Machine Learning of Ambiguous Sentences in Technical Manual for Tacit Knowledge Acquisition

2019 ◽  
Author(s):  
Naoto Kai ◽  
Kota Sakasegawa ◽  
Tsunenori Mine ◽  
Sachio Hirokawa
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Tacit Knowledge ◽  
Technical Manual

Research on Knowledge Discovery and Stock Forecasting of Financial News Based on Domain Ontology

2019 ◽  
Vol 18 (03) ◽  
pp. 953-979 ◽  
Author(s):  
Lingling Zhang ◽  
Minghui Zhao ◽  
Zili Feng
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Discovery ◽  
Tacit Knowledge ◽  
Explicit Knowledge ◽  
Academic Research ◽  
Online News ◽  
Domain Ontology ◽  
General Mechanism ◽  
Financial News ◽  
Knowledge Reasoning

In the era of big data, how to obtain useful knowledge from online news and utilize it as an important basis to make investment decision has become the hotspot of industrial and academic research. At present, there have been research and practice on explicit knowledge acquisition from news, but tacit knowledge acquisition is still under exploration. Based on the general mechanism of domain knowledge, knowledge reasoning, and knowledge discovery, this paper constructs a framework for discovering tacit knowledge from news and applying the knowledge to stock forecasting. The concrete work is as follows: First, according to the characteristics of financial field and the conceptual cube, the conceptual structure of industry–company–product is constructed, and the framework of domain ontology is put forward. Second, with the construction of financial field ontology, the financial news knowledge management framework is proposed. Besides, with the application of attributes in ontology and domain rules extracted from news text, the knowledge reasoning mechanism of financial news is constructed to achieve financial news knowledge discovery. Finally, news knowledge that reflects important information about stock changes is integrated into the traditional stock price forecasting model and the newly proposed model performs well in the empirical analysis of polyester industry.

Machine Learning as a Knowledge Acquisition Tool Application in the Domain of the Interpretation of Test Results

1989 ◽  
pp. 37-44
Author(s):  
R. A. J. Schijven ◽  
J. L. Talmon ◽  
E. Ermers ◽  
R. Penders ◽  
P. J. E. H. M. Kitslaar
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Test Results

Machine Learning and Knowledge Acquisition

1992 ◽  
Vol 5 (1) ◽  
pp. 19-24 ◽  
Author(s):  
F. Bergadano ◽  
Y. Kodratoff ◽  
K. Morik
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition

Tacit Knowledge Management Within Hospitality Establishments

2016 ◽  
Vol 12 (3) ◽  
pp. 15-29 ◽  
Author(s):  
Spyros Avdimiotis
Keyword(s):  
Knowledge Management ◽  
Knowledge Acquisition ◽  
Tacit Knowledge ◽  
Task Assignment ◽  
Personal Characteristics ◽  
Research Field ◽  
Working Environment ◽  
The Subject ◽  
Fierce Competition ◽  
Tacit Knowledge Management

Tacit knowledge is a concept developed in connection with knowledge management research field. It is acknowledged as the cornerstone of competitive advantage; however, merely its possession does not guarantee an edge in fierce competition. Even though tacit knowledge holds a dominative role towards labor efficiency, productivity and innovation, the subject of tacit knowledge acquisition and transfer has been rather unexploited, mostly due to its intrinsic, highly personal and seamlessly bonded to holder's personality, attributes. The purpose of the article is to contribute to the exploitation of the embedded tacit knowledge of employees in hospitality establishments, a sector where the employment of tacit knowledge has to be extensive and foremost, capitalizing the maximum of personnel competences. Moreover, the paper correlates tacit knowledge acquisition and transfer, with behaviors stemming from a working environment where task assignment is adjusted to employees personal characteristics. Towards this goal, research hypotheses were built and tested, using SEM Methodology.

Accelerated engaged tacit knowledge acquisition during executive succession

2019 ◽  
Vol 43 (5) ◽  
pp. 573-594
Author(s):  
Rida Elias ◽  
Bassam Farah
Keyword(s):  
Theoretical Model ◽  
Knowledge Acquisition ◽  
Tacit Knowledge ◽  
Transition Period ◽  
Knowledge Flow ◽  
Executive Succession ◽  
Human Resource Managers ◽  
Specific Knowledge ◽  
Content Type ◽  
Resource Managers

Purpose The purpose of this paper is to provide a model that can explain how organizations may retain their executives’ tacit knowledge in the organization especially during the succession period. The proposed model takes into consideration three critical contexts that may assist in improving the knowledge flow during the transition period, namely, motivation context, transition context and ability context. Design/methodology/approach This paper presents a conceptual framework that emphasizes the importance of the will and skill of two parties involved in succession, i.e. the predecessor and successor, as well as the context of the succession. To this end, the paper advances a set of propositions that explain how these different contexts affect the quantity and quality of the knowledge acquired by the successor at the end of the succession period. Findings This paper advances a theoretical model that describes the antecedents and moderator of job-specific knowledge acquired during executive succession. Research limitations/implications This paper presents a theoretical model that explains knowledge flow during the transitory period of succession. It emphasizes the importance of the motivation and ability of the partners involved while taking into consideration the context of succession. Practical implications This paper contributes considerably and in a practical manner to managers in general and to human resource managers in particular. It draws the attention of concerned managers to check the motivation of both successor and predecessor in experiencing the transition, explain to the successors the job description of the position to direct their attention to learn specific knowledge and equip both parties involved in the succession with the needed skills. Originality/value This paper advances a new concept termed as accelerated engaged tacit knowledge acquisition. This concept complements other perspectives of knowledge flow and learning and takes into consideration the specific context of executive succession.

On a Novel Cognitive Knowledge Base (CKB) for Cognitive Robots and Machine Learning

2014 ◽  
Vol 6 (2) ◽  
pp. 41-62 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Cognitive Learning ◽  
Knowledge Bases ◽  
Formal Concept ◽  
Human Knowledge ◽  
Cognitive Knowledge ◽  
Cognitive Robots ◽  
Concept Network

A cognitive knowledge base (CKB) is a novel structure of intelligent knowledge base that represents and manipulates knowledge as a dynamic concept network mimicking human knowledge processing. The essence of CKB is the denotational mathematical model of formal concept that is dynamically associated to other concepts in a CKB beyond conventional rule-based or ontology-based knowledge bases. This paper presents a formal CKB and autonomous knowledge manipulation system based on recent advances in neuroinformatics, concept algebra, semantic algebra, and cognitive computing. An item knowledge in CKB is represented by a formal concept, while the entire knowledge base is embodied by a dynamic concept network. The CKB system is manipulated by algorithms of knowledge acquisition and retrieval on the basis of concept algebra. CKB serves as a kernel of cognitive learning engines for cognitive robots and machine learning systems. CKB plays a central role not only in explaining the mechanisms of human knowledge acquisition and learning, but also in the development of cognitive robots, cognitive learning engines, and knowledge-based systems.

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