondents, drawn randomly from six banks. The results showed that the six selected factors (Need for Customer Knowledge, Identify Source of Customer Knowledge, Verify Source of Customer Knowledge, Capture of Customer Knowledge, Apply of Knowledge about Customer, and Verify of Knowledge about Customer) have a significant impact on customer knowledge acquisition. On the other hand, the (Analysis of Customer Knowledge) is not a significant impact on customer knowledge acquisition in Jordanian banks. For this reason, the purpose of this paper is to study how Jordanian Banks use the Customer Knowledge process to support Customer Knowledge Acquisition. The empirical findings will certainly help both researchers and practitioners in future customer knowledge process, and Customer Knowledge Acquisition research.

User Knowledge Acquisition Research for Automotive Design

The paper makes automotive design as a carrier, and uses the methods of user research and statistical analysis; so as to reduce the dimension of automotive design image words and reduce redundant information interference, then obtains the representation of vocabulary and representative car samples of automotive-styling design, to make automotive design more effective.

Knowledge acquisition and the web

Knowledge-acquisition research started in the eighties as a small research community focusing on knowledge-intensive problems in relatively small domains. In this paper we look at the influence the Web has had on knowledge acquisition and vice versa. To this end we discuss in some depth four topics, namely the ontology language OWL, the vocabulary language SKOS, the notion of ontology alignment and the potential of semantic search. Even from this limited selection of research issues related to “Web knowledge” it is safe to conclude that the Web has had a large impact on knowledge acquisition, but also the other way around.

Situated cognition and knowledge acquisition research

In the early years of knowledge acquisition research there was considerable discussion about the possibility of obtaining knowledge from experts, based on the ideas of situated cognition. The central idea from situated cognition was that knowledge is only ever created in a context and so cannot be used reliably out of context. These ideas seem to have had little impact on most of the knowledge acquisition research that followed, probably because they seemed to be too negative about the possibilities for knowledge-based systems. This paper suggests that situated cognition can be reinterpreted as making the positive suggestion that if people distinguish between different conclusions in different contexts, they do so because they can identify features that distinguish the contexts. This type of case or context differentiation could be readily integrated with a range of knowledge acquisition frameworks, and experience with Ripple-Down Rules suggests that it provides a very simple way for domain experts and others to easily provide a large amount of knowledge for a system.

The knowledge acquisition workshops: A remarkable convergence of ideas

Intense interest in knowledge-acquisition research began 25 years ago, stimulated by the excitement about knowledge-based systems that emerged in the 1970s followed by the realities of the “AI Winter” that arrived in the 1980s. The knowledge-acquisition workshops that responded to this interest led to the formation of a vibrant research community that has achieved remarkable consensus on a number of issues. These viewpoints include (1) the rejection of the notion of knowledge as a commodity to be transferred from one locus to another, (2) an acceptance of the situated nature of human expertise, (3) emphasis on knowledge acquisition as the modeling of problem solving, and (4) the pursuit of reusable patterns in problem solving and in domain descriptions that can facilitate both modeling and system implementation. The Semantic Web community will benefit greatly by incorporating these perspectives in its work.

Interactive knowledge capture in the new millennium: how the Semantic Web changed everything

AbstractThe Semantic Web has radically changed the landscape of knowledge acquisition research. It used to be the case that a single user would edit a local knowledge base, that the user would have domain expertise to add to the system, and that the system would have a centralized knowledge base and reasoner. The world surrounding knowledge-rich systems changed drastically with the advent of the Web, and many of the original assumptions were no longer a given. Those assumptions had to be revisited and addressed in combination with new challenges that were put forward. Knowledge-rich systems today are distributed, have many users with different degrees of expertise, and integrate many shared knowledge sources of varying quality. Recent work in interactive knowledge capture includes new and exciting research on collaborative knowledge sharing, collecting knowledge from Web volunteers, and capturing knowledge provenance.

Young children’s talk about learning events

In order to understand children’s conception of knowledge acquisition better, everyday uses of the terms “learn” and “teach” were examined. Longitudinal data obtained from CHILDES (MacWhinney & Snow, 1990) included 329 target term uses and related references by children ( N=5, aged 2;4–7;3) and 431 by adults talking with them. Each reference was coded for mention of what was learned, when, how, and where learning occurred, who learned, and who taught/told, among other topics. Children and adults referred most frequently to what was learned and who learned/taught, and less frequently to when, how, and where learning occurred, a pattern that did not change as children aged. Consistent with earlier experimental reports, children talked mostly about their own learning, rarely mentioning sources of knowledge besides other people (e.g., teachers). Behavior learning was mentioned more than fact learning. Implications for characterizations of children’s developing conceptions of knowledge acquisition, for past and future experimental research, and for education were discussed.

Applications of the Cambridge Structural Database to molecular inorganic chemistry.

Applications of the data in the Cambridge Structural Database (CSD) to knowledge acquisition and fundamental research in molecular inorganic chemistry are reviewed. Various classes of application are identified, including the derivation of typical molecular dimensions and their variability and transferability, the derivation and testing of theories of molecular structure and bonding, the identification of reaction paths and related conformational analyses based on the structure correlation hypothesis, and the identification of common and presumably energetically favourable intermolecular interactions. In many of these areas, the availability of plentiful structural data from the CSD is set against the emergence of high-quality computational data on the geometry and energy of inorganic complexes.

User studies of knowledge acquisition tools: methodology and lessons learned

Knowledge acquisition research concerned with the development of knowledge acquisition tools is in need of a methodological approach to evaluation. This paper describes experimental methodology to conduct studies and experiments of users modifying knowledge bases with knowledge acquisition tools. The paper also reports on the lessons learned from several experiments that have been performed using this methodology. The hope is that it will help others design user evaluations of knowledge acquisition tools. Ideas are discussed for improving the current methodology and some open issues that remain.

Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems

The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts and knowledge engineers and difficulties with the process of acquiring knowledge from domain experts. Within the field of artificial intelligence, this has been called the ‘knowledge acquisition’ problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well‐defined problem domain efficiently from one or more experts and represent it in the appropriate computer format. Given the ‘paradox of expertise’, the experts have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM) field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of problem domains. We then describe the most prominent knowledge acquisition techniques. Based on the examination of the existing empirical knowledge acquisition research, we present how the empirical work can be used to provide guidance to developers of expert systems in the field of P/OM.

Alternatives to rules for knowledge-based modelling

The technology of knowledge-based systems undoubtedly offers potential for educational modelling, yet its practical impact on today's school classrooms is very limited. To an extent this is because the tools presently used in schools are mostly rule-based expert system shells, which lack usability. We developed three alternative tools, using ideas from knowledge acquisition research, and compared their effectiveness to that of an established rule-based shell. Children working with the new tools produced higher quality models and developed more positive attitudes. We relate these findings to the forms of representation provided by the new tools and present evidence that modelling increases children's representational skill. We conclude that knowledge acquisition systems and alternative forms of representation can contribute to improved forms of knowledge-based modelling.

(KA)2: building ontologies for the Internet: a mid-term report

Ontologies are becoming increasingly more important in many different areas, including the knowledge management area. In knowledge management, ontologies can be used as an instrument to make knowledge assets intelligently accessible to people in organizations through an Intranet or the Internet. Most enterprises agree that knowledge is an essential asset for success and survival on an increasingly competitive and global market. In this paper, we present an ontology-based approach through a large-scale initiative involving knowledge management for the knowledge-acquisition research community.

Integration of Machine Learning and Knowledge Acquisition

“Integration of Machine Learning and Knowledge Acquisition” may be a surprising title for an ECAI-94 workshop, since most machine learning (ML) systems are intended for knowledge acquisition (KA). So what seems problematic about integrating ML and KA? The answer lies in the difference between the approaches developed by what is referred to as ML and KA research. Apart from sonic major exceptions, such as learning apprentice tools (Mitchell et al., 1989), or libraries like the Machine Learning Toolbox (MLT Consortium, 1993), most ML algorithms have been described without any characterization in terms of real application needs, in terms of what they could be effectively useful for. Although ML methods have been applied to “real world” problems few general and reusable conclusions have been drawn from these knowledge acquisition experiments. As ML techniques become more and more sophisticated and able to produce various forms of knowledge, the number of possible applications grows. ML methods tend then to be more precisely specified in terms of the domain knowledge initially required, the control knowledge to be set and the nature of the system output (MLT Consortium, 1993; Kodratoff et al., 1994).

Task analysis interview based on task ontology

In order to realize automated knowledge acquisition, we have to solve many problems such as lack of reusability and sharability of knowledge, which is one of the shortcomings in the current knowledge base technology, to fill the conceptual gap between the computer and domain experts and so on. Recent research activities in knowledge acquisition community are focused on task ontology, because it is expected to contribute a lot to making it easier to elicit expertise from domain experts. The authors have been involved in the research of knowledge acquisition and knowledge reuse. This article is concerned with task ontology and its use in a task analysis interview system MULTIS. We first discuss the knowledge reusability to identify appropriate task ontology. Then, we introduce a two-level mediating representation that contributes to bridging the gap and hence to making the task analysis interview fluent. MULTIS has been implemented in Macintosh Common Lisp.

Towards method-independent knowledge acquisition

Rapid prototyping and tool reusability have pushed knowledge acquisition research to investigate method-specific knowledge acquisition tools appropriate for predetermined problem-solving methods. We believe that method-dependent knowledge acquisition is not the only approach. The aim of our research is to develop powerful yet versatile machine learning mechanisms that can be incorporated into general-purpose but practical knowledge acquisition tools. This paper shows through examples the practical advantages of this approach. In particular, we illustrate how existing knowledge can be used to facilitate knowledge acquisition through analogy mechanisms within a domain and across domains. Our sample knowledge acquisition dialogues with a domain expert illustrate which parts of the process are addressed by the human and which parts are automated by the tool, in a synergistic cooperation for knowledge-base extension and refinement. The paper also describes briefly the EXPECT problem-solving architecture that facilitates this approach to knowledge acquisition.

Knowledge acquisition tools based on personal construct psychology

AbstractKnowledge acquisition research supports the generation of knowledge-based systems through the development of principles, techniques, methodologies and tools. What differentiates knowledge-based system development from conventional system development is the emphasis on in-depth understanding and formalization of the relations between the conceptual structures underlying expert performance and the computational structures capable of emulating that performance.Personal construct psychology is a theory of individual and group psychological and social processes that has been used extensively in knowledge acquisition research to model the cognitive processes of human experts. The psychology takes a constructivist position appropriate to the modelling of human knowledge processes, but develops this through the characterization of human conceptual structures in axiomatic terms that translate directly to computational form. In particular, there is a close correspondence between the intensional logics of knowledge, belief and action developed in personal construct psychology, and the intensional logics for formal knowledge representation developed in artificial intelligence research as term subsumption, or KL-ONE-like, systems.This paper gives an overview of personal construct psychology and its expression as an intensional logic describing the cognitive processes of anticipatory agents, and uses this to survey knowledge acquisition tools deriving from personal construct psychology.

Semantic Analysis of Medical Records

A program (LogStory) is described that was developed for the automatic semantic analysis of clinical narratives, stored in a computerized problem-oriented medical record (PROMED). The diagnoses were written in a free-text format during consultation, and later collected into diagnostic classes, e.g., diseases. A lexical parser automatically created dictionaries from the clinical narrative associated with each disease. Automatic (fuzzy) set operations were performed on the words associated with each class. The manifestations of 16 diseases were automatically extracted by pairwise operations on the word sets. The correlation between diseases and corresponding signs, symptoms and treatment was highly significant (p < 0.001). Applying the difference operation on diseases with disjunct sets of clinical findings allowed the recovery of disease-specific knowledge. The evolution of a disease was accounted for, and the system was able to generalize its findings. The PROMED-LogStory concept enables the processing of natural language and may be a powerful tool for knowledge acquisition and clinical research.

Knowledge acquisition as a constructive modeling activity

Knowledge acquisition is a constructive modeling process, not simply a matter of “expertise transfer.” Consistent with this perspective, we advocate knowledge acquisition practices and tools that facilitate active collaboration between expert and knowledge engineer, that exploit a serviceable theory in their application, and that support knowledge-based system development from a life-cycle perspective. A constructivist theory of knowledge is offered as a plausible theoretical foundation for knowledge acquisition and as an effective practical approach to the dynamics of modeling. In this view, human experts construct knowledge from their own personal experiences while interacting with their social constituencies (e.g., supervisors, colleagues, clients patients) in their niche of expertise. Knowledge acquisition is presented as a cooperative enterprise in which the knowledge engineer and expert collaborate in constructing an explicit model of problem solving in a specific domain. From this perspective, the agenda for the knowledge acquisition research community includes developing tools and methods to aid experts in their efforts to express, elaborate, and improve their models of the domain. This functional view of expertise helps account for several problems that typically arise in practical knowledge acquisition projects, many of which stem directly from the inadequacies of representations used at various stages of system development. to counter these problems, we emphasize the use of mediating representations as a means of communication between expert and knowledge engineer, and intermediate representations to help bridge the gap between the mediating representations themselves, as well as between the mediating representations and a particular implementation formalism. © 1993 John Wiley & Sons, Inc.