scholarly journals Modeling domain knowledge based on the central laws of integrative brain activity

2016 ◽  
Vol 2 (2(28)) ◽  
pp. 33
Author(s):  
Сергій Ілліч Доценко
Keyword(s):  
Domain Knowledge ◽  
Brain Activity ◽  
Knowledge Based

Configuration Tree Solver: A Technology for Automated Design and Configuration

1990 ◽  
Author(s):  
Alexander Kott ◽  
Gerald Agin ◽  
Dave Fawcett
Keyword(s):  
Problem Solving ◽  
General Model ◽  
General Class ◽  
Domain Knowledge ◽  
Automated Design ◽  
Automotive Component ◽  
Business Operation ◽  
Knowledge Based ◽  
Specific Configuration

Abstract Configuration is a process of generating a definitive description of a product or an order that satisfies a set of specified requirements and known constraints. Knowledge-based technology is an enabling factor in automation of configuration tasks found in the business operation. In this paper, we describe a configuration technique that is well suited for configuring “decomposable” artifacts with reasonably well defined structure and constraints. This technique may be classified as a member of a general class of decompositional approaches to configuration. The domain knowledge is structured as a general model of the artifact, an and-or hierarchy of the artifact’s elements, features, and characteristics. The model includes constraints and local specialists which are attached to the elements of the and-or-tree. Given the specific configuration requirements, the problem solving engine searches for a solution, a subtree, that satisfies the requirements and the applicable constraints. We describe an application of this approach that performs configuration and design of an automotive component.

Predicting readers’ domain knowledge based on eye-tracking measures

2018 ◽  
Vol 36 (6) ◽  
pp. 1027-1042 ◽  
Author(s):  
Quan Lu ◽  
Jiyue Zhang ◽  
Jing Chen ◽  
Ji Li
Keyword(s):  
Logistic Regression ◽  
Eye Tracking ◽  
Information Search ◽  
Domain Knowledge ◽  
Fixation Duration ◽  
Baseline Model ◽  
Content Type ◽  
Knowledge Based ◽  
Total Fixation ◽  
Negative Predictor

Purpose This paper aims to examine the effect of domain knowledge on eye-tracking measures and predict readers’ domain knowledge from these measures in a navigational table of contents (N-TOC) system. Design/methodology/approach A controlled experiment of three reading tasks was conducted in an N-TOC system for 24 postgraduates of Wuhan University. Data including fixation duration, fixation count and inter-scanning transitions were collected and calculated. Participants’ domain knowledge was measured by pre-experiment questionnaires. Logistic regression analysis was leveraged to build the prediction model and the model’s performance was evaluated based on baseline model. Findings The results showed that novices spent significantly more time in fixating on text area than experts, because of the difficulty of understanding the information of text area. Total fixation duration on text area (TFD_T) was a significantly negative predictor of domain knowledge. The prediction performance of logistic regression model using eye-tracking measures was better than baseline model, with the accuracy, precision and F(β = 1) scores to be 0.71, 0.86, 0.79. Originality/value Little research has been reported in literature on investigation of domain knowledge effect on eye-tracking measures during reading and prediction of domain knowledge based on eye-tracking measures. Most studies focus on multimedia learning. With respect to the prediction of domain knowledge, only some studies are found in the field of information search. This paper makes a good contribution to the literature on the effect of domain knowledge on eye-tracking measures during N-TOC reading and predicting domain knowledge.

Domain Knowledge-Based Compaction

2013 ◽  
pp. 125-145
Author(s):  
T. Ravindra Babu ◽  
M. Narasimha Murty ◽  
S. V. Subrahmanya
Keyword(s):  
Domain Knowledge ◽  
Knowledge Based

scholarly journals A Model of Domain Knowledge Content Updating Based on Management Information Interactions

2019 ◽  
Vol 85 ◽  
pp. 69-97
Author(s):  
Jurij Tekutov ◽  
Saulius Gudas ◽  
Vitalijus Denisovas ◽  
Julija Smirnova
Keyword(s):  
Value Chain ◽  
Domain Knowledge ◽  
Chain Model ◽  
Management Information ◽  
Process Measures ◽  
Knowledge Database ◽  
Knowledge Based ◽  
Educational Domain ◽  
Case System ◽  
Knowledge Content

The hierarchical Detailed Value Chain Model and the Elementary Management Cycle model of educational domain knowledge content updating are formally described in this paper, wherein computerized process measures are also proposed. The paper provides a method for updating the knowledge of the analyzed domain, referred to as the “enterprise domain,” based on enterprise modelling in terms of management information interactions. A method was designed, the formal DVCM and EMC descriptions of which are provided in the BPMN notation, allowing to develop a two-level (granular) model for describing the knowledge of educational domain management information interactions. In implementing this model and its algorithms in technological terms, a subsystem of enterprise knowledge has been created in a knowledge-based CASE system (computerized knowledge-based IS engineering), which performs the function of a domain knowledge database.

Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation

2019 ◽  
Vol 18 (01) ◽  
pp. 311-338 ◽  
Author(s):  
Lingling Zhang ◽  
Jing Li ◽  
Qiuliu Zhang ◽  
Fan Meng ◽  
Weili Teng
Keyword(s):  
Bipartite Graph ◽  
Link Prediction ◽  
Domain Knowledge ◽  
Attenuation Factor ◽  
Prediction Algorithm ◽  
Bipartite Network ◽  
Topological Similarity ◽  
Product Recommendation ◽  
Knowledge Based ◽  
Context Knowledge

In this paper, we propose domain knowledge-based link prediction algorithm in customer-product bipartite network to improve effectiveness of product recommendation in retail. The domain knowledge is classified into product domain knowledge and time context knowledge, which play an important part in link prediction. We take both of them into consideration in recommendation and form a unified domain knowledge-based link prediction framework. We capture product semantic similarity by ontology-based analysis and time attenuation factor from time context knowledge, then incorporate them into network topological similarity to form a new linkage measure. To evaluate the algorithm, we use a real retail transaction dataset from Food Mart. Experimental results demonstrate that the usage of domain knowledge in link prediction achieved significantly better performance.

scholarly journals A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hao Chen ◽  
Baorong Zhai ◽  
Jiangjiang Wu ◽  
Chun Du ◽  
Jun Li
Keyword(s):  
Data Transmission ◽  
Domain Knowledge ◽  
Scheduling Algorithm ◽  
Satellite Observation ◽  
Observation Data ◽  
Transmission Scheduling ◽  
Expected Time ◽  
Knowledge Based ◽  
Sensing Applications ◽  
Speed Up

The scheduling of Earth Observation Satellite (EOS) data transmission is a complex combinatorial optimization problem. With the development of remote sensing applications, a new special requirement named data transmission oriented to topics has appeared. It supposes that the data obtained from each observation activity by satellites belong to certain observation data topics, and every observation data topic has completeness and timeliness requirements. Unless all of the observation data belonging to one topic has been transmitted to the ground before the expected time, the value of the observation data will be decayed sharply and only a part of the rewards (or even no reward) for the data transmission will be obtained. Current researches do not meet the new data topic transmission requirements well. Based on the characteristics of the problem, a mathematic scheduling model is established, and a novel hybrid scheduling algorithm based on evolutionary computation is proposed. In order to further enhance the performance and speed up the convergence process of our algorithm, a domain-knowledge-based mutation operator is designed. Quantitative experimental results show that the proposed algorithm is more effective to solve the satellite observation data topic transmission scheduling problem than that of the state-of-the-art approaches.

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