OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
A presentation about Ontology Learning with an overview of the area and some methods used, specially techniques of Ontology Learning from Text. This presentation was part of a seminary in the MSc Course in Computer Science at UFPE - Recife - Brazil.
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
A presentation about Ontology Learning with an overview of the area and some methods used, specially techniques of Ontology Learning from Text. This presentation was part of a seminary in the MSc Course in Computer Science at UFPE - Recife - Brazil.
Ontology Learning from Text
Ontology construction ‘Layer Cake’
Knowledge representation and knowledge management systems
Subtasks in ontology learning
Most Popular Ontology Learning Tools
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.
Slides from the Introduction and Theoretical Foundations of New Media course of the Interactive Media and Knowledge Environments master program (Tallinn University).
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
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
Ontology Learning from Text
Ontology construction ‘Layer Cake’
Knowledge representation and knowledge management systems
Subtasks in ontology learning
Most Popular Ontology Learning Tools
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.
Slides from the Introduction and Theoretical Foundations of New Media course of the Interactive Media and Knowledge Environments master program (Tallinn University).
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
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
Evolution of minds and languages: What evolved first and develops first in ch...Aaron Sloman
SLIDESHARE NOW STUPIDLY DOES NOT ALLOW SLIDES TO BE UPDATED. To find the latest version of these slides go to http://www.cs.bham.ac.uk/research/projects/cogaff//talks/#talk111
The version posted here was last updated on 16 March 2015. There have been several changes since then on the alternative site. Why did Slideshare take such a stupid decision (after being bought by Linkedin?)
A theory is presented according to which "languages" with structural variability and compositional semantics evolved in several species for *internal* use (e.g. in perception, planning, learning, forming goals, deciding, etc.) before *external* languages evolved for communication. The theory implies that such internal languages develop in young humans before a language for communication.
It is is also noted that the standard notion of 'compositional semantics' has to allow for the propagation of semantic content from parts to wholes to be potentially context sensitive at every stage: i.e. current context, speaker intentions, user knowledge, shared goals, can all affect how semantics of larger parts are derived from semantics of smaller parts+syntactic structure. This applies as much to non-verbal languages as to verbal ones.
This theory of how human languages evolved from earlier 'internal languages' (GLs) is inconsistent with the best known published theories of evolution or development of language.
But that does not make it wrong. Moreover, this theory is supported by empirical evidence including the example of deaf children in Nicaragua: http://en.wikipedia.org/wiki/Nicaraguan_Sign_Language
Reorganised several times since first uploaded: most recently 25 Jan 2016
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Slides include link to video of lecture (158MB) http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#ailect2-2015
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Two questions are shown to have deep connections: What are the functions of vision in animals? and How did human languages evolve? The answer given here is that the functions of vision need to be supported by richly structured internal languages (forms of representation used for acquiring, storing, manipulating, deriving and using information), from which it follows that internal languages must have evolved before languages for communication.
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The account of the functions of vision mentions early AI vision, the impact of Marr and the even greater impact of Gibson, but argues that they did not recognize all the functions of vision, e.g. the uses of vision in making mathematical discoveries leading to Euclid's elements.
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Many questions are left unanswered by this research, which is part of the Meta-Morphogenesis project, introduced here:
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html
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A slideshare presentation on "origins of language" by Jasmine Wong, adds some useful additional evidence, but presents a simpler theory:
http://www.slideshare.net/JasmineWong6/origins-of-language
---------------------------------------------------------------------------------------------------------------
Minor corrections+ additions 30-Mar-2015, 1-Apr-2015, 15-Apr-2015 12-Nov-2015
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
UMBEL: Subject Concepts Layer for the WebMike Bergman
This is an intro to UMBEL (Upper Mapping and Binding Exchange Layer), a lightweight ontology for relating Web content and data to a standard set of 20,000 subject concepts. Connecting to the UMBEL structure gives context and coherence to Web data. Via UMBEL, Web content, data and metadata can be linked, made interoperable, and more easily navigated and discovered. These subject concepts have defined relationships between them, and can act as semantic binding nodes for any Web content or data. The UMBEL subject concepts are derived from the OpenCyc version of the proven Cyc knowledge base.
This is a fun, 5 minute presentation about Project Athena, a proposition for a Create Once Publish Everywhere solution at CIPD, UK. HTML, XML, DITA XML, content strategy, taxonomy, metadata, content models
Development of Semantic Web based Disaster Management SystemNIT Durgapur
Semantic Web model In the field of disaster management to structurise the data such that any information needed during emergency will be easily available.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
Semantic Web - Ontologies
1. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Semantic Web
Unit 5: Ontologies & Linked Data
2. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 2
Semantic Web Roadmap:
Controlled growth bottom
up according to this
architecture.
Architecture was (slightly)
modified in the last years.
5.1. Why is RDF not sufficient?
3. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 3
5.1. Sharing a conceptualization
5.2. Ontologies in Computer-Science
5.3. Ontology Language
5.4. Types of Ontologies
5.6. Ontology Tools
5.7. References
5.1. Why is RDF not sufficient?
5.5. Linked Data
4. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 4
Level of knowledge representation and semantics
XML / XML Schema
objects, structure
RDF / RDF Schema
knowledge about
objects, relations
between objects
OWL
domain knowledge,
interconnections
5.1. Sharing a conceptualization
5. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 5
woman
picture
nudity
photo
artwork
porn
artwork
woman
Different people, different perceptions
Users
Author
Resource
collaborative tagging
Web 2.0 approach
authoritative metadata
Semantic Web approach
Search engine
?
Need of a shared
conceptualization
5.1. Sharing a conceptualization
6. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 6
woman
human
isa
man
isa
≠
photo
picture
is a
street
taken
1 0..*
depicted
0..*
0..*
Conceptualization
concepts
relations between
concepts
attributes
name
age
size
name
length
instances
bd. JFK
3 km
Louise Ciccone
54
173 cm
Using the same
ontology allows two
different systems to
communicate and to
reason over (meta)data
5.1. Sharing a conceptualization
7. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 7
5.2. Ontologies in Computer-Science
Ontologies in Computer-Science
An ontology has a common language (symbols, expressions ) syntax
The meaning of the symbols and expressions in an ontology is clear semantics
Symbols and expressions with similar semantics are grouped in classes conceptualization
Concepts are organized in a hierarchical way taxonomy
Implicit knowledge can be made explicit reasoning
An ontology is an explicit, formal specification of a
shared conceptualization (Thomas R. Gruber, 1993)
Conceptualization : abstract model of domain related expressions
Specification : domain related
Explicit : semantics of all expressions is clear
Formal : machine-readable
Shared : consensus (different people have different perceptions)
8. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Classes: (concepts) are abstract groups, sets, or collections of objects (individuals and classes). Here: Thing,
Human, Father, etc are classes.
Taxonomy: hierarchical representation of classes
Individuals: (instances) are the basic, "ground level" components of
an ontology. For example: SerLinck is an individual of the class Man,
formally: Man(SerLinck)
Attributes: (properties) describing objects
(individuals and classes) in the ontology. Here, the
class Human has an attribute hasName and the
individual SerLinck has the attribute value "Serge
Linckels" for the attribute hasName
Structure of ontologies
5.2. Ontologies in Computer-Science
9. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Structure of ontologies
Relationships: (associations) expressing how objects in the ontology are related to each other. Typically a
relation is an attribute whose value is another object in the ontology.
subsumption relation: (is-superclass-of) defines
which objects are members of classes. Here Man
subsumes Father.
There are two common types of relations: the vertical "subsumption"
and (normally) horizontal user defined relations.
Relations can be recursive, e.g, a human has a child
that is human.
user defined: defines any kind of relation between
objects. E.g., hasHusband is a relation from the
class Woman to the class Man.
hasChild
hasHusband
5.2. Ontologies in Computer-Science
10. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Structure of ontologies
Restrictions can be attached to relations:
quantified restrictions, e.g.,
• a woman can have 0 or 1 husband
• a human can have 0 or n children
• every mother must have at least one child
difference, e.g.,
a woman is not a man (a human can be either a
woman or a man, not both)
hasChild
hasHusband
5.2. Ontologies in Computer-Science
11. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Reasoning over ontologies
Axioms are knowledge definitions in the ontology that was explicitly defined and that have not to be proven
true
Examples:
• SerLinck is an individual of the class Father
• MagLinck is an individual of the class Woman
• MagLinck has BobLinck as child
Examples:
• Because SerLinck is an individual of the
class Man, he is human (because Human
subsumes Man)
• Because MagLinck has a child, she is an
individual of the class Mother
• The class Wife can be inductively defined as
being all the women who have at least one
husband
hasChild
hasHusband
Implicit knowledge can be made explicit by logical
induction reasoning over the ontology
5.2. Ontologies in Computer-Science
12. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 12
5.3. Ontology Language
Knowledge representation
How to represent such expressions in a computer-readable way, in order to reason over that
knowledge?
Examples of natural language:
• a woman can have 0 or 1 husband
• every mother must have at least one child
• a woman is not a man (a human can be either a woman or a man, not both)
Informal representation of knowledge:
Beside the structural dimension of an ontology, an ontology uses a common language to
formalize its specifications and conceptualizations
Ontology Language
Examples of ontology languages:
• Web Ontology Language (OWL)
• Ontology Interface Layer (OIL)
• DARPA Agent Markup Language (DAML)
• CycL
• Knowledge Interchange Format (KIF)
Most of these languages are
based on a subset of First Order
Logic (FOL)
13. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
5.3. Ontology Language
Example of knowledge representation
Informal Formal
A woman can only have male husband Woman hasHusband.Man
Every mother must have at least one human
child
Mother Woman hasChild.Human
A human can either be a woman or a man,
not both
Human Woman Man
Woman Man
Man Woman
Formal representation: computer-readable and free of ambiguities (only one interpretation
possible), e.g., code in a programming language
Informal representation: not formal, meaning something not characterized by a clear and
unambiguous interpretation, e.g., natural language
Description Logics
14. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 14
5.4. Types of Ontologies
Upper Ontologies
An upper ontology (or world ontology) is a model of the common objects that are generally
applicable across a wide range of domain ontologies
It contains a core glossary in whose terms objects in a set of domains can be described
Dublin Core metadata element set is a standard for cross-domain information resource
description. In other words, it provides a simple and standardized set of conventions for
describing things online in ways that make them easier to find.
Examples:
The General Formal Ontology (GFO) integrates processes and objects. GFO provides
a framework for building custom, domain-specific ontologies
OpenCyc includes hundreds of thousands of terms along with millions of assertions
relating the terms to each other. One stated goal is that of providing a completely free
and unrestricted semantic vocabulary for use in the Semantic Web.
Suggested Upper Merged Ontology (SUMO) was developed within the IEEE Standard
Upper Ontology Working Group. The goal is to develop a standard ontology that will
promote data interoperability, information search and retrieval, automated inferencing,
and natural language processing.
15. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 15
5.4. Types of Ontologies
Domain Ontologies
A domain ontology models a specific domain, or part of the world
It represents the particular meanings of terms as they apply to that domain
One of the most cited ontologies is the wine ontology it is about the most appropriate
combination of wine and meals
Examples:
The soccer ontology describes most concepts that are specific to soccer: players,
rules, field, supporters, actions, etc. It is used to annotate videos in order to produce
personalized summary of soccer matches
An ontology library for lung pathology is maintained by the FU-Berlin. The aim of the
project "A Semantic Web for Pathology" is to realize a semantic web based retrieval
system for the domain of lung pathology. For this purpose the pathology data is
annotated with semantic references, and the textual pathology reports are used as
descriptions of what the associated images represent
The music ontology provides main concepts and properties for describing music, i.e.
artists, albums, tracks, but also performances, arrangements, etc.
16. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 16
5.4. Types of Ontologies
Expressivity of ontologies
In General, the more specific the ontology is, the more expressive it becomes
catalog
ID
terms
glossary
thesaurus
informal
"is-a"
– +Expressivity
lightweight ontologies
controlled, unambiguous, and finite set of vocabulary in a catalog
(Lassila/McGuinness, 2001)
finite list of terms and meaning in natural language
additional semantics with relations between terms (thesaurus)
conceptualization in a hierarchy of few top-classes
17. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 17
5.4. Types of Ontologies
Expressivity of ontologies
In General, the more specific the ontology is, the more expressive it becomes
catalog
ID
terms
glossary
thesaurus
informal
"is-a"
formal
"is-a"
formal
"instance"
frames
properties
value
restrictions
disjointness
inverse
part-of
general logic
constraints
– +Expressivity
heavyweight ontologies
taxonomies with strict subclass relationships
logical induction over instance checking
classes include property information
using logical quantifiers to express restrictions
(Lassila/McGuinness, 2001)
more complex restrictions,
e.g., disjointness
complete and complex
logical expressions
17
18. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 18
5.4. Types of Ontologies
Expressivity of ontologies
In General, the more specific the ontology is, the more expressive it becomes
catalog
ID
terms
glossary
thesaurus
informal
"is-a"
formal
"is-a"
formal
"instance"
frames
properties
value
restrictions
disjointness
inverse
part-of
general logic
constraints
– +Expressivity
XML
RDF
OWL
19. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 19
Example: Thesaurus of English words
5.4. Types of Ontologies
http://www.visualthesaurus.com/
20. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 20
Example: Linnaen Taxonomy
Linnaean taxonomy is a method of classifying living
things in a taxonomy based on "is-a" relationships
By Carl Linnaeus (1707 – 1778)
5.4. Types of Ontologies
21. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 21
Example: WordNet – lexical database (thesaurus & taxonomy)
5.4. Types of Ontologies
http://wordnet.princeton.edu/
Semantically equivalent words (synsets) are interlinked by means of conceptual-semantic and
lexical relations
hyperonym: a word with a more general meaning
(e.g., animal is a hyperonym of cat),
hyponym: a word with a more specific meaning (e.g.,
cat is a hyponym of animal),
synonym: a word with identical meaning (e.g., car
and automobile are synonyms),
homonym: words with identical spelling but different
meaning (e.g., Ada is a programming language but
also a person).
22. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 22
Example: KR Ontology (Upper Ontology)
5.4. Types of Ontologies
http://www.jfsowa.com/ontology/
Top level ontology with 27
concepts and interlinked (lattice)
Describes general concepts independent
from a given context
23. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 23
Example: Cyc (Upper Ontology)
5.4. Types of Ontologies
http://www.opencyc.org/
Includes hundreds of thousands of terms along
with millions of assertions relating the terms to
each other
Complex queries can be expressed, also in natural
language
24. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 24
Example: UMBEL (Upper Ontology)
5.4. Types of Ontologies
http://www.umbel.org/
Upper Mapping and Binding Exchange Layer
Subset of OpenCyc
28000 concepts to provide commen mapping
points for relating different ontologies to one
another
Shared vocabulary for ontology mapping
Used in Linked data to to link classes of different
sub-ontologies to other datasets, e.g. 48000
mappings to DBpedia
25. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 25
Example: Wine Ontology (Domain Ontology)
5.4. Types of Ontologies
http://www.w3.org/TR/owl-guide/wine.rdf
It is about finding the most appropriate combination of wine and meals
26. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 26
"Using the Web to connect
related data that wasn't
previously linked, or using the
Web to lower the barriers to
linking data currently linked
using other methods."
Linked Open Data:
DBPedia plays a
central role as it
makes the content of
Wikipedia available in
RDF
27. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Linking open-data community project
Goal: “expose” open datasets in
RDF
Set RDF links among the
data items from different
datasets
Set up SPARQL
endpoints
Billions of triples, millions
of “links”
DBpedia is a community effort (1) to extract structured information from Wikipedia, (2) to
provide a SPARQL endpoint to the dataset, and (3) to interlink the DBpedia dataset with
other datasets on the Web
5.5. Linked Data
Inconveniences:
• incomplete
• sometimes inconsistent
28. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
5.5. Linked Data
Semantic Web ::: Serge Linckels, 2011 ::: http://www.linckels.lu/ ::: 28
Used in the IBM Watson artificial intelligence system
Knowledge base developed at the Max Planck Institute for
Computer Science in Saarbrücken
Data is automatically extracted from Wikipedia and other sources
Linked to the DBpedia ontology and uses SPARQL
29. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
SKOS - Simple Knowledge Organization System
Data model based on RDF & RDF-S for
sharing and linking knowledge
• skos:Concept defines a new concept
• skos:broader and skos:narrow
defines that a concept is more general
or more specific than another
• skos:related defines a similar
concept
• skos:exactMatch defines that two
concepts are identical (i.a.,
owl:sameAs)
5.5. Linked Data
http://www.w3.org/2004/02/skos/
30. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
DBpedia
Extracting structured data from Wikipedia
@prefix dbpedia <http://dbpedia.org/resource/>.
@prefix dbterm <http://dbpedia.org/property/>.
dbpedia:Amsterdam
dbterm:officialName "Amsterdam" ;
dbterm:longd "4" ;
dbterm:longm "53" ;
dbterm:longs "32" ;
dbterm:website <http://www.amsterdam.nl> ;
dbterm:populationUrban "1364422" ;
dbterm:areaTotalKm "219" ;
dbterm:hometown 2_Unlimited ;
dbterm:location Anne_Frank_House ;
...
New entry points
(resources)
5.5. Linked Data
31. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Automatic links among open datasets
<http://dbpedia.org/resource/Amsterdam>
owl:sameAs <http://rdf.freebase.com/ns/...> ;
owl:sameAs <http://sws.geonames.org/2759793> ;
...
<http://sws.geonames.org/2759793>
owl:sameAs <http://dbpedia.org/resource/Amsterdam>
wgs84_pos:lat “52.3666667” ;
wgs84_pos:long “4.8833333” ;
geo:inCountry <http://www.geonames.org/countries/#NL> ;
...
5.5. Linked Data
DBpedia
32. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
5.5. Linked Data
33. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Music ontology
5.5. Linked Data
34. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
New entry points
(resources)
5.5. Linked Data
Music ontology
35. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
5.5. Linked Data
Music ontology
36. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
! False positive !
(I promise)
Slideshare
5.5. Linked Data
37. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Slideshare
5.5. Linked Data
38. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Linked Data – putting it all together
5.5. Linked Data
39. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Shared
Cache
FalconS
Sindice
Marbles
Engine
Search
Engines
Linked Data on
the Web
HTTP GET
Amazon
EC2
Linked Data – putting it all together
5.5. Linked Data
40. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 40
Example: OntoEdit
5.6. Ontology Tools
http://www.ontoknowledge.org/tools/ontoedit.shtml
Supports F-Logic, RDF-Schema and OIL
Interface to the F-Logic Inference Engine
and FaCT
41. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 41
Example: Protégé
5.6. Ontology Tools
http://protege.stanford.edu/
The Protégé platform supports two main
ways of modeling ontologies via the
Protégé-Frames and Protégé-OWL
editors
Protégé ontologies can be exported into
a variety of formats including RDF(S),
OWL, and XML Schema
Java Application; multiple plug-ins
available
Interfaces to different reasoners
42. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu 42
create the classes of the taxonomy
create properties that are related to another class (range)
create properties that have literal values
create instances of classes
and properties
Example: Protégé
44. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
http://www.ted.com/talks/manuel_lima_a_visual_history_of_human_knowledge
representation of knowlege :
formaly ass tree
but now as network
45. 5. Introduction to Ontologies
Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu
Ontologies: A Silver
Bullet for Knowledge
Management and
Electronic Commerce
Dieter Fensel
5.7. References
Handbook on Ontologies
Steffen Staab, Rudi Studer
45
Linked Data - Evolving the Web into a
Global Space
Tom Heath, Christian Bizer
E-Librarian Service
User-Friendly Semantic
Search in Digital Libraries
Serge Linckels, Christoph Meinel