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.
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.
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
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.
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
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.
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.
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.
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
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.
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
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.
Knowledge lost in information, meanings lost in semantics?Sophie Visser
Knowledge and meaning are complex concepts that are impacted by information systems. As knowledge is codified into information and formalized for computer systems, some meanings may be lost. Three key issues are: (1) meanings are restricted by what can be expressed in language and formalized for machines, (2) formalization requirements of semantics limit the scope of represented meanings, and (3) predefined concepts, definitions and ontologies impose standardized meanings rather than supporting individual interpretations. Representing complex domains like cultural landscapes in information systems thus risks oversimplifying the knowledge and reducing the diversity of meanings.
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.
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.
Ontology is a formal explicit specification of a conceptualization that provides a shared understanding of a domain. An ontology for software engineering can help facilitate communication between distributed development teams by providing a common vocabulary and conceptualization of key software engineering concepts and their relationships. Such an ontology can be modeled using notations like UML class diagrams and activity diagrams to represent important software engineering concepts like classes, activities, and relationships. The software engineering ontology then allows for improved knowledge sharing and communication framework among distributed development teams.
ARTSEDU 2012
Educational Robotics between narration and simulation
Alessandri Giuseppe , Paciaroni Martina
Faculty of Education Sciences - University of Macerata, (MC, Italy)
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.
Semi-automated metadata extraction in the long-termPERICLES_FP7
This presentation was delivered by Emma Tonkin (King's College London) at the Digital Preservation Coalition (DPC) event entitled 'Practical Preservation and People: a briefing about metadata', which took place at the Public Records Office of Northern Ireland, Belfast on 3 December 2015.
Ontology learning techniques and applications computer science thesis writing...Tutors India
At Tutors India, we offer Computer science and Information Technology Research Guidance services – We deliver exceptional work where your dissertation will deserve publication without significant reworking or alternation.
For #Enquiry
https://www.tutorsindia.com
info@tutorsindia.com
(Whatsapp): +91-8754446690
(UK): +44-1143520021
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.
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.
The tutorial has been presented at CAISE 2010. The tutorial discusses the state-of-the-art on research addresseing the quality of data at the conceptual level (conceptual schemas) and of Ontologies
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.
Cognitive computing aims to address complex problems characterized by ambiguity and uncertainty by learning from and interacting with people. The document discusses several topics related to cognitive computing and tacit knowledge, including:
- The history and traditions of cognitive science from the 1950s onward.
- How early experiments in the 1990s explored using computer-mediated telepresence to enable real-time collaborative creative work across distances, finding it can achieve similar results to physical presence.
- Questions around how cognitive computing systems today can help individuals and groups create value and preserve identity while working together.
- Whether true real-time collaborative creative work engaging tacit knowledge is possible digitally, or if physical presence is still needed.
Information among networks and systems of knowledgeJosé Nafría
This document discusses different types of networks and models of information exchange. It begins by defining abstract networks as sets of nodes and links, and discusses how networks can represent both potentialities and actualities. It then covers semantic networks and how they can map both passive concepts and active communicative agents. Models of communication and information exchange are presented, including Shannon's technical model and more inferential semantic models. The role of context and convention in communication is discussed. Finally, the document addresses semantic networks in knowledge representation and the dynamics of scientific knowledge development.
This paper considers how hermeneutics and other related theories may bring new insights into KO. They provide a most realistic representation of the complexity of knowledge and meaning according to which new forms of KOSs could be designed. Computational and conceptual aspects of these issues are discussed taking into account a number of case studies.
The document summarizes Markus Strohmaier's presentation on extracting semantics from crowds at the International Summer School on Semantic Computing in 2011. The presentation discussed how semantics can be extracted from online crowd behaviors like social labeling with hashtags on Twitter, social tagging on sites like Delicious, and social navigation. Methods discussed include analyzing tag relatedness, generality, and hierarchies to emerge semantic structures from folksonomies. The goal is to utilize how crowds interact with data and each other to construct and enrich large-scale semantic representations.
AUTOMATED DISCOVERY OF LOGICAL FALLACIES IN LEGAL ARGUMENTATIONijaia
This paper presents a model of an algorithmic framework and a system for the discovery of non sequitur fallacies in legal argumentation. The model functions on formalised legal text implemented in Prolog. Different parts of the formalised legal text for legal decision-making processes such as, claim of a plaintiff, the piece of law applied to the case, and the decision of judge, will be assessed by the algorithm, for detecting fallacies in an argument. We provide a mechanism designed to assess the coherence of every premise of a claim, their logic structure and legal consistency, with their corresponding piece of law at each stage of the argumentation. The modelled system checks for validity and soundness of a claim, as well as sufficiency and necessity of the premise of arguments. We assert that, dealing with the challenges of validity, soundness, sufficiency and necessity resolves fallacies in argumentation.
Knowledge lost in information, meanings lost in semantics?Sophie Visser
Knowledge and meaning are complex concepts that are impacted by information systems. As knowledge is codified into information and formalized for computer systems, some meanings may be lost. Three key issues are: (1) meanings are restricted by what can be expressed in language and formalized for machines, (2) formalization requirements of semantics limit the scope of represented meanings, and (3) predefined concepts, definitions and ontologies impose standardized meanings rather than supporting individual interpretations. Representing complex domains like cultural landscapes in information systems thus risks oversimplifying the knowledge and reducing the diversity of meanings.
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.
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.
Ontology is a formal explicit specification of a conceptualization that provides a shared understanding of a domain. An ontology for software engineering can help facilitate communication between distributed development teams by providing a common vocabulary and conceptualization of key software engineering concepts and their relationships. Such an ontology can be modeled using notations like UML class diagrams and activity diagrams to represent important software engineering concepts like classes, activities, and relationships. The software engineering ontology then allows for improved knowledge sharing and communication framework among distributed development teams.
ARTSEDU 2012
Educational Robotics between narration and simulation
Alessandri Giuseppe , Paciaroni Martina
Faculty of Education Sciences - University of Macerata, (MC, Italy)
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.
Semi-automated metadata extraction in the long-termPERICLES_FP7
This presentation was delivered by Emma Tonkin (King's College London) at the Digital Preservation Coalition (DPC) event entitled 'Practical Preservation and People: a briefing about metadata', which took place at the Public Records Office of Northern Ireland, Belfast on 3 December 2015.
Ontology learning techniques and applications computer science thesis writing...Tutors India
At Tutors India, we offer Computer science and Information Technology Research Guidance services – We deliver exceptional work where your dissertation will deserve publication without significant reworking or alternation.
For #Enquiry
https://www.tutorsindia.com
info@tutorsindia.com
(Whatsapp): +91-8754446690
(UK): +44-1143520021
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.
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.
The tutorial has been presented at CAISE 2010. The tutorial discusses the state-of-the-art on research addresseing the quality of data at the conceptual level (conceptual schemas) and of Ontologies
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.
Cognitive computing aims to address complex problems characterized by ambiguity and uncertainty by learning from and interacting with people. The document discusses several topics related to cognitive computing and tacit knowledge, including:
- The history and traditions of cognitive science from the 1950s onward.
- How early experiments in the 1990s explored using computer-mediated telepresence to enable real-time collaborative creative work across distances, finding it can achieve similar results to physical presence.
- Questions around how cognitive computing systems today can help individuals and groups create value and preserve identity while working together.
- Whether true real-time collaborative creative work engaging tacit knowledge is possible digitally, or if physical presence is still needed.
Information among networks and systems of knowledgeJosé Nafría
This document discusses different types of networks and models of information exchange. It begins by defining abstract networks as sets of nodes and links, and discusses how networks can represent both potentialities and actualities. It then covers semantic networks and how they can map both passive concepts and active communicative agents. Models of communication and information exchange are presented, including Shannon's technical model and more inferential semantic models. The role of context and convention in communication is discussed. Finally, the document addresses semantic networks in knowledge representation and the dynamics of scientific knowledge development.
This paper considers how hermeneutics and other related theories may bring new insights into KO. They provide a most realistic representation of the complexity of knowledge and meaning according to which new forms of KOSs could be designed. Computational and conceptual aspects of these issues are discussed taking into account a number of case studies.
The document summarizes Markus Strohmaier's presentation on extracting semantics from crowds at the International Summer School on Semantic Computing in 2011. The presentation discussed how semantics can be extracted from online crowd behaviors like social labeling with hashtags on Twitter, social tagging on sites like Delicious, and social navigation. Methods discussed include analyzing tag relatedness, generality, and hierarchies to emerge semantic structures from folksonomies. The goal is to utilize how crowds interact with data and each other to construct and enrich large-scale semantic representations.
AUTOMATED DISCOVERY OF LOGICAL FALLACIES IN LEGAL ARGUMENTATIONijaia
This paper presents a model of an algorithmic framework and a system for the discovery of non sequitur fallacies in legal argumentation. The model functions on formalised legal text implemented in Prolog. Different parts of the formalised legal text for legal decision-making processes such as, claim of a plaintiff, the piece of law applied to the case, and the decision of judge, will be assessed by the algorithm, for detecting fallacies in an argument. We provide a mechanism designed to assess the coherence of every premise of a claim, their logic structure and legal consistency, with their corresponding piece of law at each stage of the argumentation. The modelled system checks for validity and soundness of a claim, as well as sufficiency and necessity of the premise of arguments. We assert that, dealing with the challenges of validity, soundness, sufficiency and necessity resolves fallacies in argumentation.
Taking a page from Facebook, WiPromo plans to “Provide pre-existing offline community with complementary online service. Then monetize by aggregating a series of deeply penetrated communities. And build strong brand recognition amongst its user base and advertisers”
Nisan Gabbay (2006). Facebook Case Study: Offline Behavior Drives Online Usage
The presentation of Innovating Innovation Manifesto, a set of 20 recommendations proposed by the BIVEE Project (www.bivee.eu) and the community of stakeholders activated in the dedicated national conferences
This document appears to be a list of dates from the Europeanschool that are all repeated on the same day of December 23, 2008. The high level information provided is a list of dates with no other context about the Europeanschool or what occurred on this single date that is listed ten separate times.
These slides are an overview of the process we present at our two-day Internet Intelligence Workshop and three-day Open Source Intelligence Gathering workshop.
The document discusses the future of the Flash platform. It focuses on four main areas: web, video, enterprise, and gaming. For each area, it outlines priorities and new capabilities for Flash, including support for multi-screen applications and devices, enhanced video streaming and protection, productivity tools for enterprise development, and expanded 3D and augmented reality capabilities for gaming. The overall strategy is to continue advancing the Flash platform across various tools, frameworks, servers, and clients to keep it at the center of digital experiences on the modern web.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the evolution of enterprise applications from mainframes to modern mobile apps and the rise of mobility. It notes that users now expect to access enterprise knowledge from any device at any time. Older methods of interacting like paper, whiteboards and desktop PCs are no longer inspiring. The emergence of smartphones and tablets is driving a new generation of mobile apps that provide rich, comfortable interactions. When introducing new mobile devices in a company, IT must address security and manageability while users expect native apps and the latest technology, regardless of corporate strategy or help desk preparedness.
The document discusses strategies for system administrators to prevent failures, including planning for worst case scenarios, minimizing risks, and gracefully recovering from mistakes. It recommends documenting plans, testing changes, verifying success criteria, imagining potential issues, and reflecting on implementations through post-mortems to improve. Tools like documentation wikis, testing frameworks, staging environments, and communication with colleagues can help avoid and recover from failures.
This document provides information about the Software Engineering Group at the Norwegian University of Science and Technology (NTNU). It includes summaries of the research areas, courses taught, and potential master's thesis topics for 13 professors and associate professors in the group. The research areas include software engineering, human-computer interaction, games and simulations, cooperation technologies, and more. The
The document summarizes life in the American colonies, including differences between the New England, Middle, and Southern colonies. In the New England colonies, nearly all residents were Puritans who believed in strict religious rules. The Middle colonies attracted a variety of religious groups, including Quakers and Dutch traders. The Southern colonies' economies centered around cash crops like tobacco and indigo grown with help from indentured servants and slaves. The document also contrasts colonial times with modern times in terms of clothing, food, jobs, housing, and more.
This document takes the reader on a journey through scales of size from 1 meter to distances of billions of light years and back down to fractions of an atom. It explores scales from the macrocosm of the universe down to the microcosm inside an atom in factors of 10. At each level of magnitude, it describes what can be observed, from leaves on a tree to galaxies and nebulae or atomic structures. It encourages thinking about humanity's place in the vast cosmos and how much remains to be learned.
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.
How to model digital objects within the semantic webAngelica Lo Duca
These slides describe the general concept of semantic Web and Linked Data, then they illustrate the concept of digital object. Finally they give a use case.
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.
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.
The document discusses semantic interoperability within a company. It describes several tools that can be used to describe and structure semantics, including ontologies, tagging, classifications, and taxonomies. It provides examples of how these tools can be applied at an enterprise level, including enterprise ontologies, tag clouds, the Zachman framework, and IBM's Information Framework.
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.
My keynote at the Ontologies Come of Age workshop at the International Semantic Web Conference in Bonn Germany. This workshop was named after a paper I wrote about a decade ago.
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.
Ontologies and the humanities: some issues affecting the design of digital in...Toby Burrows
This document discusses issues related to designing digital infrastructure for the humanities using ontologies. It notes that there are many ongoing efforts to develop ontologies for different domains in digital humanities. However, it also acknowledges linguistic, semantic, and conceptual difficulties in representing humanities knowledge through ontologies. As an alternative, it discusses strategies like topic modeling, linked data, and conceptual spaces that may better capture humanistic perspectives on relationships, cognition, and meaning. It argues that future humanities research should look beyond ontologies alone and examine computational modeling from cognitive science and philosophy.
OntoSOC: S ociocultural K nowledge O ntology IJwest
This paper
present
s
a
sociocultural knowledge ontology (OntoSOC) modeling appro
a
ch. Ont
o-
SOC modeling appro
a
ch is based on Engeström‟s
Human Activity Theory (HAT)
.
That Theory allowed us
to identify fundamental concepts and rel
a
tionshi
ps between them. The top
-
down precess has been used to
d
efine differents sub
-
concepts. The
modeled vocabulary permits us to organise data, to facilitate in
form
a-
tion retrieval
by introducing a semantic layer in social web platform architec
ture,
we project t
o impl
e
ment.
This platform can be considered as a «
collective me
mory
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and Participative and Distributed Info
r
mation
System
(PDIS) which will allow Cameroonian communities to share an co
-
construct knowledge on perm
a-
nent organi
z
ed activ
i
ties.
folksonomy, social tagging, tag clouds, automatic folksonomy construction, word clouds, wordle,context-preserving word cloud visualisation, CPEWCV, seam carving, inflate and push, star forest, cycle cover, quantitative metrics, realized adjacencies, distortion, area utilization, compactness, aspect ratio, running time, semantics in language technology
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.
Ph d course on formal ontology and conceptual modelingNicola Guarino
The document discusses conceptual modeling and ontological analysis. It covers several key topics:
1. The importance of ontological analysis to understand the content and assumptions behind information systems, in order to improve semantic interoperability.
2. Concepts, properties, relations, and the distinction between intension and extension. Concepts represent general principles used to determine reference.
3. What constitutes a conceptualization from a cognitive perspective - how humans isolate relevant invariances from physical reality based on perception, cognition, and language to form conceptual domains and relations.
The document provides an overview of the speaker's background and research interests in digital ecosystems modelling. It discusses how socio-technical systems can be modeled using a systems approach rather than just mathematics. It also touches on ideas like knowledge ecosystems, complex systems emergence, and the importance of shared vocabularies. The goal is to engage the audience and potentially find opportunities for collaboration.
Ontology-Based Resource Interoperability in Socio-Cyber-Physical Systems ITIIIndustries
The paper proposes a core ontology of socio-cyberphysical systems for resource interoperability. The ontology comprises the main concepts and relationships which are identified as relevant to model such systems. The approach considers a socio-cyber-physical system comprising cyber space, physical space, and mental space. In the ontology, these spaces are represented by sets of resources. The ontology provides the resources with a common vocabulary to share information and services and therefore makes these resources interoperable. The core ontology is specialized for a socio-cyber-physical system embedded in robotics domain. Technology of online communities is proposed to be used for resource communication.
This document provides an overview of Karen Cham and her work in the field of digital transformation design (DTD). It discusses DTD as a design-led, user-centered method for transforming complex human systems using digital technologies. The document outlines Karen Cham's experience in sectors like technology, media, education and more. It also summarizes some of her academic writings on topics like complexity theory, systems thinking, and designing complex systems.
Semantic technologies for the Internet of Things PayamBarnaghi
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Here are the steps I would suggest for aligning the ontologies:
1. Representatives present their ontology and explain key concepts and relationships.
2. Editor records all concepts and relationships on a whiteboard in a concept map format without evaluation.
3. Representatives discuss each concept and relationship to reach agreement on meaning and resolve any conflicts or ambiguities.
4. Editor incorporates agreed upon concepts and relationships into a single ontology, resolving any structural issues.
5. Representatives review the aligned ontology and provide feedback.
6. Editor incorporates final changes to produce the aligned ontology for use by all groups.
The goal is to understand each perspective, identify areas of overlap and conflict, and work together iteratively
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The creativity wave involves generating ideas through triggers, divergent thinking, walking to stimulate alpha waves, and considering different perspectives. Feasibility assesses the technical, financial, market, and competency feasibility of ideas. Prototyping transforms ideas into working prototypes to test and refine. Engineering transforms successful prototypes into real products by addressing costs, usability, and production plans. The final stage is transferring knowledge to production and ensuring organizations are ready to absorb innovation and change.
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The document discusses different definitions and perspectives on innovation. It defines innovation as enthusiasm, energy, creativity, but also concern, collaboration, and risk-taking. Innovation can target products, processes, services, organizations, business models, and marketing. Key players in innovation include teams within and outside an organization. Innovation can be technology-driven, demand-driven, or come from within or outside an organization. The document also discusses open innovation and how it has evolved from Open Innovation 1.0 to 2.0 with more participation. Open innovation involves actors from industry, academia, government, and the community. Finally, the document discusses whether innovation should be viewed as a process or as a knowledge artifact.
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The presentation introduces in a systematic way a new vision and a new approach for enterprise innovation. The addressed topics are largely drawn upon the work carried out in the European Project BIVEE (www.bivee.eu). In the conclusion, there is a first proposal to start thinking to innovation as a proper discipline, fertilised by several existing scientific areas, form business to engineering, from creative thinking to technology.
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The document discusses methods and tools for enterprise innovation in a networked economy using a knowledge-centric approach. It proposes a virtual innovation factory (VIF) that operates in an innovation space, transforming raw and enabling knowledge into new working knowledge and final products through a value production chain. The VIF is intended to facilitate open innovation using a social semantic knowledge management platform to manage enterprise documents in a semantically enriched way using ontologies. The goal is to support continuous business innovation through knowledge management and transformation of enterprise processes, products, and technologies.
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1. Collaborative Ontology
Engineering and Management
May 20-24, 2013
The Sheraton San Diego Hotel &
Marina
San Diego, California, USA
The 2013 International Conference on
Collaboration Technologies and Systems
(CTS 2013)
Michele Missikoff
Polytechnic University of Marche and LEKS-CNR, Italy
2. Content
• What is an ontology? Why we need them?
• The Semantic Web and Social Semantic
Networks
• On the nature of (computational) knowledge
• Conceptual modeling: principles
• From perception to representation
• Ontology Engineering
• The social dimension of Ontology Building
And ...
• Some practical exercises
2CTS 2013, San Diego
3. The Speaker
Michele Missikoff
• Scientific Advisor at Univ Polytechnic of
Marche (Ancona), for the European BIVEE1
Project
• Coordinator of Lab for Enterprise Knowledge
and Systems, Italian National Research Council
• European Task Force Leader of FInES - Future
Internet Enterprise Systems Research
Roadmap
• Professor of Enterprise Information Systems at
International University of Rome
3CTS 2013, San Diego
(1Business Innovation in Virtual Enteprise Environments)
4. 4
What is an ontology? What is the
Semantic Web? Why we need
them?
Ontology Introduction
5. Ontology: Origins and History
Ontology Introduction 5
• In Philosophy,
fundamental branch of
metaphysics
– Studies “being” or
“existence” and their basic
categories
– Aims to find out what
entities and types of
entities exist
– Identifies and characterises
their properties
(Credits: I. Horrocks)
6. Ontology Introduction 6
What is a Computational Ontology?
From Philosophy to practical use of an Ontology
– It is about what exists, and is relevant for our
purposes, in our domain of interest;
– Needs the consensus of a group which is
representative of the community of interest
– Aims at reaching a shared view of the domain of
interest
– Allows for reduction or elimination of
terminological and conceptual confusion
An ontology is an evolving repository of relevant
concepts, continuously incorporating new meanings
from the interaction with the environment
7. Ontology Introduction 7
An Ontology is …
“… a theory about the nature of beings” (Philosophical view)
“… a formal, explicit specification of a shared
conceptualisation.” (AI view – T.R. Gruber)*
– ‘Formal' refers to the fact that the ontology should be
machine understandable.
– 'Explicit' means that the type of concepts used and the
constraints on their use are explicitly and fully defined.
– 'Shared' reflects the notion that ontology captures
consensual knowledge, that is, it is not restricted to some
individual, but accepted by a group / community.
– A 'conceptualisation' refers to an abstract model of some
phenomena in the world, it identifies the relevant concepts
related to that phenomena.
• In formal terms: Ont = (Conc, Rel, Axioms, Inst)
8. Ontology Introduction 8
An Ontology is … (con’d)
"An ontology defines the common terms and
concepts (meaning) used to describe and
represent an area of knowledge. An ontology can
range in expressivity from a Taxonomy
(knowledge with minimal hierarchy or a
parent/child structure), to a Thesaurus (words
and synonyms), to a Conceptual Model (with
more complex knowledge), to a Logical Theory
(with very rich, complex, consistent and
meaningful knowledge)." [www.omg.org]
9. Ontology Introduction 9
Conceptual models and ontologies
They have common roots, but ...
Conceptual Model
• Traditionally conceived for inter-human communication
• Typically in diagrammatic form (e.g., UML, BPMN)
• Semi-formal representation
– Formal syntax, but intuitive semantics
• Used with a precise goal (e.g., IS engineering)
Computational Ontologies
• Conceived to be ‘fully’ processed by a computer
• Linear (textual) form (supports equivalent diagrammatic
forms)
• Typically represented with a formal language (e.g., RDF(S),
OWL, CG, F-Logic, ...)
• Used to represent an application domain, not a specific system
10. Ontology Introduction 10
Ontologies as Social Artefacts
• An Ontology is a socio-cultural phenomenon, but we
want to describe the concepts in a formal and
unambiguous way, processable by a computer
An ontology contains:
– a set of concepts (e.g., entities, attributes, processes) seen as
relevant in a given domain
– the definitions and inter-relationships among these concepts
– set of Axioms (e.g., constraints) and, in case, instances
• To be used by computers, ontologies must
– have precise definitions, with a formal semantics (Tarski)
– evolve according to an evolving reality and adapt to current
needs and usage of both human users and computers
– be supported by an Ontology Management System
12. Ontology Introduction 12
First Motivations
When starting a cooperation (to work together
or in interacting in social settings), people
and organizations may have different:
– viewpoints
– assumptions
– needs
about the same domain, due to different
contexts, goals, backgrounds and cultures
13. 13
motivations (cont’d)
Furthermore, the frequent use of different:
– jargon
– terminology
sometimes diverging or overlapping, generate confusion.
Even worse,
– concepts
may be mismatched or ill-defined (e.g., delivery_date).
Goal
allow people, organizations, computer applications, smart
objects to effectively cooperate, despite the mentioned
differences
• All computers today can communicate, but it does not imply
that they cooperate (due to different services & data
organization)
• People and organizations do communicate and cooperate, but
with low automatic support (and several misunderstandings)
14. Ontology Introduction 14
Cooperation Problems
The lack of a shared understanding leads to a poor
communication that impacts on:
– effectiveness of people’s cooperation
– flaws in enterprise operations
– even social fragmentation (… tension)
When Information Systems Engineering is involved, further
problems arise on:
– the identification of the requirements for the system
specification
– potential reuse and sharing of system components
– interoperability among systems
Then … ONTOLOGIES
15. Ontology Introduction 15
Benefits of Reference Ontologies
• Business Opportunity analysis
• Partnering
• Interoperability
• Semantic Knowledge Management
• Business / IT Alignment
• Social / Shared vision
By means of
• A collaboration practice for a shared context
understanding
• Ontology management – Building an EO
• Semantic Annotation
• Interoperability among legacy systems
• Sem Search: exact / approximate
• Similarity reasoning
17. Ontology Introduction 17
Progression of Domain
specification
Lexicon - Set of terms (also multi-word) representing
relevant entities and relationships in the domain
Glossary - Alphabetically ordered terms, with their
descriptions, in natural language. First
categorizations according to an Ontology
Framework (e.g., OPAL)
Taxonomy - hierarchy of terms according to a
refinement relation (e.g., ISA)
Thesaurus - First introduction of elationships, such as:
synonyms, antonyms; BT, NT, RT
Semantic Net - Full fledged deployment of Concepts
and Relations: Gen/Spec, part of/HasPart, Sim,
InstOf, … + domRel
20. 20
The Knowedge Society
• European Council: Lisbon Strategy for growth
and jobs
“Europe needs will achieve the largest and most
competitive knowledge-based economy in the planet”
• Investing in knowledge and innovation is
intended to spur the EU's transition to a
knowledge-based and creative economy.
• The "fifth freedom" – the free movement of
knowledge – should thus be established
• Knowledge is a value if embodied in models and
practices of the Society and Production
systems (…New Economy).
(europa.eu/legislation_summaries/employment_and_social_policy/eu2020/growt
h_and_jobs/c11806_en.htm)
Ontology Introduction
21. World is Changing...
... and we need new:
• Systems of values
• Development models
• Social relationships to guarantee
sustainability at:
– Social, economic, environmental levels
Ontology Introduction 21
22. 22
The Advent of the Semantic Web
The collaborative, shared dimension of
Knowledge: The Semantic Web
“The Semantic Web is an extension of the
current Web in which information is given
well-defined meaning, better enabling
computers and people [and Smart Objects] to
work in cooperation.”
(Tim Berners-Lee, James Hendler and Ora Lassila, The
Semantic Web, Scientific American, May 2001)
Ontology Introduction
25. 25
Two kind of resources
Documental Resources (DR): Human-oriented
information and knowledge
Factual K, such as: the Rome Sheraton Hotel has
250 rooms, the prices are…
Intensional K: An Hotel is composed by: a
reception, some rooms, etc…
Procedural K: To make a reservation, prepare first
the credit card, then enter the hotel Web site, …
Semantic Resources (SR): Knowledge to be
‘understood’ and processed by a computer.
x H, y: hotel(x) has(x,y) reception(y) …
Ontology Introduction
26. 26
Human-readable vs Computer-
readable
According to Tim Berners-Lee:
“Today’s web pages are conceived to be human-
readable (in terms of content), we need to find
solutions to make them computer-readable.”
A technical intuition:
• HTML is the language of the Traditional Web,
to represent human-oriented hypermedia docs
• RDF is the language of the Semantic Web, to
represent computer-oriented knowledge
Ontology Introduction
27. What’s computer ‘readability’?
Ontology Introduction 27
What We Say to Dogs
"Stay out of the garbage!
Understand, Ginger? Stay out
of the garbage!"
What Dogs understand
"... blah blah blah blah GINGER
blah blah blah blah ..."
28. 28
Functions of the Traditional Web
• Keyword-based
Information Retrieval
• Hypertext Navigation
• Manual Classification
• Specialised search robots
(Softbots, crawlers, ..)
!
?
Retrieval quality
(precision & recall)
inversely proportional
to data quantity
Ontology Introduction
29. 29
Functions of the the Semantic Web
• Semantic Information
Retrieval
• Machine Reasoning
• Machine-machine
advanced cooperation
• Shared
Conceptualisations
(shared ontologies)
with std knowledge
representation
Retrieval quality directly
proportional to knowledge
quantity
(and reasoning capabilities)
Ontology Introduction
32. 32
The dimensions of knowledge
• Level of explicitness (Nonaka, theory of Ba): Tacit,
Implicit, Explicit
• Addressee (Human, Machine, both)
• Level of declarative (vs procedurale) approach
• Level of formalization (from NL text to
algebra/logics)
• Level of abstraction (from factual to conceptual to
metaCon)
• Synchronic vs Diachronic (Structural vs
Behavioural)
33. 33
Representing Knowledge? For whom, for what
It depends on the:
• Who is the Addressee
– for people (easy to read and manipulate)
– for machines (easy to process automatically)
– to exchange K between people and machines
• What Activity it supports (for people and/or computers)
– preliminary domain investigation and analysis
– decision support and recommender systems
– Data mining
– detail analysis, design and (sw) implementation
– Business transactions
– Knoweledge storage and retrieval
– Semantic query processing (with reasoning)
– Semantic interoperability
– Intelligent user interfaces
34. 34
Level of declarativeness
According to the OMG-MDA vision:
• Descriptive (Computational Independent
Model)
– Ex. Class Diagram, abstract Business Process
model (EPC, UML, …)
• Prescriptive (Platform IM)
– Workflow Management System (Savvion,
TeamWare, OpenFlow, …), no transaction exec
• Operational (P Specific M)
– Process/action exec specification (e.g., BPEL,
BPMN)
– Enterprise Information System, ERP, SCM, …
35. 35
The three formalisation levels
-Informal: typically textual documents (free or
loosely structured text)
-Semiformal: diagrams, tables, forms (rigorous
structure/syntax, intuitive semantics: UML,
EPC, Purchase order, invoice, etc.)
-Formal: rigorous specification languages
(rigorous syntax and semantics: RDF, OWL,
KIF, Z++, PSL/Pi Calculus, Ontolingua, etc.)
36. 36
The Knowledge Tiers
- Factual knowledge: ground information,
representing individuals (DB technology)
- Conceptual knowledge: representing abstract
entities and operations (Enterprise models and
IS design blueprints)
- Methodological knowledge: representing
languages and guidelines for KB construction
(knowledge engineering languages methods,
metamodels, modeling ideas)
40. Social Ontology Building
and Evolution (SOBE)
SOBE supports the building of shared
ontologies through:
• Automatic knowledge extraction
– Analysis of textual documents by using NLP
techniques
• Social participation
– Voting and discussing (forum) for validating
and enriching extracted knowledge
Ontology Introduction 40
41. SOBE Methodology
• Step-wise approach through five incremental
steps (Milestones)
– Lexicon (M1): plain list of terms
– Glossary (M2): terms + natural language definition
– Concept Categorization (M3): in accordance with
the OPAL (e.g., Object, Process, Actor)
– Taxonomy (M4): definition of ISA hierarchy
– Ontology enrichment (M5): additional
relationships (e.g., predication, relatedness)
Ontology Introduction 41
43. Collaborative Dimension
Driven by Web 2.0 and social communities
philosophy
• Voting: accept/discard results of the
automatic extraction (lexicon and glossary)
• Proposing: new terms and definitions to be
validated by participants
• Discussing: for reaching an agreement on
glossary definitions (dedicated forums)
Ontology Introduction 43
44. 44
Conclusions
• Semantic Web applications will involve humans (H),
smart objects and devices (O), mainly improving:
– O2O communication and cooperation, when devices interact
to support human activities and goals achievments
– H2O and H2H (tech-enhanced), with digital technology that
will progressively disappear, allowing ‘natural’ interactions
• Semantic Web needs Ontologies to interpret meanings of
(digital) resources
• Ontologies effectiveness depends on representation
languages, reasoning, and collaborative consensus
reaching
Ontology Introduction