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.

Understanding Semantic Web: a conceptual model

2015 ◽  
Vol 64 (1/2) ◽  
pp. 82-100 ◽  
Author(s):  
Michael Calaresu ◽  
Ali Shiri
Keyword(s):  
Semantic Web ◽  
Conceptual Model ◽  
The Body ◽  
Three Dimensional Model ◽  
Conceptual Approach ◽  
Web Based ◽  
Content Type ◽  
Software Applications ◽  
Web Architecture ◽  
The Web

Purpose – The purpose of this article is to explore and conceptualize the Semantic Web as a term that has been widely mentioned in the literature of library and information science. More specifically, its aim is to shed light on the evolution of the Web and to highlight a previously proposed means of attempting to improve automated manipulation of Web-based data in the context of a rapidly expanding base of both users and digital content. Design/methodology/approach – The conceptual analysis presented in this paper adopts a three-dimensional model for the discussion of Semantic Web. The first dimension focuses on Semantic Web’s basic nature, purpose and history, as well as the current state and limitations of modern search systems and related software agents. The second dimension focuses on critical knowledge structures such as taxonomies, thesauri and ontologies which are understood as fundamental elements in the creation of a Semantic Web architecture. In the third dimension, an alternative conceptual model is proposed, one, which unlike more commonly prevalent Semantic Web models, offers a greater emphasis on describing the proposed structure from an interpretive viewpoint, rather than a technical one. This paper adopts an interpretive, historical and conceptual approach to the notion of the Semantic Web by reviewing the literature and by analyzing the developments associated with the Web over the past three decades. It proposes a simplified conceptual model for easy understanding. Findings – The paper provides a conceptual model of the Semantic Web that encompasses four key strata, namely, the body of human users, the body of software applications facilitating creation and consumption of documents, the body of documents themselves and a proposed layer that would improve automated manipulation of Web-based data by the software applications. Research limitations/implications – This paper will facilitate a better conceptual understanding of the Semantic Web, and thereby contribute, in a small way, to the larger body of discourse surrounding it. The conceptual model will provide a reference point for education and research purposes. Originality/value – This paper provides an original analysis of both conceptual and technical aspects of Semantic Web. The proposed conceptual model provides a new perspective on this subject.

An Overview and Technological Background of Semantic Technologies

2021 ◽  
pp. 1-21
Author(s):  
Reinaldo Padilha França ◽  
Ana Carolina Borges Monteiro ◽  
Rangel Arthur ◽  
Yuzo Iano
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Data Representation ◽  
Current Data ◽  
The Internet ◽  
Semantic Technologies ◽  
Web Based ◽  
Web 3.0 ◽  
Technological Advances ◽  
The Web

The Semantic Web concept is an extension of the web obtained by adding semantics to the current data representation format. It is considered a network of correlating meanings. It is the result of a combination of web-based conceptions and technologies and knowledge representation. Since the internet has gone through many changes and steps in its web versions 1.0, 2.0, and Web 3.0, this last call of smart web, the concept of Web 3.0, is to be associated with the Semantic Web, since technological advances have allowed the internet to be present beyond the devices that were made exactly with the intention of receiving the connection, not limited to computers or smartphones since it has the concept of reading, writing, and execution off-screen, performed by machines. Therefore, this chapter aims to provide an updated review of Semantic Web and its technologies showing its technological origins and approaching its success relationship with a concise bibliographic background, categorizing and synthesizing the potential of technologies.

Conclusions

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Large Scale ◽  
Web Search ◽  
Web Based ◽  
E Learning ◽  
Computer Based ◽  
Intelligent Devices ◽  
Automated Data Integration ◽  
The Web

At the start of this book we outlined the challenges of automatic computer based processing of information on the Web. These numerous challenges are generally referred to as the ‘vision’ of the Semantic Web. From the outset, we have attempted to take a realistic and pragmatic view of this vision. Our opinion is that the vision may never be fully realized, but that it is a useful goal on which to focus. Each step towards the vision has provided new insights on classical problems in knowledge representation, MASs, and Web-based techniques. Thus, we are presently in a significantly better position as a result of these efforts. It is sometimes difficult to see the purpose of the Semantic Web vision behind all of the different technologies and acronyms. However, the fundamental purpose of the Semantic Web is essentially large scale and automated data integration. The Semantic Web is not just about providing a more intelligent kind of Web search, but also about taking the results of these searches and combining them in interesting and useful ways. As stated in Chapter 1, the possible applications for the Semantic Web include: automated data mining, e-science experiments, e-learning systems, personalized newspapers and journals, and intelligent devices. The current state of progress towards the Semantic Web vision is summarized in Figure 8.1. This figure shows a pyramid with the human-centric Web at the bottom, sometimes termed the Syntactic Web, and the envisioned Semantic Web at the top. Throughout this book, we have been moving upwards on this pyramid, and it should be clear that a great deal of progress that has been made towards the goal. This progress is indicated by the various stages of the pyramid, which can be summarized as follows: • The lowest stage on the pyramid is the basic Web that should be familiar to everyone. This Web of information is human-centric and contains very little automation. Nonetheless, the Web provides the basic protocols and technologies on which the Semantic Web is founded. Furthermore, the information which is represented on the Web will ultimately be the source of knowledge for the Semantic Web.

scholarly journals Trust management in opportunisitic networks: a semantic web-based approach

2021 ◽  
Author(s):  
Elvira Noelly Bonilla Tamez
Keyword(s):  
Semantic Web ◽  
Trust Management ◽  
Opportunistic Networks ◽  
Use Case ◽  
New Paradigm ◽  
Case Scenario ◽  
Web Based ◽  
New Vision ◽  
The Web ◽  
Service Framework

The need for having a mechanism to automatically interpret content available on the Web without a human intervention has lead to the development of a new vision for the next generation of the Web, known as the Semantic Web. This new paradigm advocates the use of ontologies to achieve a common language for communication among humans, computers, and programs. In this thesis, a novel Semantic Web-based solution called SCOW-Q (Semantic Capability Discovery With QoS) model, is proposed, which provides an architectural basis for representing trust and trust management in Opportunistic Networks. The model is validated by means of a Use Case Scenario using a well-defined Semantic Web Service framework.

Semantic Web Based Agricultural Information Integration

2017 ◽  
Vol 8 (3) ◽  
pp. 39-51 ◽  
Author(s):  
Kaladevi Ramar ◽  
Geetha Gurunathan
Keyword(s):  
Information System ◽  
Semantic Web ◽  
Information Integration ◽  
Relevant Information ◽  
Integrated Systems ◽  
Web Based ◽  
Prevention Methods ◽  
Available Information ◽  
Semantic Linkage ◽  
The Web

Huge volume of information is available in the WWW. However, the demand is on relevant information rather than available information, which are often heterogeneous and distributed. Agriculture is one such domain, which includes information like soil, crops, weather, etc., under one roof. This information is in different representations and structures e.g. weather. This scenario leads to a challenge that how to integrate the available and heterogeneous agricultural information to deliver better production. The information on the web is syntactically structured but, the need is to provide semantic linkage. The semantic web supports the existing web to easily process and interpret information. In this paper, a semantic based Agricultural Information System (AIS) is proposed which addresses heterogeneity issues among weather systems and integrates information like soil, weather, crop and fertilizers. AIS helps the farmers regarding the type of crop/soil, crop/climate, fertilizer applications, diseases and prevention methods using effective retrieval of information from integrated systems.

Building a New Semantic Social Network Using Semantic Web-Based Techniques

2021 ◽  
pp. 54-65
Author(s):  
admin admin ◽  
◽  
◽  
◽  
Khlid M. .. ◽  
...  
Keyword(s):  
Semantic Web ◽  
Social Networking ◽  
Web Application ◽  
Web Search ◽  
Public Information ◽  
Social Networking Site ◽  
Semantic Search ◽  
Web Based ◽  
Social Community ◽  
The Web

Most people are more or less related to the web by participating in a kind of social networking site. Semantic Web technology plays a crucial role in these sites as they contain an enormous amount of data about ‎persons, pages, events, places, corporations, etc. This research is a Semantic Web application designed to create a new ‎semantic social community called Socialpedia. It links the already existing social public information to the newly ‎public ones. This information is linked with different information on the web to construct a new immense ‎data container. The resulting data container can be processed using a variety of Semantic Web techniques to produce ‎machine-understandable content. This content shows the promise of using integrated data to improve Web search and ‎Web-scale data analysis, unlike conventional search engines or social ones. This community involves obtaining data ‎from traditional users known as contributors or participants, linking data from existing social networks, extracting ‎structured data in triples using predefined ontologies, and finally querying and inferring such data to obtain ‎meaningful pieces of information. Socailpedia supports all popular functionalities of social networking websites ‎besides the enhanced features of the Semantic Web, providing advanced semantic search that acts as a semantic ‎search engine.

scholarly journals Design and Implementation of Adaptive Recommendation System

2018 ◽  
pp. 101-117
Author(s):  
MagedEla zony ◽  
Ahmed Khalifa ◽  
Sayed Nouh ◽  
Mohamed Hussein
Keyword(s):  
Semantic Web ◽  
Semantic Similarity ◽  
Recommendation System ◽  
Learning Objects ◽  
Just In Time ◽  
Web Based ◽  
Design And Implementation ◽  
Knowledge Based ◽  
E Learning ◽  
Learner Profile

E-learning offers advantages for E-learners by making access to learning objects at any time or place, very fast, just-in-time and relevance. However, with the rapid increase of learning objects and it is syntactically structured it will be time-consuming to find contents they really need to study.In this paper, we design and implementation of knowledge-based industrial reusable, interactive web-based training and use semantic web based e-learning to deliver learning contents to the learner in flexible, interactive, and adaptive way. The semantic and recommendation and personalized search of Learning objects is based on the comparison of the learner profile and learning objects to determine a more suitable relationship between learning objects and learner profiles. Therefore, it will advise the e-learner with most suitable learning objects using the semantic similarity.

Agency and the Semantic Web

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Multi Agent Systems ◽  
Web Content ◽  
Semantic Web Technologies ◽  
Research Issues ◽  
Agent Systems ◽  
Knowledge Based ◽  
Multi Agent ◽  
The University ◽  
The Web

This highly topical text considers the construction of the next generation of the Web, called the Semantic Web. This will enable computers to automatically consume Web-based information, overcoming the human-centric focus of the Web as it stands at present, and expediting the construction of a whole new class of knowledge-based applications that will intelligently utilize Web content. The text is structured into three main sections on knowledge representation techniques, reasoning with multi-agent systems, and knowledge services. For each of these topics, the text provides an overview of the state-of-the-art techniques and the popular standards that have been defined. Numerous small programming examples are given, which demonstrate how the benefits of the Semantic Web technologies can be realized at the present time. The main theoretical results underlying each of the technologies are presented, and the main problems and research issues which remain are summarized. Based on a course on 'Multi-Agent Systems and the Semantic Web' taught at the University of Edinburgh, this text is ideal for final-year undergraduate and graduate students in Mathematics, Computer Science, Artificial Intelligence, and Logic and researchers interested in Multi-Agent Systems and the Semantic Web.

Interactive knowledge capture in the new millennium: how the Semantic Web changed everything

2011 ◽  
Vol 26 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Yolanda Gil
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Shared Knowledge ◽  
Knowledge Capture ◽  
The World ◽  
Domain Expertise ◽  
Single User ◽  
New Challenges ◽  
Collaborative Knowledge ◽  
The Web

AbstractThe Semantic Web has radically changed the landscape of knowledge acquisition research. It used to be the case that a single user would edit a local knowledge base, that the user would have domain expertise to add to the system, and that the system would have a centralized knowledge base and reasoner. The world surrounding knowledge-rich systems changed drastically with the advent of the Web, and many of the original assumptions were no longer a given. Those assumptions had to be revisited and addressed in combination with new challenges that were put forward. Knowledge-rich systems today are distributed, have many users with different degrees of expertise, and integrate many shared knowledge sources of varying quality. Recent work in interactive knowledge capture includes new and exciting research on collaborative knowledge sharing, collecting knowledge from Web volunteers, and capturing knowledge provenance.

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