scholarly journals Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts

2021 ◽  
Vol 11 (17) ◽  
pp. 8072
Author(s):  
Hossam A. Gabbar ◽  
Sk Sami Al Jabar ◽  
Hassan A. Hassan ◽  
Jing Ren
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Semantic Network ◽  
Human Experience ◽  
Vital Role ◽  
Knowledge Network ◽  
Specific Domain ◽  
Crucial Component ◽  
Research Gap ◽  
Key Phrases

An organized knowledge structure or knowledge base plays a vital role in retaining knowledge where data are processed and organized so that machines can understand. Instructive text (iText) consists of a set of instructions to accomplish a task or operation. Hence, iText includes a group of texts having a title or name of the task or operation and step-by-step instructions on how to accomplish the task. In the case of iText, storing only entities and their relationships with other entities does not always provide a solution for capturing knowledge from iTexts as it consists of parameters and attributes of different entities and their action based on different operations or procedures and the values differ for every individual operation or procedure for the same entity. There is a research gap in iTexts that created limitations to learn about different operations, capture human experience and dynamically update knowledge for every individual operation or instruction. This research presents a knowledge base for capturing and retaining knowledge from iTexts existing in operational documents. From each iTexts, small pieces of knowledge are extracted and represented as nodes linked to one another in the form of a knowledge network called the human experience semantic network (HESN). HESN is the crucial component of our proposed knowledge base. The knowledge base also consists of domain knowledge having different classified terms and key phrases of the specific domain.

A Fixpoint Semantics for Rule-Base Anomalies

2011 ◽  
pp. 265-276 ◽  
Author(s):  
Du Zhang
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Intelligent System ◽  
Domain Analysis ◽  
Rule Base ◽  
Crucial Component ◽  
Formal Definitions ◽  
Fixpoint Semantics ◽  
Verification Process ◽  
And Performance

A crucial component of an intelligent system is its knowledge base that contains knowledge about a problem domain. Knowledge base development involves domain analysis, context space definition, ontological specification, and knowledge acquisition, codification and verification. Knowledge base anomalies can affect the correctness and performance of an intelligent system. In this chapter, we describe a fixpoint semantics for a knowledge base that is based on a multi-valued logic. We then use the fixpoint semantics to provide formal definitions for four types of knowledge base anomalies: inconsistency, redundancy, incompleteness, circularity. We believe such formal definitions of knowledge base anomalies will help pave the way for a more effective knowledge base verification process.

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

Identifying The Competencies Possessed By The Engineering College Faculty In India

2019 ◽  
Vol 118 (4) ◽  
pp. 68-71
Author(s):  
Mohanakumari. D ◽  
R. Magesh
Keyword(s):  
Domain Knowledge ◽  
Teaching And Learning ◽  
Vital Role ◽  
Faculty Members ◽  
Approaches To Teaching ◽  
Global Workforce ◽  
Content Domain ◽  
Technology Changes ◽  
Education In India ◽  
Pre Service Teacher

The main intention of the Paper is identifying the competencies possessed by the faculty in engineering college and adequate skills of all the disciplines required and that plays a vital role in educational institutions.In this era, engineering education in India faces major challenges as it requires meeting the demands of technical profession and emerging job market. Researchers have created some universally desired, yet challenging skills for global workforce. Nowadays, technology changes rapidly, so we have to update our self-according to the changing world, i.e., infrastructure, content/domain knowledge, educators/HR trainers. Thus, our technical faculty members should necessary to learn the innovative approaches to teaching and learning, which in turn will require effective professional development for both new and experienced instructors alike. It is right time now to redesign our curriculam, pedagogy and make the pre-service teacher preparation programme mandatory part of technical higher education.

Application of a methodological approach to compare ontologies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yudith Cardinale ◽  
Maria Alejandra Cornejo-Lupa ◽  
Alexander Pinto-De la Gala ◽  
Regina Ticona-Herrera
Keyword(s):  
Domain Knowledge ◽  
Design Methodology ◽  
Methodological Approach ◽  
Quality Model ◽  
Specific Domain ◽  
Content Type ◽  
Localization And Mapping ◽  
Golden Standard ◽  
Knowledge Levels ◽  
Practical Implications

Purpose This study aims to the OQuaRE quality model to the developed methodology. Design/methodology/approach Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them. Findings In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain. Practical implications To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains. Originality/value Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.

scholarly journals Knowledge Derived From Wikipedia For Computing Semantic Relatedness

2007 ◽  
Vol 30 ◽  
pp. 181-212 ◽  
Author(s):  
S. P. Ponzetto ◽  
M. Strube
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Search Engine ◽  
Semantic Network ◽  
Semantic Relatedness ◽  
Coreference Resolution ◽  
Valuable Resource ◽  
Resolution System

Wikipedia provides a semantic network for computing semantic relatedness in a more structured fashion than a search engine and with more coverage than WordNet. We present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet on some datasets. We also address the question whether and how Wikipedia can be integrated into NLP applications as a knowledge base. Including Wikipedia improves the performance of a machine learning based coreference resolution system, indicating that it represents a valuable resource for NLP applications. Finally, we show that our method can be easily used for languages other than English by computing semantic relatedness for a German dataset.

Approaches for Semantic Association Mining and Hidden Entities Extraction in Knowledge Base

2010 ◽  
pp. 119-141 ◽  
Author(s):  
Thabet Slimani ◽  
Boutheina Ben Yaghlane ◽  
Khaled Mellouli
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Domain Knowledge ◽  
Information And Communications Technology ◽  
Association Mining ◽  
Semantic Association ◽  
Real World Data ◽  
Semantic Associations ◽  
Hyperclique Pattern ◽  
Flow Of Information

Due to the rapidly increasing use of information and communications technology, Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is represented by means of an ontology in order to support reasoning performed by a machine. A semantic association (SA) is a set of relationships between two entities in knowledge base represented as graph paths consisting of a sequence of links. Because the number of relationships between entities in a knowledge base might be much greater than the number of entities, it is recommended to develop tools and invent methods to discover new unexpected links and relevant semantic associations in the large store of the preliminary extracted semantic association. Semantic association mining is a rapidly growing field of research, which studies these issues in order to create efficient methods and tools to help us filter the overwhelming flow of information and extract the knowledge that reflect the user need. The authors present, in this work, an approach which allows the extraction of association rules (SWARM: Semantic Web Association Rule Mining) from a structured semantic association store. Then, present a new method which allows the discovery of relevant semantic associations between a preliminary extracted SA and predefined features, specified by user, with the use of Hyperclique Pattern (HP) approach. In addition, the authors present an approach which allows the extraction of hidden entities in knowledge base. The experimental results applied to synthetic and real world data show the benefit of the proposed methods and demonstrate their promising effectiveness.

Web knowledge

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Web Applications ◽  
Semantic Network ◽  
Knowledge Bases ◽  
Web Standards ◽  
Description Framework ◽  
The Web

In the introductory chapter of this book, we discussed the means by which knowledge can be made available on the Web. That is, the representation of the knowledge in a form by which it can be automatically processed by a computer. To recap, we identified two essential steps that were deemed necessary to achieve this task: 1. We discussed the need to agree on a suitable structure for the knowledge that we wish to represent. This is achieved through the construction of a semantic network, which defines the main concepts of the knowledge, and the relationships between these concepts. We presented an example network that contained the main concepts to differentiate between kinds of cameras. Our network is a conceptualization, or an abstract view of a small part of the world. A conceptualization is defined formally in an ontology, which is in essence a vocabulary for knowledge representation. 2. We discussed the construction of a knowledge base, which is a store of knowledge about a domain in machine-processable form; essentially a database of knowledge. A knowledge base is constructed through the classification of a body of information according to an ontology. The result will be a store of facts and rules that describe the domain. Our example described the classification of different camera features to form a knowledge base. The knowledge base is expressed formally in the language of the ontology over which it is defined. In this chapter we elaborate on these two steps to show how we can define ontologies and knowledge bases specifically for the Web. This will enable us to construct Semantic Web applications that make use of this knowledge. The chapter is devoted to a detailed explanation of the syntax and pragmatics of the RDF, RDFS, and OWL Semantic Web standards. The resource description framework (RDF) is an established standard for knowledge representation on the Web. Taken together with the associated RDF Schema (RDFS) standard, we have a language for representing simple ontologies and knowledge bases on the Web.

Research on Knowledge Base of Device Test Training System Based on Rough Set Data Mining

2013 ◽  
Vol 645 ◽  
pp. 232-238
Author(s):  
Qing Li ◽  
Wei Yang ◽  
Xiao Nan Ye ◽  
Xiao Xiao Ma
Keyword(s):  
Data Mining ◽  
Knowledge Base ◽  
Rough Set ◽  
Domain Knowledge ◽  
Simulation Training ◽  
Device Simulation ◽  
Training System ◽  
Base System ◽  
Data Mining Algorithms ◽  
Device Test

The realization of a device test training system requires the use of a lot of domain knowledge, and building knowledge base will play an important role. In view of the uncertainty, inaccuracy and incompleteness of test data in the testing process, this paper makes the data mining algorithms based on rough set as knowledge acquisition algorithm, and proposes an improved algorithm for insufficient of approximate reduction of rough set knowledge based on the tolerance relation of incomplete information system. The paper studies the design and realization of knowledge base system in the developing of device simulation training system on this basis, and validates the method through a design example of knowledge base of a certain device simulation training system.

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