The document provides an overview of ontologies and ontology development:
1. It defines ontologies as explicit specifications of conceptualizations in a domain that define concepts, properties, attributes, and relationships to enable knowledge sharing.
2. Ontology components include concepts, properties, restrictions, and individuals. Ontologies can range from single large ontologies to several specialized smaller ones.
3. OWL is introduced as the standard language for representing ontologies, with features like classes, properties, restrictions, and logical operators.
4. A general methodology for ontology development is outlined, including determining scope, reusing existing ontologies, enumerating terms, and defining classes, properties, and other components in an iterative
Seth Earley, Founder & CEO of Earley Information Science and author of the award winning book, "The AI Powered Enterprise" explains what knowledge graphs are, how they compare to ontologies, and how they can be used to power AI driven applications.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets.
The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
Seth Earley, Founder & CEO of Earley Information Science and author of the award winning book, "The AI Powered Enterprise" explains what knowledge graphs are, how they compare to ontologies, and how they can be used to power AI driven applications.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets.
The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
Informed search algorithms are commonly used in various AI applications, including pathfinding, puzzle solving, robotics, and game playing. They are particularly effective when the search space is large and the goal state is not immediately visible. By intelligently guiding the search based on heuristic estimates, informed search algorithms can significantly reduce the search effort and find solutions more efficiently than uninformed search algorithms like depth-first search or breadth-first search.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Linear Regression vs Logistic Regression | EdurekaEdureka!
YouTube: https://youtu.be/OCwZyYH14uw
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
Types of Machine Learning
Regression Vs Classification
What is Linear Regression?
What is Logistic Regression?
Linear Regression Use Case
Logistic Regression Use Case
Linear Regression Vs Logistic Regression
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
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Informed search algorithms are commonly used in various AI applications, including pathfinding, puzzle solving, robotics, and game playing. They are particularly effective when the search space is large and the goal state is not immediately visible. By intelligently guiding the search based on heuristic estimates, informed search algorithms can significantly reduce the search effort and find solutions more efficiently than uninformed search algorithms like depth-first search or breadth-first search.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Linear Regression vs Logistic Regression | EdurekaEdureka!
YouTube: https://youtu.be/OCwZyYH14uw
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
Types of Machine Learning
Regression Vs Classification
What is Linear Regression?
What is Logistic Regression?
Linear Regression Use Case
Logistic Regression Use Case
Linear Regression Vs Logistic Regression
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Introduction to agents and multi-agent systemsAntonio Moreno
Multi-agent systems course at University Rovira i Virgili. Slides mostly based on those of Rosenschein, from the content of the book by Wooldridge.
Lecture 1-Introduction to agents and multi-agent systems.
MAS course at URV. Lecture 4, agent types (specially interface agents, information agents, hybrid systems, agentification). Based on diverse resources.
The Six Category Ontology: Basic Formal Ontology and Its ApplicationsBarry Smith
Basic Formal Ontology (BFO) is a small, domain-neutral, upper-level ontology that is used to support integration of domain-specific ontologies in scientific, military, clinical and other areas.
Like Lowe's 4CO, BFO divides reality into particulars and universals. But it replaces 4CO's dichotomy of substantials and non-substantials with a trichotomy of independent continuants, dependent continuants, and occurrents.
I will sketch the BFO ontology and show how it is being used as a starting point for the creation of domain ontologies to support data integration in scientific research.
Poster presented at the 2014 European Conference on Artificial Intelligence - Unsupervised semantic clustering of Twitter hashtags - automatic topic detection in Twitter
To view recording of this webinar please use below URL
http://wso2.com/library/webinars/2015/11/wso2-product-release-webinar-wso2-complex-event-processor-4.0/
In this webinar, Lasantha and Suho will discuss the following key features and improvements in detail:
Integrating WSO2 CEP with Apache Storm to achieve distributed real-time stream processing
Key features of the latest version of Siddhi
New transports that enhances integration capabilities of WSO2 CEP
Creating query templates using execution manager
Using the analytics dashboard to visualize results in real-time
A Comparative Study Ontology Building Tools for Semantic Web Applications IJwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing,
especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms
and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all
possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely
available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d)
market status and penetration. The results of the review in ontologies are analyzed for each application area, such
as transport, tourism, personal services, health and social services, natural languages and other HCI-related
domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks.
Although each tool provides different functionalities, most of the users just use only one, because they are not able
to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different
ontologies with different development and management tools. The paper is also concerns the detection of
commonalities and differences between the examined ontologies, both on the same domain (application area) and
among different domains.
A Comparative Study Ontology Building Tools for Semantic Web Applications dannyijwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing, especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d) market status and penetration. The results of the review in ontologies are analyzed for each application area, such as transport, tourism, personal services, health and social services, natural languages and other HCI-related domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks. Although each tool provides different functionalities, most of the users just use only one, because they are not able to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different ontologies with different development and management tools. The paper is also concerns the detection of commonalities and differences between the examined ontologies, both on the same domain (application area) and among different domains.
A Comparative Study of Ontology building Tools in Semantic Web Applications dannyijwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing,
especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms
and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all
possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely
available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d)
market status and penetration. The results of the review in ontologies are analyzed for each application area, such
as transport, tourism, personal services, health and social services, natural languages and other HCI-related
domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks.
Although each tool provides different functionalities, most of the users just use only one, because they are not able
to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different
ontologies with different development and management tools. The paper is also concerns the detection of
commonalities and differences between the examined ontologies, both on the same domain (application area) and
among different domains.
In this paper we present the SMalL Ontology for malicious software classification, SMalL Java Application for antivirus systems comparison and the SMalL knowledge based file format for malware related attacks. We believe that our ontology is able to aid the development of malware prevention software by offering a common knowledge base and a clear classification of the existing malicious software. The application is a prototype regarding how this ontology might be used in conjunction with known antivirus capabilities to offer a comprehensive comparison.
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)Antonio Moreno
Presentation at workshop on recommender systems at WI-2014.
Automatic learning of keyword-based preferences through the analysis of the implicit information provided by the interaction of the user.
Automatic and unsupervised topic discovery in social networksAntonio Moreno
Research seminar given at the Poznan University of Technology, Poland, June 2014. The topic was the automatic and unsupervised discovery of topics in social networks.
URV-UPC-UB Master on Artificial Intelligence (Tarragona & Barcelona). Please contact antonio.moreno@urv.cat if you intend to attend this Master and you plan to be based in Tarragona. Contact Dr Ulises Cortés (UPC) at ia@lsi.upc.edu for general queries about the Master or if you plan to be based on Barcelona. The admission period for the 2014-15 course is already open, you can apply at https://preinscripcio.upc.edu/home_candidat.php?idioma=3 .
URV Master on Computer Engineering: Computer Security and Inteligent SystemsAntonio Moreno
Courses offered at the URV Master on Computer Engineering: Computer Security and Intelligent Systems. On-line admission requests: March & April (www.urv.cat). Admission: early May. Start of the course: September. Length: 90 ECTS (3 semesters).
Candidates that hold a long Engineering Bachelor degree (5 years) or previous Masters courses may have some of the Master topics omitted and they might complete the Master in 2 semesters. Please contact antonio.moreno@urv.cat if you are interested in attending the Master. Contact postgraduate@urv.cat for details on personal and academic documentation, visa, accommodation, etc. The Computer Science and Mathematics Department and URV might offer partial scholarships (teaching assistants) to those candidates that are admitted in the Master in May.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Connector Corner: Automate dynamic content and events by pushing a button
Lect6-An introduction to ontologies and ontology development
1. LECTURE 6:
An introduction to ontologies
and ontology development
Artificial Intelligence II – Multi-Agent Systems
Introduction to Multi-Agent Systems
URV, Winter-Spring 2010
(Based on a presentation by Dr David Sánchez)
2. Outline of the lecture
Ontologies
Definition
Components
Use in MAS
OWL: Web Ontology Language
A method for ontology development
3. Ontologies in FIPA-ACL
You have come across ontologies before in
this course:
(cfp
:sender (agent-identifier :name j)
:receiver (set (agent-identifier :name i))
:content
"((action (agent-identifier :name i)
(sell book “The Lord of the Rings”)
(any ?x (and (= (price book) ?x) (< ?x 10)))))"
:ontology book-market
:language fipa-sl)
4. What Is An Ontology (I)
Tom Gruber:
Short answer: An ontology is a specification of a
conceptualization
Long answer: […] an ontology is a description
(like a formal specification of a program) of the
concepts and relationships that can exist in a
given domain of discourse for an agent or a
community of agents
5. What Is An Ontology (II)
An ontology is an explicit description of a domain
concepts
properties and attributes of concepts
restrictions on properties and attributes
individuals (often, but not always)
An ontology defines
a common vocabulary
a shared understanding of a domain among a set of
agents
6. Why Develop an Ontology?
To share a common understanding of the
structure of information
among people
among software agents
To make domain assumptions explicit
To enable reuse of domain knowledge
to avoid “re-inventing the wheel”
to introduce standards to allow interoperability
7. Ontology components
Concepts
Disease, Treatment, Symptom
Properties and attributes of concepts
Causes, OccursIn, Receives
Restrictions on properties and attributes
Cancer always Receives Radiotherapy
Individuals (often, but not always)
“John Smith’s cough” is a particular Symptom
8.
9. What Is “Ontology Engineering”?
Defining concepts in the domain (classes)
Arranging the concepts in a hierarchy
(subclass-superclass hierarchy)
Defining attributes and properties that
classes can have and restrictions on their
values
Defining individuals and filling in property
values
10. Size and scope of an ontology
Two extremes (the reality is usually something in
between):
One huge ontology that captures "everything“ in the domain
One (small) ontology for each specific application
A A O A
A O
A O
O A A A
O O O
A A A
A O A A
11. "One large ontology" approach (I)
Benefits
Few or no internal inconsistencies
Easier to find for an application developer
Uniform documentation
Less overlapping work!
12. "One large ontology" approach (II)
Drawbacks
Who maintains it?
Who is responsible?
Heavy and slow to use (both for human
users and for applications)
Difficult to take into account everybody's
opinions and wishes at design time and
when updating
Difficult and expensive construction
13. Example: Unified Medical Language
System (UMLS)
Metathesaurus
Over 1 million biomedical concepts
Integrates 100 vocabularies and classification
systems
ICD-10: International classification of diseases (more
than 12400 codes)
MeSH: Medical Subjects Headings (more than 25,000
descriptors)
LOINC: Logical Observation Identifiers Names and
Codes (58,000 observation terms)
SNOMED CT: Systematized Nomenclature of Medicine -
- Clinical Terms (over 1 million medical concepts)
14. "Several small ontologies" approach (I)
Benefits
Ontologies fit perfectly the application
demands
Smaller, and thus faster to use
Easier to understand and to form the
complete picture of an ontology (fewer
concepts and interrelations)
15. "Several small ontologies" approach (II)
Drawbacks
Different ontologies do not fit together without
either
central coordination body, or
ontology alignment software
Overlapping work - same concepts defined in
multiple ontologies, either in the same way or
(even worse!) differently
Application developers have to choose between
multiple incomplete options
17. Some mixed approaches
A O A O A O
A
A O A O O
O O O
upper O O
ontology A O A
A O domains
A O
A mediating A A none
software
A
A O A
O M A
O A A
A A
A A
A
18. Importance in MAS (I)
Agent-systems are typically distributed
systems
There is certainly the possibility of being
able to access different domain ontologies
Agent-systems consisting of a single
agent are rare and often not useful
Agents typically need to communicate with
each other
Agents should understand each other
19. Importance in MAS (II)
People often design and implement
agents independently, unaware of
other developers working in the same
domain
Agents' understanding of each other is
mostly based on ontologies
20. Usual alternatives (I)
Single, common
A A ontology
Uses:
A MAS developed by a
O A
unique group
[Practical exercise]
A A Well-structured domain
A
with a jointly agreed
standard vocabulary
[Medicine]
21. Usual alternatives (II)
Common core
A O
ontology (e.g. high
A
level ontologies like
A O WordNet)
O complemented with
O O
A
especialised lower-
A O
level classes and
instances locally by
each agent
22. Usual alternatives (III)
A A
O
A
O M A
O
A
A
A
Application that maps the concepts and
relationships in different partial domain ontologies
Usually quite complex, and with human supervision
Union of previous MASs
23. Outline of the lecture
Ontologies
Definition
Components
Use in MAS
OWL: Web Ontology Language
A method for ontology development
29. OWL: Web Ontology Language
The newest ontology representation language
Since October 2009, OWL2
Standard worldwide notation
Designed to bring semantic content to the Web
(Semantic Web)
WebOnt group developed the OWL formalism
OWL language now a W3C recommendation
http://www.w3.org/TR/2009/REC-owl2-quick-reference-
20091027/ OWL2 Quick Reference Guide (October 2009)
30. OWL: Language components
RDF Schemas Features
Equality and Inequality
Property Characteristics
Property Restrictions
Logical Operators
31. RDF Schemas Features
They define basic ontological components
Classes
Subclasses
Individuals
Properties
Subproperties
Domain
Range
32. Classes
Classes are sets of individuals with common
characteristics
A Class should be described such that it is possible
for it to contain Individuals
Classes that cannot possibly contain any individuals
are said to be inconsistent
Eg: Disorder, Patient, Treatment, Symptom
33. Subclasses
Define class specializations by constraining
their coverage
Ex: Breast Cancer is a subclass of Cancer
Class hierarchies can be specified by making
one or more statements that a class is a
subclass of another class
34.
35. Individuals (Instances)
Individuals are the specific objects in the domain
Individuals may be (and are likely to be) members
of multiple Classes
Ex. St_Johns_Hospital, Peter_Smith_disorder
36. Properties
Properties can be used to state relationships
between individuals or from individuals to data
values
Relationships in OWL are binary
Subject predicate Object
Individual a hasProperty Individual b
Individual hasProperty Value
Eg: hasSymptom, isCausedBy, Author
37. Property types
Object Property: relates individuals
Establishes a relationship between objects
Datatype Property: relates individuals to data (int,
string, float etc)
Can be considered “attributes” of the instance
Annotation Property: for attaching metadata to
classes, individuals or properties
E.g. version, author, comment
40. Sub Properties
Defines properties specializations by
constraining their coverage
Make hierarchies from one or more
statements that a property is a subproperty of
one or more other properties
Ex. hasScientificName is a subPropertyOf
hasName
41. Domain
It indicates the individuals to which the
property should be applied
If a property relates an individual A to
another individual B, and the property has a
class C as its domain, then the individual A
must belong to class C
Ex. hasSymptom has the domain
Disorder
X hasSymptom Y X is a Disorder
42. Range
It indicates the individuals to which the
property should be applied
If a property relates an individual A to another
individual B, and the property has class C as
its range, then the individual B must belong to
class C
Ex. hasSymptom has a range of Symptom
X hasSymptom Y Y is a Symptom
43. Equality and Inequality
OWL terms that allow expressing equalities
and inequalities between ontological
components
EquivalentClass: two classes are equivalent
EquivalentProperty: two properties are
equivalent
SameIndividualAs: different names that refer to
the same individual
DifferentFrom: two individuals are different
AllDifferent: all members of a list are distinct
and pairwise disjoint
44. Property Characteristics
They define the semantics of properties
InverseProperty: one property is the inverse of
another
TransitiveProperty: the property is transitive
SymmetricProperty: the property is symmetric
FunctionalProperty: the property has a unique
value
InverseFunctionalProperty: The inverse of the
property is functional
45. Property Restrictions (I)
Define some constraints on the use of
properties
AllValuesFrom: all the values in the range of a
property belong to a given class
Cancer isTreatedWith [AllValuesFrom Radiotherapy]
SomeValuesFrom: at least one value in the range
of a property belongs to a given class
Flu hasSymptom [SomeValuesFrom Fever]
46. Property Restrictions (II)
MinCardinality, MaxCardinality:
minimum/maximum number of individuals to
whom you can be related with a certain
property
47. Logical Operators (I)
Define classes out of other classes
IntersectionOf
Tuberculosis_Symptoms = Fever IntersectionOf
Coughing_Blood
UnionOf
Flu_Symptoms = Fever UnionOf Vomit
ComplementOf
StandardDisorder = ComplementOf
ContagiousDisorder
48. Logical Operators (II)
DisjointWith: two classes are disjoint
Symptom DisjointWith Cause
OneOf: defines a class by enumerating all
the individuals that belong to it
Hospitals is OneOf {University-Hospital},
{St_John}, {City-Clinic}
49. OWL - Conclusions
OWL is a language for representing ontologies,
which extends frame languages
OWL has a rich set of features
There exist reasoners to check the consistency
of an ontology
Before building a knowledge base (ontology) an
study of the domain is required (in order to
determine constraints, relationships and
incompatibilities)
50. Outline of the lecture
Ontologies
Definition
Components
Use in MAS
OWL: Web Ontology Language
A method for ontology development
52. General golden rules
There is not one ‘correct’ way to model a
domain
There are always different structuring
possibilities
Ontology development is necessarily an
iterative process
Concepts in the ontology should be close to
(physical or logical) objects –nouns- and
relationships –verbs- in the domain of
interest
53. I-Determine Domain and Scope
determine consider enumerate define define define create
scope reuse terms classes properties restrictions instances
Goal: limit the scope of the model
What is the domain that the ontology will cover?
For what are we going to use the ontology?
To what types of questions (competency questions) should
the information in the ontology provide answers?
Who will use and maintain the ontology?
Answers to these questions may change during the lifecycle
54. Limiting the scope
An ontology should not contain all the
possible information about the domain
No need to specialize or generalize more than the
application requires
No need to include all the possible properties of a
class
Only the most relevant properties
Only the properties that the application requires
55. Competency Questions
Incremental explicit list of questions that the
final ontology knowledge base should be able
to answer
Is cancer contagious or not?
Which symptoms define the flu disorder?
Which are the causes of hypertension?
Which treatment should I apply for a patient that
is allergic to penicillin and has flu?
56. II-Consider Reuse
determine consider enumerate define define define create
scope reuse terms classes properties restrictions instances
Why reuse other ontologies?
To avoid repeating the work
To interact with the tools that use other ontologies
To use ontologies that have been validated through use in
previous applications
To make the final knowledge base compatible with
predefined standards (e.g. MeSH, UMLS)
57. What to Reuse?
Domain-specific standard terminology
Unified Medical Language System (UMLS)
MeSH, ICD10
59. III-Enumerate Important Terms
determine consider enumerate define define define create
scope reuse terms classes properties restrictions instances
Goal: build a complete list of terms in the delimited
scope.
Are they the appropriate ones to answer all the
Competency Questions?
Which are the terms we need to talk about?
What do we want to say about the terms?
Make a comprehensive list of the terms without
considering (here) the overlap between concepts they
represent, relations among terms, or whether the
concepts are classes or properties
61. IV-Define Classes and a Class Hierarchy
determine consider enumerate define define define create
scope reuse terms classes properties restrictions instances
Goal: find out in the list of terms those which represent
concepts in the domain
A class is a concept in the domain
A class of Disorders
A class of Symptoms
A class of Cancers
A class is a collection of elements with similar properties
A class can later be instantiated
John’s blood disorder
62. Class Inheritance
Classes usually constitute a taxonomic hierarchy (a
subclass-superclass hierarchy)
An instance of a subclass is an instance of a
superclass
If you think of a class as a set of elements, a
subclass is a subset that has a certain common
characteristic
63. Class Inheritance - Example
Cancer is a subclass of Disorder
Every cancer is a disorder
Lung cancer is a subclass of Cancer
Every lung cancer is a cancer
65. Modes of Development
Top-down: define the most general concepts
first and then specialize them
Bottom-up: start with the most specific
concepts and then organize them in more
general classes
Combination: define the more relevant
concepts first and then generalize and
specialize them as necessary
66. Documentation
Classes (and properties) usually have
documentation
Describing the class in natural language
Listing domain assumptions relevant to the class
definition
Listing synonyms
Different labels for different languages
67. Some hints
If a class only has one child, there may be a
modelling problem
If a class has more than a dozen children,
additional subcategories may be necessary
Subclasses of a class usually …
have additional properties
have different restrictions
participate in different relationships
68. V-Define Properties of Classes
determine consider enumerate define define define create
scope reuse terms classes properties restrictions instances
Describe attributes of instances of the class and
relations to other instances
[Attributes] For each disorder we want to know its natural
language name, its scientific name, its ICD-10 code, etc.
[Relations to other concepts] Each disorder has symptoms,
causes, treatments, etc.
70. Properties
Datatype vs Object properties
Datatype properties (attributes)
Contain primitive values (strings, numbers)
Disorder name: string
Disorder scientific name: string
Disorder ICD-10 code: float
Disorder contagiousness: boolean
Object properties (relationships)
Contain (or point to) other objects
A syndrome has a set of symptoms
A disease can be the cause of a syndrome
An intervention plan is associated with a syndrome
71. Property and Class Inheritance
A subclass inherits all the properties from
its superclass
If a disorder has a name and a contagiousness, a Flu
disorder also has a name and a contagiousness
If a class has multiple superclasses, it
inherits properties from all of them
Leukemia is both a Blood disorder and a Cancer
72. VI-Property Restrictions
determine consider enumerate define define define create
scope reuse terms classes properties restrictions instances
Property restrictions constrain or limit the set
of possible values for a property
The scientific name of a disorder is a string
The symptoms of any disorder have to be instances of the
Symptom class
A disorder is required to have at least one MeSH code
73. Common Restrictions
Cardinality: the number of values a property
has
Value type: the type of values a property has
Minimum and maximum value: a range of
values for a numeric property
Default value: the value a property has
unless explicitly specified otherwise
74. Domain and Range of a Property
Domain of a property: the class (or classes) that
have the property
More precisely: class (or classes) of instances which can
have the property
Which are the classes that can use a property?
Range of a property: the class (or classes) to which
property values belong
Which are the classes restricting the property possible
values?
75. Restrictions and Class Inheritance
A subclass inherits all the properties
restrictions from its superclass
A subclass can override the restrictions to
“narrow” the list of allowed values
Make the cardinality range smaller
Replace a class in the range with a subclass
76. VII-Create Instances (Individuals)
determine consider enumerate define define define create
scope reuse terms classes properties restrictions instances
Choose the class of the instance to be created
Create an instance of a class
The class becomes a direct type of the instance
Any superclass of the direct type is a type of the instance
Assign property values for the instance frame
Property values should conform to the restrictions
Knowledge-acquisition tools often check it
77. Extra material in Moodle space
OWL official description
Link to Protégé web page