This document summarizes an academic paper that describes an ontology for representing web services using the Web Services Description Language (WSDL) and the Resource Description Framework (RDF). The paper discusses how ontologies provide a set of rules for describing domains and supporting reasoning. It then provides background on WSDL for describing web services and RDF for representing ontologies using graphs. The paper proposes using WSDL and RDF together to describe ontologies for web services.
This document summarizes the first meeting of the Knowledge Representation seminar at Kings College London in June 2010. It discusses ontologies from three perspectives:
1) The theoretical perspective defines ontologies and discusses different definitions.
2) The pragmatic perspective explains what ontologies are used for.
3) The design perspective outlines how to build ontologies and discusses components like logic, ontology, and computation.
The document also covers topics like the differences between ontologies and data models or knowledge bases, degrees of "ontological depth", upper vs. domain ontologies, examples of top-level ontologies, and realist vs. conceptualist perspectives on ontologies.
Representation of ontology by Classified Interrelated object modelMihika Shah
1. The document discusses representing ontology using the Classified Interrelated Object Model (CIOM) data modeling technique. CIOM represents ontology components like classes, subclasses, attributes, and relationships between classes.
2. Key components of an ontology like classes, subclasses, attributes, and inter-class relationships are described and examples are given of how each would be represented using CIOM notation.
3. CIOM provides a general purpose methodology for representing ontologies using existing database technologies and overcomes limitations of specialized ontology languages and tools.
The document discusses the basics of ontologies, including their origin in philosophy, definitions, types, benefits and application areas. Some key points are:
- An ontology is a formal specification of a conceptualization used to help humans and programs share knowledge. It establishes a shared vocabulary for exchanging information.
- Ontologies describe domain knowledge and provide an agreed-upon understanding of a domain through concepts and relations. They help solve problems of ambiguity and enable knowledge sharing.
- Ontologies benefit applications like information retrieval, digital libraries, knowledge engineering and natural language processing by facilitating semantic search and integration of data.
A Semi-Automatic Ontology Extension Method for Semantic Web ServicesIDES Editor
this paper provides a novel semi-automatic ontology
extension method for Semantic Web Services (SWS). This is
significant since ontology extension methods those existing
in literature mostly deal with semantic description of static
Web resources such as text documents. Hence, there is a need
for methods that can serve dynamic Web resources such as
SWS. The developed method in this paper avoids redundancy
and respects consistency so as to assure high quality of the
resulting shared ontologies.
Ekaw ontology learning for cost effective large-scale semantic annotationShahab Mokarizadeh
This document discusses using ontology learning to semantically annotate a corpus of 15,000 web service interfaces. It proposes extracting terms from the interfaces at a fine-grained level and using pattern-based methods to discover taxonomic and non-taxonomic relations to automatically generate an ontology. The method achieved 62% accuracy for common concepts and 71% for common instances compared to a golden ontology.
Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery.
Bio:
Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Use of ontologies in natural language processingATHMAN HAJ-HAMOU
The document discusses the limitations of classical natural language processing approaches that rely solely on text-based search and statistics. It describes how ontologies can help address these limitations by representing knowledge at the semantic level rather than just words. The document outlines how ontologies can solve problems related to vagueness, high-level concepts, semantic relations, and time dimensions. It then summarizes two research papers that explore using ontologies to index and categorize documents as well as provide multiple views and dimensions to explore a document collection.
This document summarizes the first meeting of the Knowledge Representation seminar at Kings College London in June 2010. It discusses ontologies from three perspectives:
1) The theoretical perspective defines ontologies and discusses different definitions.
2) The pragmatic perspective explains what ontologies are used for.
3) The design perspective outlines how to build ontologies and discusses components like logic, ontology, and computation.
The document also covers topics like the differences between ontologies and data models or knowledge bases, degrees of "ontological depth", upper vs. domain ontologies, examples of top-level ontologies, and realist vs. conceptualist perspectives on ontologies.
Representation of ontology by Classified Interrelated object modelMihika Shah
1. The document discusses representing ontology using the Classified Interrelated Object Model (CIOM) data modeling technique. CIOM represents ontology components like classes, subclasses, attributes, and relationships between classes.
2. Key components of an ontology like classes, subclasses, attributes, and inter-class relationships are described and examples are given of how each would be represented using CIOM notation.
3. CIOM provides a general purpose methodology for representing ontologies using existing database technologies and overcomes limitations of specialized ontology languages and tools.
The document discusses the basics of ontologies, including their origin in philosophy, definitions, types, benefits and application areas. Some key points are:
- An ontology is a formal specification of a conceptualization used to help humans and programs share knowledge. It establishes a shared vocabulary for exchanging information.
- Ontologies describe domain knowledge and provide an agreed-upon understanding of a domain through concepts and relations. They help solve problems of ambiguity and enable knowledge sharing.
- Ontologies benefit applications like information retrieval, digital libraries, knowledge engineering and natural language processing by facilitating semantic search and integration of data.
A Semi-Automatic Ontology Extension Method for Semantic Web ServicesIDES Editor
this paper provides a novel semi-automatic ontology
extension method for Semantic Web Services (SWS). This is
significant since ontology extension methods those existing
in literature mostly deal with semantic description of static
Web resources such as text documents. Hence, there is a need
for methods that can serve dynamic Web resources such as
SWS. The developed method in this paper avoids redundancy
and respects consistency so as to assure high quality of the
resulting shared ontologies.
Ekaw ontology learning for cost effective large-scale semantic annotationShahab Mokarizadeh
This document discusses using ontology learning to semantically annotate a corpus of 15,000 web service interfaces. It proposes extracting terms from the interfaces at a fine-grained level and using pattern-based methods to discover taxonomic and non-taxonomic relations to automatically generate an ontology. The method achieved 62% accuracy for common concepts and 71% for common instances compared to a golden ontology.
Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery.
Bio:
Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Use of ontologies in natural language processingATHMAN HAJ-HAMOU
The document discusses the limitations of classical natural language processing approaches that rely solely on text-based search and statistics. It describes how ontologies can help address these limitations by representing knowledge at the semantic level rather than just words. The document outlines how ontologies can solve problems related to vagueness, high-level concepts, semantic relations, and time dimensions. It then summarizes two research papers that explore using ontologies to index and categorize documents as well as provide multiple views and dimensions to explore a document collection.
Using ontology for natural language processing can help mediate human-machine communication. Ontologies provide a formal representation of knowledge that can be used in natural language processing. The document discusses how general and specific ontologies can be combined and used with natural language processing techniques like machine translation and speech recognition. It also describes how ontologies can be extended over time using artificial neural networks to improve natural language understanding abilities.
Swoogle: Showcasing the Significance of Semantic SearchIDES Editor
The World Wide Web hosts vast repositories of
information. The retrieval of required information from the
Internet is a great challenge since computer applications
understand only the structure and layout of web pages and
they do not have access to their intended meaning. Semantic
web is an effort to enhance the Internet, so that computers
can process the information presented on WWW, interpret
and communicate with it, to help humans find required
essential knowledge. Application of Ontology is the
predominant approach helping the evolution of the Semantic
web. The aim of our work is to illustrate how Swoogle, a
semantic search engine, helps make computer and WWW
interoperable and more intelligent. In this paper, we discuss
issues related to traditional and semantic web searching. We
outline how an understanding of the semantics of the search
terms can be used to provide better results. The experimental
results establish that semantic search provides more focused
results than the traditional search.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
Tools for Ontology Building from Texts: Analysis and Improvement of the Resul...IOSR Journals
Text2Onto is a tool that learns ontologies from textual data by extracting ontology components like concepts, relations, instances, and hierarchies. It analyzes texts through linguistic preprocessing using Gate to tokenize, tag parts of speech, and identify noun and verb phrases. Algorithms then extract ontology components and store them probabilistically in a Preliminary Ontology Model independent of any representation language. The study aimed to understand Text2Onto's architecture, analyze errors in its extractions, and attempt improvements by using a meta-model of the text to better classify concepts under core concepts.
The document discusses developing ontologies, including:
1. What an ontology is and different types of ontologies such as taxonomies, thesauri, and reference models.
2. Representing ontologies using knowledge representation formalisms that have evolved from semantic networks to description logics.
3. The Semantic Web ontology language OWL, which extends RDFS and is divided into three species with different levels of expressivity.
This document provides an overview of ontology learning from unstructured text. It discusses how ontology became important for facilitating text understanding and automatic processing. It also describes different types of ontologies used in computer science, including upper ontologies describing very generic concepts, and domain ontologies describing specific subject domains. The document outlines various methods for learning ontologies from text and clarifies different understandings and uses of the term "ontology" in philosophy and computer science.
This document provides an overview of ontologies and the semantic web. It defines ontologies as formal specifications of conceptualizations that are shared between people and computers. Ontologies provide a common vocabulary and conceptual structure to facilitate understanding between humans and machines. They allow different systems and communities to work together by providing shared definitions of concepts and relationships. The development of ontologies and the semantic web aims to make web resources more computer-readable and enable machines to better understand and process online information.
Computational Explanation in Biologically Inspired Cognitive Architectures/Sy...Antonio Lieto
Computational models of cognition can have explanatory power when they are structurally valid models of the natural systems that inspired them. The document discusses different approaches to modeling knowledge in cognitive architectures and humans. It analyzes how ACT-R, CLARION, and LIDA represent concepts, and suggests that humans likely use heterogeneous representations including prototypes, exemplars, and other conceptual structures. Models should account for this heterogeneity to better explain human cognition.
Lieto - Book Presentation Cognitive Design for Artificial Minds (AGI Northwes...Antonio Lieto
The document discusses a book titled "Cognitive Design for Artificial Minds" by Antonio Lieto. It includes quotes from several professors praising the book for proposing a re-unification of artificial intelligence and cognitive science. The book explores connections between AI modeling techniques and cognitive science methods. It also provides an overview of cognitive architectures and argues that a biologically/cognitively inspired approach can help develop next generation AI systems beyond deep learning. The document discusses challenges in developing a standard model of cognition and the need for collaboration across the AI and cognitive science communities.
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecn...Antonio Lieto
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecnologia (IIT), I-Cog Initiative. https://www.facebook.com/icog.initiative/posts/129265685733532
Kenneth Lloyd introduces category theory as a potential language for scientific discourse in agent-based modeling and simulation (ABMS). Category theory defines mathematical structures and relationships between them. Lloyd argues that agents can be considered as structures within a category. He provides an example of applying category theory concepts like functors to represent functional objects and agents. Finally, Lloyd discusses how category theory may provide a formalism for describing emergent properties in multi-agent systems and validating hypotheses through simulation.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
The document provides an overview of ontology and its various aspects. It discusses the origin of the term ontology, which derives from Greek words meaning "being" and "science," so ontology is the study of being. It distinguishes between scientific and philosophical ontologies. Social ontology examines social entities. Perspectives on ontology include philosophy, library and information science, artificial intelligence, linguistics, and the semantic web. The goal of ontology is to encode knowledge to make it understandable to both people and machines. It provides motivations for developing ontologies such as enabling information integration and knowledge management. The document also discusses ontology languages, uniqueness of ontologies, purposes of ontologies, and provides references.
The document summarizes a seminar on ontology mapping presented by Samhati Soor. The seminar covered the need for ontology mapping due to the proliferation of ontologies, and the purpose of mapping ontologies to achieve interoperability and sharing knowledge. It defined ontologies and ontology mapping and discussed categories of mapping including between global and local ontologies, between local ontologies, and for merging ontologies. Tools for ontology mapping discussed included GLUE and SAM. Evaluation criteria and challenges of ontology mapping were also summarized along with conclusions and references.
The document discusses ontology from philosophical and computer science perspectives. In philosophy, ontology is the science of being and investigates categories of things that exist. In computer science, an ontology is an explicit specification of a conceptualization - the objects, relations, and other entities that are presumed to exist in some area of interest. It defines the types, properties, and interrelationships of the entities. The document contrasts ontologies with other concepts like conceptual schemas, knowledge bases, and classifications. It also discusses challenges in ontology engineering like balancing domain independence with application dependencies.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
The document discusses ontology matching, which is the process of finding relationships between entities in different ontologies. It describes various techniques for ontology matching including basic techniques that operate at the element-level or structure-level, as well as classifications of matching techniques based on the type of input used and level of interpretation. The document also provides examples of commonly used methods for ontology matching like string-based, language-based, and structure-based techniques.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses a study on the fatigue analysis of glass fiber reinforced composites. It describes how glass fiber reinforced epoxy laminate composite samples were manufactured using vacuum bagging technique. The samples were then cut and tested under varying cyclic loads on a fatigue testing machine. The results showed that the glass fiber reinforced composites could withstand fatigue loads efficiently compared to aluminum and the number of cycles before failure decreased as the applied load increased. The document concludes that future work could extend these tests under different loading conditions to improve stress variation understanding and enable application in aircraft structures.
Using ontology for natural language processing can help mediate human-machine communication. Ontologies provide a formal representation of knowledge that can be used in natural language processing. The document discusses how general and specific ontologies can be combined and used with natural language processing techniques like machine translation and speech recognition. It also describes how ontologies can be extended over time using artificial neural networks to improve natural language understanding abilities.
Swoogle: Showcasing the Significance of Semantic SearchIDES Editor
The World Wide Web hosts vast repositories of
information. The retrieval of required information from the
Internet is a great challenge since computer applications
understand only the structure and layout of web pages and
they do not have access to their intended meaning. Semantic
web is an effort to enhance the Internet, so that computers
can process the information presented on WWW, interpret
and communicate with it, to help humans find required
essential knowledge. Application of Ontology is the
predominant approach helping the evolution of the Semantic
web. The aim of our work is to illustrate how Swoogle, a
semantic search engine, helps make computer and WWW
interoperable and more intelligent. In this paper, we discuss
issues related to traditional and semantic web searching. We
outline how an understanding of the semantics of the search
terms can be used to provide better results. The experimental
results establish that semantic search provides more focused
results than the traditional search.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
Tools for Ontology Building from Texts: Analysis and Improvement of the Resul...IOSR Journals
Text2Onto is a tool that learns ontologies from textual data by extracting ontology components like concepts, relations, instances, and hierarchies. It analyzes texts through linguistic preprocessing using Gate to tokenize, tag parts of speech, and identify noun and verb phrases. Algorithms then extract ontology components and store them probabilistically in a Preliminary Ontology Model independent of any representation language. The study aimed to understand Text2Onto's architecture, analyze errors in its extractions, and attempt improvements by using a meta-model of the text to better classify concepts under core concepts.
The document discusses developing ontologies, including:
1. What an ontology is and different types of ontologies such as taxonomies, thesauri, and reference models.
2. Representing ontologies using knowledge representation formalisms that have evolved from semantic networks to description logics.
3. The Semantic Web ontology language OWL, which extends RDFS and is divided into three species with different levels of expressivity.
This document provides an overview of ontology learning from unstructured text. It discusses how ontology became important for facilitating text understanding and automatic processing. It also describes different types of ontologies used in computer science, including upper ontologies describing very generic concepts, and domain ontologies describing specific subject domains. The document outlines various methods for learning ontologies from text and clarifies different understandings and uses of the term "ontology" in philosophy and computer science.
This document provides an overview of ontologies and the semantic web. It defines ontologies as formal specifications of conceptualizations that are shared between people and computers. Ontologies provide a common vocabulary and conceptual structure to facilitate understanding between humans and machines. They allow different systems and communities to work together by providing shared definitions of concepts and relationships. The development of ontologies and the semantic web aims to make web resources more computer-readable and enable machines to better understand and process online information.
Computational Explanation in Biologically Inspired Cognitive Architectures/Sy...Antonio Lieto
Computational models of cognition can have explanatory power when they are structurally valid models of the natural systems that inspired them. The document discusses different approaches to modeling knowledge in cognitive architectures and humans. It analyzes how ACT-R, CLARION, and LIDA represent concepts, and suggests that humans likely use heterogeneous representations including prototypes, exemplars, and other conceptual structures. Models should account for this heterogeneity to better explain human cognition.
Lieto - Book Presentation Cognitive Design for Artificial Minds (AGI Northwes...Antonio Lieto
The document discusses a book titled "Cognitive Design for Artificial Minds" by Antonio Lieto. It includes quotes from several professors praising the book for proposing a re-unification of artificial intelligence and cognitive science. The book explores connections between AI modeling techniques and cognitive science methods. It also provides an overview of cognitive architectures and argues that a biologically/cognitively inspired approach can help develop next generation AI systems beyond deep learning. The document discusses challenges in developing a standard model of cognition and the need for collaboration across the AI and cognitive science communities.
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecn...Antonio Lieto
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecnologia (IIT), I-Cog Initiative. https://www.facebook.com/icog.initiative/posts/129265685733532
Kenneth Lloyd introduces category theory as a potential language for scientific discourse in agent-based modeling and simulation (ABMS). Category theory defines mathematical structures and relationships between them. Lloyd argues that agents can be considered as structures within a category. He provides an example of applying category theory concepts like functors to represent functional objects and agents. Finally, Lloyd discusses how category theory may provide a formalism for describing emergent properties in multi-agent systems and validating hypotheses through simulation.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
The document provides an overview of ontology and its various aspects. It discusses the origin of the term ontology, which derives from Greek words meaning "being" and "science," so ontology is the study of being. It distinguishes between scientific and philosophical ontologies. Social ontology examines social entities. Perspectives on ontology include philosophy, library and information science, artificial intelligence, linguistics, and the semantic web. The goal of ontology is to encode knowledge to make it understandable to both people and machines. It provides motivations for developing ontologies such as enabling information integration and knowledge management. The document also discusses ontology languages, uniqueness of ontologies, purposes of ontologies, and provides references.
The document summarizes a seminar on ontology mapping presented by Samhati Soor. The seminar covered the need for ontology mapping due to the proliferation of ontologies, and the purpose of mapping ontologies to achieve interoperability and sharing knowledge. It defined ontologies and ontology mapping and discussed categories of mapping including between global and local ontologies, between local ontologies, and for merging ontologies. Tools for ontology mapping discussed included GLUE and SAM. Evaluation criteria and challenges of ontology mapping were also summarized along with conclusions and references.
The document discusses ontology from philosophical and computer science perspectives. In philosophy, ontology is the science of being and investigates categories of things that exist. In computer science, an ontology is an explicit specification of a conceptualization - the objects, relations, and other entities that are presumed to exist in some area of interest. It defines the types, properties, and interrelationships of the entities. The document contrasts ontologies with other concepts like conceptual schemas, knowledge bases, and classifications. It also discusses challenges in ontology engineering like balancing domain independence with application dependencies.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
The document discusses ontology matching, which is the process of finding relationships between entities in different ontologies. It describes various techniques for ontology matching including basic techniques that operate at the element-level or structure-level, as well as classifications of matching techniques based on the type of input used and level of interpretation. The document also provides examples of commonly used methods for ontology matching like string-based, language-based, and structure-based techniques.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses a study on the fatigue analysis of glass fiber reinforced composites. It describes how glass fiber reinforced epoxy laminate composite samples were manufactured using vacuum bagging technique. The samples were then cut and tested under varying cyclic loads on a fatigue testing machine. The results showed that the glass fiber reinforced composites could withstand fatigue loads efficiently compared to aluminum and the number of cycles before failure decreased as the applied load increased. The document concludes that future work could extend these tests under different loading conditions to improve stress variation understanding and enable application in aircraft structures.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses the applications of robotics in medicine. It describes how robotic surgery can accomplish tasks with more precision and repeatability than human surgeons. Robots are being used for minimally invasive surgeries of the heart, brain, spine, and other areas. The document also discusses how surgical planning works using imaging and 3D modeling to plan robotic procedures. It explains the registration process that aligns the robotic system to the patient. Finally, it briefly discusses developing technologies like nanorobotics and potential future applications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
1) O documento apresenta exemplos de cálculos de descontos simples e compostos de títulos e valores nominais. 2) São mostrados passo a passo os cálculos para determinar os valores descontados e os descontos de diversos títulos à taxas diferentes. 3) As questões abordam cálculos de descontos racionais e comerciais simples e compostos.
El documento presenta los resultados de una investigación sobre el uso de la metodología de rincones y las herramientas TIC en jardines de infantes. Los hallazgos mostraron que la mayoría de maestras tienen habilidades básicas en TIC y debilidades en la implementación de rincones. Por lo tanto, se planificaron talleres de capacitación en uso de TIC, metodología de rincones y proyectos, así como mayor involucramiento de padres y directores para mejorar la gestión del proyecto.
Este documento fornece instruções para configurar o Linux e seus programas para permitir o uso da língua portuguesa, discutindo a configuração do teclado e fontes nos modos de console e X Window. Ele também recomenda leituras adicionais sobre o assunto e fornece detalhes sobre como contribuir com o documento.
El documento describe dos períodos de la historia de Egipto: el Primer Período Intermedio (2150-2080 a.C.) y el Imperio Medio (2080-1759 a.C.), con una breve mención de la pintura.
El documento describe las 5 fases de una carrera militar en el ejército, que incluyen la primera, segunda y tercera fase, así como los requisitos para unirse a la fuerza aérea.
El documento resume el estado actual del conocimiento sobre implantes neurales y neuroprótesis. Menciona que existen pocos conocimientos sobre los circuitos cerebrales involucrados en la cognición y que esto se refleja en las historias de ciencia ficción sobre implantes cerebrales. También destaca que el implante coclear ha sido la primera neuroprótesis exitosa en humanos para estimular el nervio auditivo. Finalmente, predice que en cinco años habrá interfaces cerebro-máquina que permitan a personas paralizadas controlar objetos con el pensamiento.
O documento discute como ensinar sobre florestas para crianças. Ele inclui atividades como contar histórias, desenhar, discutir ideias prévias, e usar um aplicativo interativo para explorar os diferentes elementos e produtos encontrados nas florestas.
O documento apresenta poemas de Maria Álvaro que exploram temas como migração, saudade, desejo, reflexão e netas. Os poemas usam linguagem figurativa e metáforas para expressar sentimentos complexos.
Este documento discute Qt 5 y KDE Frameworks. Explica que Qt-project.org ha estado activo desde 2011 y tiene cualidades como justicia, transparencia e inclusividad. Qt 5 tiene objetivos como funcionar en múltiples dispositivos y tener UIs bonitas, y novedades como Qt Quick 2 y soporte para C++11. KDE Frameworks se basa en Qt 5 y divide kdelibs en frameworks individuales para mejorar la integración en el sistema. El documento también proporciona actualizaciones sobre el estado de KDE Frameworks.
El paciente se sienta frente a su terapeuta y le confiesa que sus padres le mintieron durante toda su vida. Procede a enumerar las mentiras y engaños que sus padres le dijeron y que afectaron profundamente su vida, como que la policía siempre ayuda y que los políticos representan fielmente al pueblo. Al terminar de revelar estas mentiras, tanto él como su terapeuta rompen en llanto al darse cuenta de lo engañados que crecieron.
O documento propõe um projeto habitacional para o terreno da Portobello na Barra da Lagoa, com três tipos de blocos para abrigar classes média, baixa e moradores periódicos. Inclui comércio nos blocos para gerar renda aos moradores e conectá-los ao bairro. Áreas verdes públicas são projetadas para convívio e lazer.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
The document discusses knowledge organization systems (KOS) and how the Simple Knowledge Organization System (SKOS) bridges KOS and the Semantic Web. It provides examples of KOS like taxonomies and thesauruses and explains how they are used differently than ontologies. SKOS is defined as an RDF vocabulary for representing KOS online in a machine-readable way and became a W3C standard in 2009.
SWSN UNIT-3.pptx we can information about swsn professionalgowthamnaidu0986
Ontology engineering involves constructing ontologies through various methods. It begins with defining the scope and evaluating existing ontologies for reuse. Terms are enumerated and organized in a taxonomy with defined properties, facets, and instances. The ontology is checked for anomalies and refined iteratively. Popular tools for ontology development include Protege and WebOnto. Methods like Meth ontology and On-To-Knowledge methodology provide processes for building ontologies from scratch or reusing existing ones. Ontology sharing requires mapping between ontologies to allow interoperability, and libraries exist for storing and accessing ontologies.
This document surveys ontology visualization methods. It begins by defining ontologies as sets of concepts and relationships in a domain that have proven useful for digital libraries, the semantic web, and personalized information management. However, effectively visualizing ontologies is challenging due to the complex relationships and attributes involved. The document aims to categorize existing ontology visualization techniques and their characteristics in order to help with method selection and further research. It provides context on related work reviewing data visualization techniques before analyzing ontology visualization methods in detail.
In this paper we present the SMalL Ontology for malicious software classification, SMalL Java Application for antivirus systems comparison and the SMalL knowledge based file format for malware related attacks. We believe that our ontology is able to aid the development of malware prevention software by offering a common knowledge base and a clear classification of the existing malicious software. The application is a prototype regarding how this ontology might be used in conjunction with known antivirus capabilities to offer a comprehensive comparison.
Philosophy of science summary presentation engelbyDavid Engelby
Philosophy of science can be summarized in 3 domains:
1) Epistemology - the study of knowledge and justified belief, including what we know and how we know it.
2) Ontology - what exists and how we conceptualize and represent domains of knowledge.
3) Methodology - the framework for combining theories and approaches, including specific research methods.
Key concepts in philosophy of science include knowledge, truth, explanation, concepts, constructs, variables, and research methodology.
This document summarizes key concepts from a paper on the CIDOC Conceptual Reference Model (CRM), an ontology for semantic interoperability of cultural heritage data. It discusses the CRM's property-centric approach and methodology, which aims for read-only integration of heterogeneous cultural data sources. The CRM is designed to reconstruct possible past worlds from loosely correlated historical records in a way that allows for monotonic extension of the ontology over time without revising existing definitions.
Microposts Ontology Construction Via Concept Extraction dannyijwest
The document discusses constructing an ontology for microposts from social media platforms. It proposes extracting concepts from microposts by analyzing keywords, named entities, and matching them to ontological concepts. The method involves parsing microposts syntactically and semantically to identify subject-object relationships represented as case roles or vibhaktis. A vibhakti table is generated to represent these relationships for input texts. The goal is to develop a mechanism for reducing manual effort in building ontologies by extracting structured knowledge from microposts.
The document discusses faceted approaches to knowledge organization. It introduces faceted classification and compares it to enumerative classification. Faceted classification breaks down subjects into basic subjects and isolate ideas, allowing for flexibility and accommodation of new concepts. It then discusses how faceted approaches can be applied to organize information on the web, including through faceted metadata schemes and faceted lightweight ontologies. The DERA framework is introduced as a knowledge representation approach that models entities using their entity classes, relations, and attributes in a faceted manner.
Knowledge Organisation Systems in Digital Libraries: A Comparative StudyBhojaraju Gunjal
The document presents a study that compares the different Knowledge Organization Systems (KOS) used in major digital libraries. It finds that while traditional libraries used standardized systems like classification schemes, digital libraries employ various KOS tools including thesauri, ontologies, and subject headings. The study analyzes the specific KOS used in different digital libraries and summarizes the current state of KOS in these libraries.
This document discusses the state-of-the-art of Internet of Things (IoT) ontologies. It begins by defining ontology and describing important design criteria for ontologies including clarity, coherence, extendibility, and minimal encoding bias. It then discusses the challenges of IoT, including large scale networks, deep heterogeneity, and unknown topology. Several existing IoT ontologies are described, including SWAMO, MMI Device Ontology, and SSN. The document concludes that while no single global IoT ontology currently exists, ontologies are needed to address the semantic interoperability challenges of heterogeneous IoT devices and domains.
Microposts Ontology Construction Via Concept Extraction dannyijwest
The social networking website Facebook offers to its users a feature called “status updates” (or just “status”), which allows users to create Microposts directed to all their contacts, or a subset thereof. Readers can respond to Microposts, or in addition to that also click a “Like” button to show their appreciation for a certain Micropost. Adding semantic meaning in the sense of unambiguous intended ideas to such Microposts. We can make a start towards semantic web by adding semantic annotation to web resources. Ontology are used to specify meaning of annotations. Ontology provide a vocabulary for representing and communicating knowledge about some topic and a set of semantic relationships that hold among the terms in that vocabulary. For increasing the efficiency of ontology based application there is a need to develop a mechanism that reduces the manual work in developing ontology. In this paper, we proposed Microposts’ ontology construction. In this paper we present a method that extracts meaningful knowledge from microposts shared in social platforms. This process involves different steps for the analysis of such microposts (extraction of keywords, named entities and their matching to ontological concepts).
A technology architecture for managing explicit knowledge over the entire lif...William Hall
This document discusses managing explicit knowledge over the entire lifecycle of large projects. It covers theories of knowledge management, including different paradigms of knowledge and how technology has revolutionized knowledge transmission. As an example, it examines issues around managing knowledge for an ANZAC ship project. It suggests content management needs to evolve to understand paradigm shifts in how knowledge itself is defined and managed.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Heraclitus II is a framework for ontology management and evolution in information management systems. It uses a temporal ontology model based on TAU that supports modeling facts with transaction and valid times. The framework represents ontologies as multiple layers from generic to domain-specific. Ontology evolution in Heraclitus II captures changes over time, preserves consistency, and propagates changes both within and across ontologies. The framework aims to provide transparency in the construction and evolution of ontologies used in information management.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
A NATURAL LOGIC FOR ARTIFICIAL INTELLIGENCE, AND ITS RISKS AND BENEFITSijwscjournal
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner
enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural
logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven”
natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but
indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently”
processed by computers, using the semantic representations of the phrases of the fragment.
A Natural Logic for Artificial Intelligence, and its Risks and Benefits gerogepatton
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven” natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently” processed by computers, using the semantic representations of the phrases of the fragment.
A NATURAL LOGIC FOR ARTIFICIAL INTELLIGENCE, AND ITS RISKS AND BENEFITSijasuc
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner
enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural
logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven”
natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but
indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently”
processed by computers, using the semantic representations of the phrases of the fragment.
The document discusses the use of ontologies in ubiquitous computing. It defines what an ontology is and describes ontology languages. It then presents a taxonomy for classifying ontologies used in ubiquitous computing into two main categories: ontologies of the ubiquitous computing domain and ontologies as software artifacts. Examples are given for each category including generic and specific domain ontologies as well as ontology-driven, ontology-aware, and ontology use at development time applications. The conclusion states that many works propose using ontologies in ubiquitous computing and the presented taxonomy can help organize these works.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
1. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.611-616
An Ontology for Describing Web Services Through WSDL and
RDF
J. Nagaraju*, K. Ravi Kumar**
*(Department of Computer Science,Anurag Engineering College,
Kodad,Andhra Pradesh,India)
**((Department of Computer Science,Anurag Engineering College
,Kodad,Andhra Pradesh,India)
ABSTRACT
Now a days web has become an naturally fall.[5]During the second half of the 20th
important resource for knowledge.To provide century, philosophers extensively debated the
many services to the users to acquire data easily possible methods or approaches to building
and efficiently web services are being described ontologies without actually building any very
using ontologies which provide a set of rules to be elaborate ontologies themselves.
used easily and understood preferably.The
mostly used languages used for describing web By contrast, computer scientists were building
services is WSDL.In this paper we have some large and robust ontologies, such as WordNet
described about WSDL and RDF using graphs to and Cyc, with comparatively little debate over how
describe ontologies. they were built.Since the mid-1970s, researchers in
Keywords-ontology, WSDL ,RDF, web services the field of artificial intelligence (AI) have
recognized that capturing knowledge is the key to
building large and powerful AI systems. AI
I. Introduction researchers argued that they could create new
In computer science and information ontologies as computational models that enable
science, an ontology formally represents knowledge certain kinds of automated reasoning. In the 1980s,
as a set of concepts within a domain, and the the AI community began to use the term ontology to
relationships between pairs of concepts. It can be refer to both a theory of a modeled world and a
used to model a domain and support reasoning about component of knowledge systems.
entities .In theory, an ontology is a "formal, explicit
specification of a shared conceptualisation". [1] An Some researchers, drawing inspiration from
ontology renders shared vocabulary and taxonomy philosophical ontologies, viewed computational
which models a domain with the definition of ontology as a kind of applied philosophy. [6]In the
objects and/or concepts and their properties and early 1990s, the widely cited Web page and paper
relations.[2]Ontologies are the structural frameworks "Toward Principles for the Design of Ontologies
for organizing information and are used in artificial Used for Knowledge Sharing" by Tom Gruber[7] is
intelligence, the Semantic Web, systems credited with a deliberate definition of ontology as a
engineering, software engineering, biomedical technical term in computer science. Gruber
informatics, library science, enterprise introduced the term to mean a specification of a
bookmarking, and information architecture as a conceptualization:"An ontology is a description
form of knowledge representation about the world (like a formal specification of a program) of the
or some part of it. concepts and relationships that can formally exist
for an agent or a community of agents.
The creation of domain ontologies is also
fundamental to the definition and use of an This definition is consistent with the usage of
enterprise architecture framework.Historically, ontology as set of concept definitions, but more
ontologies arise out of the branch of philosophy general. And it is a different sense of the word than
known as metaphysics, which deals with the nature its use in philosophy.According to Gruber
of reality – of what exists. This fundamental branch (1993):"Ontologies are often equated with
is concerned with analyzing various types or modes taxonomic hierarchies of classes, class definitions,
of existence, often with special attention to the and the subsumption relation, but ontologies need
relations between particulars and universals, not be limited to these forms. Ontologies are also
between intrinsic and extrinsic properties, and not limited to conservative definitions — that is,
between essence and existence. The traditional goal definitions in the traditional logic sense that only
of ontological inquiry in particular is to divide the introduce terminology and do not add any
world "at its joints" to discover those fundamental knowledge about the world.[9] To specify a
categories or kinds into which the world’s objects
611 | P a g e
2. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.611-616
conceptualization, one needs to state axioms that do Thus,we might speak of an ontology for ―liquids‖ or
constrain the possible interpretations for the defined for ―parts and wholes.‖ Here, the singular term
terms. stands for the entire set of concepts and terms
needed to speak about phenomena involving liquids
II. Ontology As Vocabulary and parts and wholes. When different theorists make
In philosophy, ontology is the study of the different proposals for an ontology or when we
kinds of things that exist. It is often said that speak about ontology proposals for different
ontologies ―carve the world at its joints.‖ In AI, the domains of knowledge,we would then use the plural
term ontology has largely come to mean one of two term ontologies to refer to them collectively.
related things. First of all, ontology is a In AI and information-systems literature, however,
representation vocabulary, often specialized to some there seems to be inconsistency: sometimes we see
domain or subject matter. More precisely, it is not references to ―ontology of domain‖ and other times
the vocabulary as such that qualifies as an ontology, to ―ontologies of domain,‖ both referring to the set
but the conceptualizations that the terms in the of conceptualizations for the domain. The former is
vocabulary are intended to capture. Thus, translating more consistent with the original (and current) usage
the terms in an ontology from one language to in philosophy.
another, for example from English to French, does
not change the ontology conceptually. In III. Ontology As Content Theory
engineering design, you might discuss the ontology The current interest in ontologies is the
of an electronic-devices domain, which might latest version of AI’s alternation of focus between
include vocabulary that describes conceptual content theories and mechanism theories.
elements—transistors, operational amplifiers, and Sometimes, the AI community gets excited by some
voltages—and the relations between these mechanism such as rule systems, frame languages,
elements—operational amplifiers are a type-of neural nets, fuzzy logic, constraint propagation, or
electronic device, and transistors are a component-of unification. The mechanisms are proposed as the
operational amplifiers. secret of making intelligent machines. At other
Identifying such vocabulary—and the underlying times,we realize that, however wonderful the
conceptualizations—generallyrequires careful mechanism, it cannot do much without a good
analysis of the kinds of objects and relations that can content theory of the domain on which it is to work.
exist in the domain. In its second sense, the term Moreover, we often recognize that once a good
ontology is sometimes used to refer to a body of content theory is available, many different
knowledge describing some domain, typically a mechanisms might be used equally well to
commonsense knowledge domain, using a implement effective systems, all using essentially
representation vocabulary. the same content.
For example, CYC1 often refers to its knowledge
representation of some area of knowledge as its AI researchers have made several attempts to
ontology. In other words, the representation characterize the essence of what it means to have a
vocabulary provides a set of terms with which to content theory. McCarthy and Hayes’theory
describe the facts in some domain, while the body of (epistemic versus heuristic distinction), 3 Marr’s
knowledge using that vocabulary is a collection of three-level theory (information processing, strategy
facts about a domain. level, algorithmsand data structures level, and
physical mechanisms level),4 and Newell’s theory
However, this distinction is not as clear as it might (Knowledge Level versus Symbol Level)5 all
first appear. In the electronic-device example, that grapple in their own ways with characterizing
transistor is a component-of operational amplifier or content. Ontologies are quintessentially content
that the latter is a type-of electronic device is just as theories, because their main contribution is to
much a fact about its domain as a CYC fact about identify specific classes of objects and relations that
some aspect of space, time, or numbers. The exist in some domain. Of course, content theories
distinction is that the former emphasizes the use of need a representation language. Thus far, predicate
ontology as a set of terms for representing specific calculuslike formalisms, augmented with type-of
facts in an instance of the domain, while the latter relations (that can be used to induce class
emphasizes the view of ontology as a general set of hierarchies), have been most often used to describe
facts to be shared. There continues to be the ontologies themselves.
inconsistencies in the usage of the term ontology. At
times, theorists use the singular term to refer to a 3.1 Use Of Ontology
specific set of terms meant to describe the entity and Ontological analysis clarifies the structure
relation-types in some domain. of knowledge. Given a domain, its ontology forms
the heart of any system of knowledge representation
for that domain. Without ontologies, or the
612 | P a g e
3. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.611-616
conceptualizations that underlie knowledge, there vocabulary and syntax to build catalogs that
cannot be a vocabulary for representing knowledge. describe their products. Then the manufacturers
Thus, the first step in devising an effective could share the catalogs and use them in automated
knowledgerepresentation system, and vocabulary, is design systems. This kind of sharing vastly
to perform an effective ontological analysis of increases the potential for knowledge reuse.
the field, or domain. Weak analyses lead to
incoherent knowledge bases. An example of why
performing good analysis is necessary comes from
the field of databases.6 Consider a domain having
several classes of people (for example, students,
professors, employees, females, and males).
This study first examined the way this database
would be commonly organized: students,
employees, professors, males, and female would be
represented as types-of the class humans. However,
some of the problems that exist with this ontology
In AI, knowledge in computer systems is thought of
are that students can also be employees at times and
as something that is explicitly represented and
can also stop being students. Further analysis
operated on by inference processes. However, that is
showed that the terms students and employee do not
an overly narrow view. All information systems
describe categories of humans, but are roles that
traffic in knowledge. Any software that does
humans can play, while terms such as females and
anything useful cannot be written without a
males more appropriately represent subcategories of
commitment to a model of the relevant world—to
humans. Therefore, clarifying the terminology
entities, properties, and relations in that world. Data
enables the ontology to work for coherent and
structures and procedures implicitly or explicitly
cohesive reasoning purposes. Second, ontologies make commitments to a domain ontology. It is
enable knowledge sharing. Suppose we perform an
common to ask whether a payroll system―knows‖
analysis and arrive at a satisfactory set of
about the new tax law, or whether a database system
conceptualizations, and their representative terms,
―knows‖ about employee salaries. Information-
for some area of knowledge—for example, the
retrieval systems, digital libraries, integration of
electronic-devices domain. The resulting ontology
heterogeneous information sources, and Internet
would likely include domain-specific terms such as
search engines need domain ontologies to organize
transistors and diodes; general terms such as
information and direct the search processes. For
functions, causal processes, and modes; and terms
example, a search engine has categories and
that describe behavior such as voltage.
subcategories that help organize the search.
The ontology captures the intrinsic conceptual
The search-engine community commonly refers to
structure of the domain. In order to build a
these categories and subcategories as ontologies.
knowledge representation language based on the
Object-oriented design of software systems similarly
analysis, we need to associate terms with the
depends on an appropriate domain ontology.
concepts and relations in the ontology and devise a
Objects, their attributes, and their procedures more
syntax for encoding knowledge in terms of the
or less mirror aspects of the domain that are relevant
concepts and relations. We can share this knowledge
to the application. Object systems representing a
representation language with others who have
useful analysis of a domain can often be reused for a
similar needs for knowledge representation in that
different application program.Object systems and
domain, thereby eliminating the need for replicating
ontologies emphasize different aspects, but we
the knowledge-analysis process. Shared ontologies
anticipate that over time convergence between these
can thus form the basis for domain-specific
technologies will increase. As information systems
knowledge-representation languages. model large knowledge domains, domain ontologies
will become as important in general software
In contrast to the previous generation of
systems as in many areas of AI.
knowledge-representation languages (such as KL-
One), these languages are content-rich; they have a
In AI,while knowledge representation pervades the
large number of terms that embody a complex
entire field, two application areas in particular have
content theory of the domain. Shared ontologies let
depended on a rich body of
us build specific knowledge bases that describe
knowledge. One of them is natural-language
specific situations. For example, different
understanding. Ontologies are useful in NLU in two
electronicdevices manufacturers can use a common
ways. First, domain knowledge often plays a crucial
role in disambiguation.
613 | P a g e
4. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.611-616
<wos:texbook dc:Creator wos:knuth>
A welldesigned domain ontology provides the basis <wos:texbook dc:Title "The TEXbook">
for domain knowledge representation. In addition, <wos:knuth foaf:name "Donald Knuth">
ontology of a domain helps identify the semantic form an RDF Graph of three statements2. The
categories that are involved in understanding TEXbook by Knuth is represented by the URI
discourse in that domain. For this use, the ontology wos:texbook (wos is the namespace prefix of an—
plays the role of a concept imaginary—‖Web of Scientists‖ vocabulary, which
dictionary. shall be presented later in this chapter) and
described in two statements. The object of the first
IV. RDF statement, wos:knuth, is an URI representing the
The Resource Description Framework is an
extensible infrastructure to express, exchange and 4.1 The Resource Description Framework
re-use structured metadata [Mil98]: ―Everything is
URI‖ Information resources are commonly The meaning of this RDF Graph is ―an information
identified by Uniform Resource Identifiers (URIs). resource, identified by wos:texbook, has the title
By generalizing the concept of ―resource‖, whatever "The TEXbook" and was created by something
is identifiable by an URI can be described in RDF. which is identified by wos:knuth and whose name is
In this way, URIs can be assigned to anything, even "Donald Knuth". Anonymous Resources There are
physical objects, living beings,abstract concepts, etc. situations in which we wish to describe information
It is important to note that the identifiability does using more complex structures of data than using a
not imply retrievability of the resource. The literal string or an URI pointer. For this,
principal advantages of this approach are that URIs ―anonymous‖ resources are used: the object of
are a globally unambiguous way to reference a statement can be an anonymous resource—or a
resources, and that no centralized authority is blank node—which itself is the subject of other
necessary to provide them. A common way to statements. Such a resource is represented by a
abbreviate it is the XML qualified name (or blank node identifier, which is usually denoted as :n,
QName) syntax of the form prefix:suffix. For with n being an integer. For example, a more
example, an URI such as sophisticated version of the above example about
http://www.w3.org/TR/rdf-primer/ would be written Knuth’s authorship of the TEXbook would be
as w3:rdf-primer/ if it has been agreed that w3 <wos:texbook dc:Creator :1>
stands for http://www.w3.org/TR/. RDF Statements <wos:texbook dc:Title "The TEXbook">
The atomic structure for RDF specifications is the < :1 foaf:name "Donald Knuth">
statement, which is a <subject predicate object>- < :1 rdf:type xy:Person>
triple. The information re- 1 For a more thorough < :1 wos:described wos:knuth>
introduction see the ―RDF Primer‖ [MM04] or other which states more clearly that the author of the
references given in subsection A.1.3 38 3. TEXbook is a human, which has a personal name
Preliminaries source being described is the subject and is further described in another resource
of the statement and is denoted by an URI. The (wos:knuth).
predicate of a statement is an URI reference
representing a property, whose property value It is important to note that the blank node identifiers
appears as the statement object. The property value carry no meaning; they are used merely for the
can be a resource as well as a literal value. A literal purpose of serialization (e.g., file storage). RDF
is a string (e.g., a personal name) of a certain Concepts We can now state more formally the
datatype and may only occur as the object of a triples which are syntactically correct: let ―uris‖ be
statement. RDF triples can be visualized as a the set of URIs, ―blanks‖ the set of blank node
directed labeled graph, __ __ __subject__ predicate identifiers, and ―lits‖ the set of possible literal
/__ ____object__in which subjects and objects are values of whatever datatype (we consider all these
represented as nodes, and predicates as arcs. sets as infinite). Then (s, p, o) 2 (uris[blanks) ×
In [KC04] a drawing convention is given—which (uris) × (uris[blanks [lits) is an RDF statement.
will be neglected in this document. According to Observe that there is no restriction to what URIs
this convention nodes representing literals are drawn may appear as statement property. We say that x is a
as rectangles and nodes representing URIs as ovals. resource if x 2 uris[blanks, and everything
In the drawings of this study, however, we need not occurringin an RDF statement is a value (x 2
to make these distinctions as we equally treat them uris[blanks [lits). In this document, most of the time
as nodes. For an example of a graph drawn it will be referred to values because the type—URI,
following that convention. blank, literal—is not of interest. To recall, a RDF
Graph T is a set of RDF statements (T abbreviates
RDF Graph A set of RDF statements is an RDF triples). A subgraph of T is a subset of T. A ground
Graph. For example, RDF Graph is an RDF Graph without blank nodes
614 | P a g e
5. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.611-616
With univ(T) we denote the set of all values considerable impact: Numerous publications,
occurring in all triples of T and call it the universe including tutorials, exist which claim that RDF ―is‖
of T; and vocab(T), the vocabulary of T, is the set of a directed labeled graph. The immediate result is
all values of the universe that are not blank nodes. the—artificial—distinction between resources and
The size of T is the number of statements it contains properties which many people make.
and is denoted by |T|. With subj(T) (respectively
pred(T), obj(T)) we designate all values which occur This prevents users from recognizing the actual
as subject simplicity of the RDF model. The results of the
(respectively predicate, object) of T. understanding of RDF bounded by the directed
Let V be a set of URIs and literal values. We define labeled graph model becomes especially evident in
RDFG(V) := { T : T is RDF Graph and vocab(T) _ the limitations of current RDF query languages as
V } i.e. the set of all RDF Graphs with a vocabulary studied in [AGH04].
included in V .Let M be a map from a set of blank
nodes to some set of literals, blanknodes and URI 4.3 Graphs as a Concept of Human Understanding
references; then any RDF Graph T0 obtained from
the RDF Graph T by replacing some or all of the Graphs are a successful method to visualize and
blank nodes N in T by M(N) is an instance of T. understand complex data. RDF, as a language
Consider an RDF Graph T1, and a bijective map M : developed to annotate and describe information
B1 ! B2 which replaces blank node identifiers of T1 resources and their relations among each other,
with other blank node identifiers. Then T2 = M(T1) allows the expression of potentially highly
is an instance of T1, and T1 is an instance of T2 (by interconnected collections of metadata assertions.
the inverse of M which is trivially defined). Two For the visualization of RDF data directed labeled
such RDF Graphs are considered as equivalent. graphs may be employed successfully for not too
Equivalent RDF Graphs are treated as identical RDF complex RDF Graphs. Also, to explain the RDF
Graphs, which is in conformance with the notion of model it is natural to use graphs. While the
blank nodes as ―anonymous resources‖ Reasons for examples provided, e.g., in the RDF Primer [MM04]
Graph Representation of RDF Graphs are are simple and therefore above-mentioned
mathematical objects which enjoy wide-spread limitations of directed labeled graphs are not as
usage for many tasks, which include the relevant, care should be taken that the (abstract)
visualization and analysis of data for humans, graph nature of RDF, the well-defined concept of
mathematical reasoning, and the implementation as RDF Graph and the representation of RDF as
a data structure for developing software. These tasks directed labeled graph are not confused
are relevant in the context of RDF data as well, as
this section shall present. V. Conclusion
Here by we can conclude that RDF and
4.2 Motivation WSDL help us to describe ontologies. Moreover
RDF using graphs creates a better understanding of
4.2.1 Fixing the Specification ontology to humans and provides good visualization
of web services
The first specification of RDF in the status of a
WWW Consortium Recommendationappeared in References
1999 [LS99]. Since then, it has taken five years to [1] Adobe Systems Incorporated. PDF
revise the original specification and to replace it by Reference, Version World Wide Web,
a suite of six documents which gained http://partners.adobe.com/asn/tech/pdf/specifi
recommendation status just recently, in February cations.jsp, 2003. 2
2004 [MM04, KC04, Hay04, GB04, Bec04, BG04]. [2] [Ado04] Adobe Developer Technologies.
The success of RDF appears to take place at a rather XMP - Extensible Metadata Platform. World
modest pace, and one is tempted to conclude that the Wide
arduously advancing process of specification is one Web,http://www.adobe.com/products/xmp/pd
reason for this. The fact that the fs/xmpspec.pdf, 2004. 2
2004WWWConsortium Recommendation still [3]
contains ambiguities as described above gives ttp://www.openrdf.org/doc/users/userguide.
motivation to supply a constructive critique and a html, 2004. 10.2.3,
proposition for refinement, with the hope to [4] Renzo Angles, Claudio Gutierrez, and
contribute to future revisions of the specification. Jonathan Hayes. RDF Query Languages Need
The issue—an incomplete definition of a graph Support for Graph Properties. Technical
representation, and a representation with certain Report TR/DCC-2004-3, Universidad de
limitations—might appear trivial. However, it has Chile,
615 | P a g e
6. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.611-616
http://www.dcc.uchile.cl/_cgutierr/ftp/graphp
roperties.pdf, 2004. 4.3.1, 4.3.5, 10.3.1, 3
[5] Rakesh Agrawal. Alpha: An Extension of
Relational Algebra to Express a Class of
Recursive Queries. IEEE Trans. Softw.
Eng.,14(7):879–885, 1988. 9.3.3
[6] Joshua Allen. Making a Semantic Web.
World Wide Web,
www.netcrucible.com/semantic.html, 2001. 2
[7] Boanerges Aleman-Meza, Chris Halaschek,
Ismailcem Budak Arpinar, and Amit P.
Sheth. Context-Aware Semantic Association
Ranking. In I. F. Cruz, V. Kashyap, S.
Decker, and R. Eckstein, editors, Proceedings
of SWDB’03, 2003.
[8] D.B. Lenat and R.V. Guha, Building Large
Knowledge-Based Systems: Representation
and Inference in the CYC Project, Addison-
Wesley, Reading, Mass., 1990.
[9] B. Chandrasekaran, ―AI, Knowledge, and the
Quest for Smart Systems,‖ IEEE Expert,Vol.
9, No. 6, Dec. 1994, pp. 2–6
.[10] J. McCarthy and P.J. Hayes, ―Some
Philosophical Problems from the Standpoint
of Artificial Intelligence,‖ Machine
IntelligenceVol. 4, B. Meltzer and D. Michie,
eds., Edinburgh University Press, Edinburgh,
1969, pp.463–502.
616 | P a g e