scholarly journals The Knowledge Based View of Embedding Strategy for Platforms Enterprises

2022 ◽  
Vol 132 ◽  
pp. 01001
Author(s):  
Chen-Guang Li ◽  
Zhen-Jun Qiu
Keyword(s):  
Knowledge Base ◽  
Innovation Network ◽  
Network Embedding ◽  
Timely Manner ◽  
Depth Of Knowledge ◽  
Knowledge Based ◽  
Relational Embeddedness ◽  
Embedding Strategy ◽  
Different Levels ◽  
Base View

Dialectically linking with heterogenous suppliers, manufacturers, consumers and resources, emerging e-commerce platforms achieve and maintain competitive advantage. Based on different levels of innovation network content and structure, YEATION and YOUPIN, two typical ecommerce platforms, adopt different strategies involving the promotion of value-seeking. Many decisions concern how companies perceive the innovation environment and how to engage in the innovation network. Knowledge base view provides useful theoretical lens to understand the network embedding approach for electric business platform enterprises. To put forward, enterprises with wide breadth should adopt structural strategy give priority to relational embeddedness, on the other hand, enterprises of which the depth of knowledge base is deep and width of insufficient shall adopt strategy give priority to the embedding of structure. It is also proposed that platform enterprises should not adopt a single embeddedness strategy but should adopt auxiliary embeddedness strategy in a timely manner.

scholarly journals The Embedded Strategy of Enterprise Innovation Network Based on Knowledge Base

2021 ◽  
Vol 96 ◽  
pp. 04005
Author(s):  
Chen Guang Li ◽  
Zhenjun Qiu ◽  
Guihuang Jiang
Keyword(s):  
Knowledge Base ◽  
Innovation Network ◽  
Depth Of Knowledge ◽  
Relational Embeddedness ◽  
Enterprise Innovation ◽  
Important Means ◽  
Embedding Strategy ◽  
The Relationship ◽  
Base Width ◽  
Different Levels

Constructing the innovation network is an important means of breakthrough in the traditional manufacturers for the emerging e-commerce platform, based on two different levels of innovation network node, content and structure, YEATION and YOUPIN were chosen to analyse and research the relationship and between knowledge base and the embedding strategy of electric business platform enterprises to create innovation network. To put forward the knowledge base width wider, insufficient depth of the enterprise should adopt structural strategy give priority to relational embeddedness, on the other hand, enterprises of which the depth of knowledge base is deep and width of insufficient shall adopt strategy give priority to the embedding of structure. At the same time, it is proposed that platform enterprises should not adopt a single embeddedness strategy, but should adopt auxiliary embeddedness strategy in a timely manner.

Putting up barriers

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
Language Processing ◽  
Cognitive Domain ◽  
Linguistic Knowledge ◽  
Levels Of Explanation ◽  
Real Time Processing ◽  
Time Processing ◽  
Neural Underpinnings ◽  
Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler
Keyword(s):  
Medical Care ◽  
Knowledge Base ◽  
Language Processing ◽  
Emergency Care ◽  
Risk Classification ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Hospital Emergency ◽  
Knowledge Based System ◽  
Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

Knowledge-Based Recommendation Systems

2014 ◽  
Vol 10 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Sarah Bouraga ◽  
Ivan Jureta ◽  
Stéphane Faulkner ◽  
Caroline Herssens
Keyword(s):  
Knowledge Base ◽  
Recommender Systems ◽  
Input Data ◽  
Recommendation Systems ◽  
Knowledge Based ◽  
Classification Framework ◽  
Knowledge Content ◽  
Main Ideas

Knowledge-Base Recommendation (or Recommender) Systems (KBRS) provide the user with advice about a decision to make or an action to take. KBRS rely on knowledge provided by human experts, encoded in the system and applied to input data, in order to generate recommendations. This survey overviews the main ideas characterizing a KBRS. Using a classification framework, the survey overviews KBRS components, user problems for which recommendations are given, knowledge content of the system, and the degree of automation in producing recommendations.

scholarly journals Resource Support or Emotional Trust: Effects of Perceived Organizational Support on Entrepreneurial Performance of Global Talents in China

2021 ◽  
Vol 235 ◽  
pp. 03003
Author(s):  
Yongzhou Li ◽  
Shiqiu Liu
Keyword(s):  
Organizational Support ◽  
Emotional Support ◽  
Perceived Organizational Support ◽  
Innovation Network ◽  
Entrepreneurial Performance ◽  
Mediating Role ◽  
Network Embeddedness ◽  
Structural Embeddedness ◽  
Relational Embeddedness ◽  
Resource Support

Global talents are introduced for entrepreneurship and development in China, which is not only a significant way to gather heterogeneous human capital and realize industrial transformation and upgrading in a short period of time, but also a strategic measure to drive innovative development and build an innovative country relying on talents. The regional innovation network gathers innovation elements such as upstream and downstream enterprises, universities and scientific research institutes in the industrial chain, which provides great information and resource support for global talents to gather innovation and entrepreneurship in China. Taking global talents in China as the research object, this paper constructs the relationship model among perceived organizational support, innovation network embeddedness and entrepreneurship performance in innovation network and conducts empirical research. The survey data of Global Talents in China was analyzed by SPSS 24.0 and MPLUS 7.4. The results show that the two dimensions of perceived organizational support instrumentality and emotionality have significant positive impact on entrepreneurial performance and innovation network embeddedness; while innovation network embeddedness has significant positive effects on entrepreneurial performance, but the influence of structural embeddedness is more significant than that of relational embeddedness; relational embeddedness and structural embeddedness play a partial mediating role in the influence of instrumental support and emotional support on technological innovation performance, while structural embeddedness plays a complete mediating role in the influence of instrumental support and emotional support on growth potential performance. Based on the results of empirical research, the paper proposes to further optimize the allocation of network resources, strengthen emotional support, expand the scale of innovative network, and strive to create an international talent development environment that is similar to overseas.

The Intelligent Monitoring System

1990 ◽  
Vol 80 (6B) ◽  
pp. 1833-1851 ◽  
Author(s):  
Thomas C. Bache ◽  
Steven R. Bratt ◽  
James Wang ◽  
Robert M. Fung ◽  
Cris Kobryn ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Monitoring System ◽  
Database Management System ◽  
Automated System ◽  
Seismic Events ◽  
Process Data ◽  
Intelligent Monitoring ◽  
Seismic Arrays ◽  
Interactive Analysis ◽  
Knowledge Based

Abstract The Intelligent Monitoring System (IMS) is a computer system for processing data from seismic arrays and simpler stations to detect, locate, and identify seismic events. The first operational version processes data from two high-frequency arrays (NORESS and ARCESS) in Norway. The IMS computers and functions are distributed between the NORSAR Data Analysis Center (NDAC) near Oslo and the Center for Seismic Studies (Center) in Arlington, Virginia. The IMS modules at NDAC automatically retrieve data from a disk buffer, detect signals, compute signal attributes (amplitude, slowness, azimuth, polarization, etc.), and store them in a commercial relational database management system (DBMS). IMS makes scheduled (e.g., hourly) transfers of the data to a separate DBMS at the Center. Arrival of new data automatically initiates a “knowledge-based system (KBS)” that interprets these data to locate and identify (earthquake, mine blast, etc.) seismic events. This KBS uses general and area-specific seismological knowledge represented in rules and procedures. For each event, unprocessed data segments (e.g., 7 min for regional events) are retrieved from NDAC for subsequent display and analyst review. The interactive analysis modules include integrated waveform and map display/manipulation tools for efficient analyst validation or correction of the solutions produced by the automated system. Another KBS compares the analyst and automatic solutions to mark overruled elements of the knowledge base. Performance analysis statistics guide subsequent changes to the knowledge base so it improves with experience. The IMS is implemented on networked Sun workstations, with a 56 kbps satellite link bridging the NDAC and Center computer networks. The software architecture is modular and distributed, with processes communicating by messages and sharing data via the DBMS. The IMS processing requirements are easily met with major processes (i.e., signal processing, KBS, and DBMS) on separate Sun 4/2xx workstations. This architecture facilitates expansion in functionality and number of stations. The first version was operated continuously for 8 weeks in late-1989. The Center functions were then transferred to NDAC for subsequent operation. Later versions will be distributed among NDAC, Scripps/IGPP (San Diego), and the Center to process data from many stations and arrays. The IMS design is ambitious in its integration of many new computer technologies, but the operational performance of the first version demonstrates its validity. Thus, IMS provides a new generation of automated seismic event monitoring capability.

OPGEN: A System to Identify and Evaluate Potential Applications of Knowledge Based Systems

1992 ◽  
Author(s):  
Samir Rohatgi ◽  
James H. Oliver ◽  
Stuart S. Chen
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Knowledge Based Systems ◽  
The Core ◽  
Knowledge Based ◽  
Evaluation Functions ◽  
Potential Applications ◽  
Computer Based ◽  
Suitable System ◽  
Overall Effectiveness

Abstract This paper describes the development of OPGEN (Opportunity Generator), a computer based system to help identify areas where a knowledge based system (KBS) might be beneficial, and to evaluate whether a suitable system could be developed in that area. The core of the system is a knowledge base used to carry out the identification and evaluation functions. Ancillary functions serve to introduce and demonstrate KBS technology to enhance the overall effectiveness of the system. All aspects of the development, from knowledge acquisition through to testing are presented in this paper.

Using Object-Oriented Frame Language for Realizing Discrete Event Simulation

1996 ◽  
Author(s):  
Ming Dong ◽  
Jianzhong Cha ◽  
Mingcheng E
Keyword(s):  
Knowledge Base ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Object Oriented ◽  
Simulation Models ◽  
Activity Cycle ◽  
Event Simulation ◽  
Knowledge Based ◽  
Inference Engines ◽  
Reasoning About Knowledge

Abstract In this paper, we realize knowledge-based discrete event simulation model’s representation, reasoning and implementation by means of object-oriented(OO) frame language. Firstly, a classes library of simulation models is built by using the OO frame language. And then, behaviours of simulation models can be generated by inference engines reasoning about knowledge base. Lastly, activity cycle diagrams can be used to construct simulation network logic models by connecting the components classes of simulation models. This kind of knowledge-based simulation models can effectively solve the modeling problems of complex and ill-structure systems.

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