scholarly journals Knowledge-based Data Mining Using Semantic Web

IERI Procedia ◽  
2014 ◽  
Vol 7 ◽  
pp. 113-119 ◽  
Author(s):  
Sumaiya Kabir ◽  
Shamim Ripon ◽  
Mamunur Rahman ◽  
Tanjim Rahman
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Knowledge Based

scholarly journals Proposing an Ontology Model for Planning Photovoltaic Systems

2021 ◽  
Vol 3 (3) ◽  
pp. 582-600
Author(s):  
Farhad Khosrojerdi ◽  
Stéphane Gagnon ◽  
Raul Valverde
Keyword(s):  
Solar Energy ◽  
Semantic Web ◽  
Relevant Information ◽  
Pv System ◽  
Semantic Web Technologies ◽  
Pv Systems ◽  
Knowledge Based ◽  
Point Tracking ◽  
Power Point ◽  
Energy Community

The performance of a photovoltaic (PV) system is negatively affected when operating under shading conditions. Maximum power point tracking (MPPT) systems are used to overcome this hurdle. Designing an efficient MPPT-based controller requires knowledge about power conversion in PV systems. However, it is difficult for nontechnical solar energy consumers to define different parameters of the controller and deal with distinct sources of data related to the planning. Semantic Web technologies enable us to improve knowledge representation, sharing, and reusing of relevant information generated by various sources. In this work, we propose a knowledge-based model representing key concepts associated with an MPPT-based controller. The model is featured with Semantic Web Rule Language (SWRL), allowing the system planner to extract information about power reductions caused by snow and several airborne particles. The proposed ontology, named MPPT-On, is validated through a case study designed by the System Advisor Model (SAM). It acts as a decision support system and facilitate the process of planning PV projects for non-technical practitioners. Moreover, the presented rule-based system can be reused and shared among the solar energy community to adjust the power estimations reported by PV planning tools especially for snowy months and polluted environments.

Metaphorical Communication about Nature

2014 ◽  
pp. 208-224
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Semantic Web ◽  
Data Visualization ◽  
Knowledge Visualization ◽  
Art And Science ◽  
Learning And Teaching ◽  
Mathematical Equations ◽  
Abstract Ideas

Metaphors are present in our thoughts and make invisible concepts perceivable. The metaphorical way of perceptual imaging is discussed in this chapter, particularly the use of art and graphic metaphors for concept visualization. We may describe with metaphors the structure and the relations among several kinds of data. Metaphors may represent mathematical equations or geometrical curves and thus make abstract ideas visible. Most metaphors originate from biology-inspired thinking. Nature-derived metaphors support data visualization, information and knowledge visualization, data mining, Semantic Web, swarm computing, cloud computing, and serve as the enrichment of interdisciplinary models. This chapter examines examples of combining metaphorical visualization with artistic principles, and then describes the metaphorical way of learning and teaching with art and graphic metaphors aimed at improving one’s power of conveying meaning, integrating art and science, and visualizing knowledge.

Semantic Web Services-Based Knowledge Management Framework

2016 ◽  
pp. 429-438
Author(s):  
Vili Podgorelec ◽  
Boštjan Grašič
Keyword(s):  
Knowledge Management ◽  
Web Services ◽  
Semantic Web ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Semantic Web Services ◽  
Semantic Web Technologies ◽  
Management Framework ◽  
Knowledge Based ◽  
Service Oriented

In this chapter, a Semantic Web services-based knowledge management framework that enables holistic knowledge management in organizations is presented. As the economy is becoming one single global marketplace, where the best offer wins, organizations have to search for competitive advantage within themselves. With the growing awareness that key potentials of an organization lie within its people and their knowledge, efficient knowledge management is becoming one of key focuses in organizational activities. The proposed knowledge management framework is based on Semantic Web technologies and service-oriented architecture, supporting the operational business processes as well as knowledge-based management of services in service-oriented architecture.

Semantic Web, Ontology, and Linked Data

Web Services ◽  
2019 ◽  
pp. 127-148 ◽  
Author(s):  
Anindya Basu
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Knowledge Organization ◽  
Human Being ◽  
Data Modelling ◽  
Amount Of Information ◽  
Knowledge Based ◽  
Enormous Amount ◽  
The Future

Enormous amount of information is being produced every day and get consumed according to the needs of human being. Semantic web and ontology represent information which are machine processable and understand the semantics present among the entities. Ontology can be represented as Knowledge Organization and data modelling tool. Librarians are designated as “Information Custodian” or “Knowledge Keepers”. Implication and application of concepts in LIS can play big role in shaping knowledge-based services and mining and inferring them in better way in future. Ontology and semantic web is the future of LIS as speculated by several professionals and experts. This chapter would delineate a basic overview of Semantic Web, Ontology and linked data.

Fuzzy Methods in Data Mining

2011 ◽  
pp. 907-912 ◽  
Author(s):  
Eyke Hüllermeier
Keyword(s):  
Data Mining ◽  
Knowledge Acquisition ◽  
Control Function ◽  
Knowledge Discovery In Databases ◽  
Knowledge Based ◽  
Automated Learning ◽  
Long Time ◽  
Basic Ideas ◽  
Inference Techniques ◽  
Tools And Techniques

Tools and techniques that have been developed during the last 40 years in the field of fuzzy set theory (FST) have been applied quite successfully in a variety of application areas. A prominent example of the practical usefulness of corresponding techniques is fuzzy control, where the idea is to represent the input-output behaviour of a controller (of a technical system) in terms of fuzzy rules. A concrete control function is derived from such rules by means of suitable inference techniques. While aspects of knowledge representation and reasoning have dominated research in FST for a long time, problems of automated learning and knowledge acquisition have more and more come to the fore in recent years. There are several reasons for this development, notably the following: Firstly, there has been an internal shift within fuzzy systems research from “modelling” to “learning”, which can be attributed to the awareness that the well-known “knowledge acquisition bottleneck” seems to remain one of the key problems in the design of intelligent and knowledge-based systems. Secondly, this trend has been further amplified by the great interest that the fields of knowledge discovery in databases (KDD) and its core methodical component, data mining, have attracted in recent years. It is hence hardly surprising that data mining has received a great deal of attention in the FST community in recent years (Hüllermeier, 2005). The aim of this chapter is to give an idea of the usefulness of FST for data mining. To this end, we shall briefly highlight, in the next but one section, some potential advantages of fuzzy approaches. In preparation, the next section briefly recalls some basic ideas and concepts from FST. The style of presentation is purely non-technical throughout; for technical details we shall give pointers to the literature.

Reactive agents

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Web Based ◽  
Knowledge Based ◽  
Sources Of Knowledge ◽  
Complex Organization ◽  
Standard Manner ◽  
Single User ◽  
Complex Knowledge ◽  
The Web

In the previous chapter we described three languages for representing knowledge on the Semantic Web: RDF, RDFS, and OWL. These languages enable us to create Web-based knowledge in a standard manner with a common semantics. We now turn our attention to the techniques that can utilize this knowledge in an automated manner. These techniques are fundamental to the construction of the Semantic Web, as without automation we do not gain any real benefit over the current Web. There are currently two views of the Semantic Web that have implications for the kind of automation that we can hope to achieve: 1. An expert system with a distributed knowledge base. 2. A society of agents that solve complex knowledge-based tasks. In the first view, the Semantic Web is essentially treated a single-user application that reasons about some Web-based knowledge. For example, a service that queries the knowledge to answer specific questions. This is a perfectly acceptable view, and its realization is significantly challenging. However, in this book we primarily subscribe to the second view. In this more-generalized view, the knowledge is not treated as a single body, and it is not necessary to obtain a global view of the knowledge. Instead, the knowledge is exchanged and manipulated in a peer-to-peer (P2P) manner between different entities. These entities act on behalf of human users, and require only enough knowledge to perform the task to which they are assigned. The use of entities to solve complex problems on the Web is captured by the notion of an agent. In human terms, an agent is an intermediary who makes a complex organization externally accessible. For example, a travel agent simplifies the problem of booking a holiday. This concept of simplifying the interface to a complex framework is a key goal of the Semantic Web. We would like to make it straightforward for a human to interact with a wide variety of disparate sources of knowledge without becoming mired in the details. To accomplish this, we want to define software agents that act with similar characteristics to human agents.

Expression and Processing of Inductive Queries

2009 ◽  
pp. 518-526
Author(s):  
Edgard Benítez-Guerrero ◽  
Omar Nieva-García
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Pattern Discovery ◽  
Data Preprocessing ◽  
Knowledge Discovery In Databases ◽  
Digital Information ◽  
Interactive Process ◽  
Useful Knowledge ◽  
Knowledge Based ◽  
Computer Based

The vast amounts of digital information stored in databases and other repositories represent a challenge for finding useful knowledge. Traditionalmethods for turning data into knowledge based on manual analysis reach their limits in this context, and for this reason, computer-based methods are needed. Knowledge Discovery in Databases (KDD) is the semi-automatic, nontrivial process of identifying valid, novel, potentially useful, and understandable knowledge (in the form of patterns) in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). KDD is an iterative and interactive process with several steps: understanding the problem domain, data preprocessing, pattern discovery, and pattern evaluation and usage. For discovering patterns, Data Mining (DM) techniques are applied.

Ontologies in the Health Field

2011 ◽  
pp. 37-56 ◽  
Author(s):  
Michel Simonet ◽  
Radja Messai ◽  
Gayo Diallo
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Semantic Web ◽  
Knowledge Bases ◽  
Health Data ◽  
Efficient Manner ◽  
Common Definition ◽  
Ontology Design ◽  
Definition Of ◽  
Health Field

Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status of the Semantic Web. Although there is no consensus on a common definition of an ontology, it is necessary to understand their main features to be able to use them in a pertinent and efficient manner for data mining purposes. This chapter introduces the basic notions about ontologies, presents a survey of their use in medicine and explores some related issues: knowledge bases, terminology, and information retrieval. It also addresses the issues of ontology design, ontology representation, and the possible interaction between data mining and ontologies.

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