This document discusses properties in ontology and logical reasoning, including transitive, symmetric, reflexive, and other properties. It provides examples of how different property types allow for certain inferences between related and unrelated nodes. It also discusses part-whole relations and reasoning with multiple hierarchical properties. References are listed on ontology modeling and OWL 2.0 specifications from the W3C.
Using the Semantic Web Stack to Make Big Data SmarterMatheus Mota
This presentation will discuss how just a few parts of the Semantic Web Cake can already boost your analytics by making your (big) data smarter and even more connected.
A little more semantics goes a lot further! Getting more out of Linked Data ...Michel Dumontier
This tutorial will provide detailed instruction to create and make use of formalized ontologies from linked open data for advanced knowledge discovery including consistency checking and answering sophisticated questions.
Automated reasoning in OWL offers the tantalizing possibility to undertake advanced knowledge discovery including verifying the consistency of conceptual schemata in information systems, verifying data integrity and answering expressive queries over the conceptual schema and the data. Given that a large amount of structured knowledge is now available as linked data, the challenge is to formalize this knowledge iso that intended semantics become explicit and that the reasoning is efficient and scalable. While using the full expressiveness of OWL 2 yields ontologies that can be used for consistency verification, classification and query answering, use of less expressive OWL profiles enable efficient reasoning and support different application scenarios. In this tutorial,
- we describe how to generate OWL ontologies from linked data
- check consistency of knowledge
- automatically transform ontologies into OWL profiles
- use this knowledge in applications to integrate data and answer sophisticated questions across domains.
- expressive ontologies enables data integration, verifying consistency of knowledge and answering questions
- formalization of linked data will create new opportunities for knowledge discovery
- OWL 2 profiles support more efficient reasoning and query answering procedures
- recent technology facilitates the automatic conversion of OWL 2 ontologies into profiles
- OWL ontologies can dramatically extend the functionality of semantically-enabled web sites
Publishing Python to PyPI using Github Actions.pptxCraig Trim
This presentation provides a straightforward guide to publishing Python projects on PyPI using GitHub Actions. It's a practical walkthrough for developers on automating the release process of their Python packages. You'll learn how to set up a PyPI token, configure GitHub workflows, and push updates that trigger automatic package deployment. This resource is for anyone looking to eliminate manual uploads to PyPI with a straightforward approach to using GitHub's tools for continuous integration and deployment.
Using the Semantic Web Stack to Make Big Data SmarterMatheus Mota
This presentation will discuss how just a few parts of the Semantic Web Cake can already boost your analytics by making your (big) data smarter and even more connected.
A little more semantics goes a lot further! Getting more out of Linked Data ...Michel Dumontier
This tutorial will provide detailed instruction to create and make use of formalized ontologies from linked open data for advanced knowledge discovery including consistency checking and answering sophisticated questions.
Automated reasoning in OWL offers the tantalizing possibility to undertake advanced knowledge discovery including verifying the consistency of conceptual schemata in information systems, verifying data integrity and answering expressive queries over the conceptual schema and the data. Given that a large amount of structured knowledge is now available as linked data, the challenge is to formalize this knowledge iso that intended semantics become explicit and that the reasoning is efficient and scalable. While using the full expressiveness of OWL 2 yields ontologies that can be used for consistency verification, classification and query answering, use of less expressive OWL profiles enable efficient reasoning and support different application scenarios. In this tutorial,
- we describe how to generate OWL ontologies from linked data
- check consistency of knowledge
- automatically transform ontologies into OWL profiles
- use this knowledge in applications to integrate data and answer sophisticated questions across domains.
- expressive ontologies enables data integration, verifying consistency of knowledge and answering questions
- formalization of linked data will create new opportunities for knowledge discovery
- OWL 2 profiles support more efficient reasoning and query answering procedures
- recent technology facilitates the automatic conversion of OWL 2 ontologies into profiles
- OWL ontologies can dramatically extend the functionality of semantically-enabled web sites
Publishing Python to PyPI using Github Actions.pptxCraig Trim
This presentation provides a straightforward guide to publishing Python projects on PyPI using GitHub Actions. It's a practical walkthrough for developers on automating the release process of their Python packages. You'll learn how to set up a PyPI token, configure GitHub workflows, and push updates that trigger automatic package deployment. This resource is for anyone looking to eliminate manual uploads to PyPI with a straightforward approach to using GitHub's tools for continuous integration and deployment.
SAS University Edition - Getting StartedCraig Trim
Get Started with SAS University Edition on your local machine using Virtual Box to host a pre-installed instance. Work through the initial setup and configuration and run SAS code from the training modules.
Octave - Prototyping Machine Learning AlgorithmsCraig Trim
Octave is a high-level language suitable for prototyping learning algorithms.
Octave is primarily intended for numerical computations and provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The syntax is matrix-based and provides various functions for matrix operations. This tool has been in active development for over 20 years.
There are many words in english that end with the suffix "-nym" or "-nymy". This comes from the ancient Greek ὄνυμα, meaning "name" or "word", and could even be loosely translated as "state of being".
A categorization of onomastic terminology is a helpful step in understanding data. In the automated creation of a semantic model, it is neccessary to develop patterns. Semantic models are primarily composed of space (static information) and time (process / event oriented). Patterns built around onoma help is deriving the former.
This is not a complete list of all Onoma. In many respects, the class of words ending with "-nym" could be considered open. Neologism (the type of words belonging to the class "neonym") can be easily created to describe any category for any entity type.
An Ontology is a description of things that exist and how they relate to each other. Ontologies and Natural Language Processing (NLP) can often be seen as two sides of the same coin.
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
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.
SAS University Edition - Getting StartedCraig Trim
Get Started with SAS University Edition on your local machine using Virtual Box to host a pre-installed instance. Work through the initial setup and configuration and run SAS code from the training modules.
Octave - Prototyping Machine Learning AlgorithmsCraig Trim
Octave is a high-level language suitable for prototyping learning algorithms.
Octave is primarily intended for numerical computations and provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The syntax is matrix-based and provides various functions for matrix operations. This tool has been in active development for over 20 years.
There are many words in english that end with the suffix "-nym" or "-nymy". This comes from the ancient Greek ὄνυμα, meaning "name" or "word", and could even be loosely translated as "state of being".
A categorization of onomastic terminology is a helpful step in understanding data. In the automated creation of a semantic model, it is neccessary to develop patterns. Semantic models are primarily composed of space (static information) and time (process / event oriented). Patterns built around onoma help is deriving the former.
This is not a complete list of all Onoma. In many respects, the class of words ending with "-nym" could be considered open. Neologism (the type of words belonging to the class "neonym") can be easily created to describe any category for any entity type.
An Ontology is a description of things that exist and how they relate to each other. Ontologies and Natural Language Processing (NLP) can often be seen as two sides of the same coin.
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
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.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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/
17. power on hasSynonym turn on
turn on hasSynonym switch on
power on hasSynonym switch on
switch on hasSynonym turn on
switch on hasSynonym power on
turn on hasSynonym power on
21. if A is part of B
and A != B then B
is not part of A
22. 1. OWL 2.0 Primer. 11 Dec 2012. W3C.
http://www.w3.org/TR/owl2-primer/
2. Allemang, Dean; Hender, James. Semantic Web for the Working
Ontologist, 2nd Edition. Elsevier, 2011. Book.
3. OWL 2 Web Ontology Language RDF-Based Semantics (Second Edition).
11 Dec 2012. W3C.
http://www.w3.org/TR/owl-rdf-based-semantics
4. AsymmetricProperty. 29 April 2009. semanticweb.org.
http://owl.semanticweb.org/page/New-Feature-AsymmetricProperty-001-
RDFXML
5. Rector, Alan; Welty, Chris. Simple part-whole relations in OWL Ontologies.
11 Aug 2005. W3C.
http://www.w3.org/2001/sw/BestPractices/OEP/SimplePartWhole/
Editor's Notes
In mathematics, a relationship R is said to be transitive if R(a, b) and R(b,c) implies R(a,c). The same idea is used for the OWL construct owl:TransitiveProperty. A simple example of a transitive property would be if the Windows Operating System has a version Windows XP, which in turn has a version (or type) of Windows XP SP2, then Windows has version Windows XP SP2. Windows hasVersion Windows XP Windows XP hasVersion Windows XP SP2 Windows hasVersion Windows XP SP2 Because owl:TransitiveProperty is a class of properties, we can assert in our model that a given property (such as hasVersion) is a member of the class: ?p rdf:type owl:TransitiveProperty :hasVersion rdf:type owl:TransitiveProperty From the OWL 2.0 Primer [1] (emphasis my own): Now have a look at a property hasAncestor which is meant to link individuals A and B whenever A is a direct descendant of B. Clearly, the property hasParent is a “special case” of hasAncestor and can be defined as a subproperty thereof. Still, it would be nice to "automatically" include parents of parents (and parents of parents of parents). This can be done by defining hasAncestor as transitive property. A transitive property interlinks two individuals A and C whenever it interlinks A with B and B with C for some individual B.
A functional property is one for which there can be just one value. Genealogy seems to lend itself here, as it does for many of these examples. A person can have just one biological parent, so hasMother (or, more precisely, the sub-property hasBiologicalMother) should be marked functional. Likewise, a social security number is (or, should be) unique. If two entities have the same social, we can reasonably draw an inference that these two entities refer to the same person. Bob Smith hasMother Sara Smith Bob Smith hasMother Sally Smythe Sara Smith owl:sameAs Sally Smythe The owl:FunctionalProperty provides us with a means of inference that two resources are the same, even if the explicit relationships in our graph might say otherwise. ssn-name rdf:type owl:FunctionalProperty I might consider the rdf:type property as a functional property. Consider the use of is-a pattern extraction on unstructured data in a corpus. The following triples are extracted: Slovakia rdf:type placeSlovakia rdf:type country Slovakia rdf:type land I might start to infer that (place == country == land) with some degree of confidence. From the OWL 2.0 Primer (emphasis my own): Consider a property hasHusband . As every person can have only one husband (which we take for granted for the sake of the example), every individual can be linked by the hasHusband property to at most one other individual. Note that this statement does not require every individual to have a husband, it just states that there can be no more than one. Moreover, if we additionally had a statement that Mary's husband is James and another that Mary's husband is Jim, it could be inferred that Jim and James must refer to the same individual.
The symmetric property is easily understood. As the name suggests, this implies a relationship is bi-directional, even if the relationship was only modeled in one direction. Tim siblingOf Jim Jim siblingOf Tim From the OWL 2.0 Primer: In some cases, a property and its inverse coincide, or in other words, the direction of a property doesn't matter. For instance the property hasSpouse relates A with B exactly if it relates B with A. For obvious reasons, a property with this characteristic is called symmetric, and it can be specified as follows
The OWL 2 construct AsymmetricObjectProperty allows it to be asserted that an object property expression is asymmetric - that is if the property expression OPE holds between the individuals x and y, then it cannot hold between y and x. Note that asymmetric is stronger than simply not symmetric.[3, 4]. Stewie hasParent Peter Peter hasParent Stewie From the OWL 2.0 Primer On the other hand, a property can also be asymmetric meaning that if it connects A with B it never connects B with A. A note was added in the RDF-based semantics rec pointing out that a property being asymmetric is a much stronger notion than its being non-symmetric, and that being symmetric is a much stronger notion than being non-asymmetric.
In a social network, Peter knows JimBob. Use of the reflexive property allows us to cover the obvious case – Peter knows Peter and JimBob knows JimBob. Peter knows JimBob Peter knows Peter From the OWL 2.0 Primer: Properties can also be reflexive: such a property relates everything to itself. For the following example, note that everybody has himself as a relative. Note that this does not necessarily mean that every two individuals which are related by a reflexive property are identical. The reflexive property is used frequently in partonomy: “ car is a part of a car” A property P is said to be reflexive when the property must relate individual a to itself. In Figure 4.25 we can see an example of this: using the property knows, an individual George must have a relationship to itself using the property knows. In other words, George must know herself. However, in addition, it is possible for George to know other people; therefore the individual George can have a relationship with individual Simon along the property knows.
If a property P is irreflexive, it can be described as a property that relates an individual a to individual b, where individual a and individual b are not the same. An example of this would be the property isMotherOf: an individual Alice can be related to individual Bob along the property isMotherOf, but Alice cannot be the mother of herself. Alice isMotherOf Bob Alice isMotherOf Alice From the OWL 2.0 Primer: Properties can furthermore be irreflexive, meaning that no individual can be related to itself by such a role. A typical example is the following which simply states that nobody can be his own parent.
Property chains are used to relate various categories the father of your father is your grandfather the wife of your brother is your sister-in-law the son of your sister is your nephew This works great for genealogies and I suspect that’s what it was created for. I’m certain there are other uses to – it seems like a convenient property. A property chain is similar to a functor . There's no need to understand functors in order to appreciate the value of this property, but the mathematical parallel is evident. How does this differ from a Transitive property? This is a similar but it involves an additional property being inferred (rather than an extension of the existing relationship). [] rdfs:subPropertyOf hasGrandfather; owl:propertyChain ( hasFather hasFather ). John III hasFather John JR John JR hasFather John SR John III hasGrandfather John SR Also of note this is not limited to just two triple as shown above. A property chain can be enacted over two or more triples triples. Whether a property be enacted over an existing property chain is something I'm not clear about myself. Is this scenario valid? hasGrandfather o hasFather = hasGreatGrandfather I’m not certain myself, but would appreciate insight. From the OWL 2.0 Primer: While the last example from the previous section implied the presence of an hasAncestor property whenever there is a chain of hasParent properties, we might want to be a bit more specific and define, say, a hasGrandparent property instead. Technically, this means that we want hasGrandparent to connect all individuals that are linked by a chain of exactly two hasParent properties. In contrast to the previous hasAncestor example, we do not want hasParent to be a special case of hasGrandparent nor do we want hasGrandparent to refer to great-grandparents etc
Synonyms can be described as both symmetrical and transitive. power on hasSynonym turn on turn on hasSynonym switch on power on hasSynonym switch on switch on hasSynonym turn on switch on hasSynonym power on turn on hasSynonym power on Using these two properties in conjuction on the hasSynonym predicate creates an explosion of implicit triples. Turn on” is set as a synonym for “Power on”, and “switch on” for “turn on”. Given the predicate properties that are checked here, all of these words are now synonyms of each other. Power on and Switch on have no direct relationship in the explict world, but are related symmetrically via turn on. Note that while a synonym is both transitive and symmetric, an acronym is neither. Digital Video Disc hasAcronym DVD Acronyms are typically not transitive (this would imply there was an acronym that represented an acronym). If the acronym was symmetric, this would the same as saying DVD hasAcronym Digital Video Disc Which would likewise be incorrect. It has been said that there are no exact synonyms in the English language; every variation has a subtle difference in meaning (perhaps given the origins of either Germanic-Saxon, Anglo-Norman or Latin). However, the predicate does not need to reflect this nuance, unless the modeler so chooses.
We could create a relationship called isRelation and define it as reflexive, transitive and symmetric. Gordon Jr isRelated Gordon Sr Gordon Sr isRelated Harold Gordon Jr isRelated Gordon Jr Gordon Jr isRelated Harold Harold isRelated Gordon Sr Hardold isRelated Gordon Jr Harold isRelated Harold Gordon Sr isRelated Gordon Sr Gordon Sr isRelated Gordon Jr