The document discusses the role of ontologies in modern expert system development. It provides background on expert systems and ontologies, explaining that ontologies define domains of knowledge and are used to encapsulate domain knowledge for use in expert systems. The document outlines the process of developing ontologies, including identifying concepts and relationships in a domain. It also provides an example of an expert system called SINFERS that uses ontologies to select soil property prediction models.
This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
Extending and integrating a hybrid knowledge representation system into the c...Valentina Rho
Extending and integrating a hybrid knowledge representation system into the cognitive architecture ACT-R - 15th International Conference of the Italian Association for Artificial Intelligence - 1 December 2016
Artificial Intelligence is branch of computer science concerned with the study and creation of computer system that exhibits some form of intelligence.
This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
Extending and integrating a hybrid knowledge representation system into the c...Valentina Rho
Extending and integrating a hybrid knowledge representation system into the cognitive architecture ACT-R - 15th International Conference of the Italian Association for Artificial Intelligence - 1 December 2016
Artificial Intelligence is branch of computer science concerned with the study and creation of computer system that exhibits some form of intelligence.
Presentation on "Knowledge acquisition & validation"Aditya Sarkar
Presentation on "Knowledge acquisition and validation made and presented by Aditya Sarkar, I took the help of different sources available on internet to make all understand how a knowledge is acquired?. I hope this presentation will help everyone.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
We consider knowledge as a refined kind of information, more general than that found in convention databases. But it may be incomplete or fuzzy as well. We may think of knowledge as a collection of related facts, procedures, models and heuristics that can be used in problem solving or inference systems.[
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...The Integral Worm
This presentation is an introduction to Artificial Intelligence: The Nine Phases of the Expert System Development Lifecycle (ESDLC). Topics covered are the following: problem identification phase, feasibility study phase, project planning phase, knowledge acquisition phase, knowledge representation phase, knowledge implementation phase, verification and validation, installation/transition/ training, operation/evaluation/maintenance.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
Uncertainty classification of expert systems a rough set approachEr. rahul abhishek
In this paper, we discussed about the un certainity classifications of the Expert Systems using a Rough Set Approach. It is a Softcomputing technique using this we classified the types of Expert Systems. An expert system has a unique structure, different from traditional programs. It is divided into two parts, one fixed, independent of the expert system: the inference engine, and one variable: the knowledge base. To run an expert system, the engine reasons about the knowledge base like a human. In the 80's a third part appeared: a dialog interface to communicate with users. This ability to conduct a conversation with users was later called "conversational". Rough set theory is a technique deals with uncertainty.
Artificial Intelligence lecture notes. AI summarized notes for introduction to machine learning, symbol based and constructionist learning, also deep learning organized here for reading and may be for self-learning, I think.
Different Semantic Perspectives for Question Answering SystemsAndre Freitas
Question Answering systems define one of the most complex tasks in computational semantics. The intrinsic complexity of the QA task allows researchers of QA systems to investigate and explore different perspectives of semantics. However, this complexity also induces a bias towards a systems perspective, where researchers are alienated from a deeper reasoning on the semantic principles that are in place within the different components of the system. In this talk we will explore the semantic challenges, principles and perspectives behind the components of QA systems, aiming at providing a principled map and overview on the contribution of each component within the QA semantic interpretation goal.
Presentation on "Knowledge acquisition & validation"Aditya Sarkar
Presentation on "Knowledge acquisition and validation made and presented by Aditya Sarkar, I took the help of different sources available on internet to make all understand how a knowledge is acquired?. I hope this presentation will help everyone.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
We consider knowledge as a refined kind of information, more general than that found in convention databases. But it may be incomplete or fuzzy as well. We may think of knowledge as a collection of related facts, procedures, models and heuristics that can be used in problem solving or inference systems.[
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...The Integral Worm
This presentation is an introduction to Artificial Intelligence: The Nine Phases of the Expert System Development Lifecycle (ESDLC). Topics covered are the following: problem identification phase, feasibility study phase, project planning phase, knowledge acquisition phase, knowledge representation phase, knowledge implementation phase, verification and validation, installation/transition/ training, operation/evaluation/maintenance.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
Uncertainty classification of expert systems a rough set approachEr. rahul abhishek
In this paper, we discussed about the un certainity classifications of the Expert Systems using a Rough Set Approach. It is a Softcomputing technique using this we classified the types of Expert Systems. An expert system has a unique structure, different from traditional programs. It is divided into two parts, one fixed, independent of the expert system: the inference engine, and one variable: the knowledge base. To run an expert system, the engine reasons about the knowledge base like a human. In the 80's a third part appeared: a dialog interface to communicate with users. This ability to conduct a conversation with users was later called "conversational". Rough set theory is a technique deals with uncertainty.
Artificial Intelligence lecture notes. AI summarized notes for introduction to machine learning, symbol based and constructionist learning, also deep learning organized here for reading and may be for self-learning, I think.
Different Semantic Perspectives for Question Answering SystemsAndre Freitas
Question Answering systems define one of the most complex tasks in computational semantics. The intrinsic complexity of the QA task allows researchers of QA systems to investigate and explore different perspectives of semantics. However, this complexity also induces a bias towards a systems perspective, where researchers are alienated from a deeper reasoning on the semantic principles that are in place within the different components of the system. In this talk we will explore the semantic challenges, principles and perspectives behind the components of QA systems, aiming at providing a principled map and overview on the contribution of each component within the QA semantic interpretation goal.
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.
In this talk we will summarise some of the detectable trends on AI beyond deep learning. We will focus on the current transition from deep learning to deep semantics, describing the enabling infrastructures, challenges and opportunities in the construction of the next generation AI systems. The talk will focus on Natural Language Processing (NLP) as an AI sub-domain and will link to the research at the AI Systems Lab at the University of Manchester.
Effective Semantics for Engineering NLP SystemsAndre Freitas
Provide a synthesis of the emerging representation trends behind NLP systems.
Shift in perspective:
Effective engineering (task driven, scalable) instead of sound formalism.
Best-effort representation.
Knowledge Graphs (Frege revisited)
Information Extraction & Text Classification
Distributional Semantic Models
Knowledge Graphs & Distributional Semantics
(Distributional-Relational Models)
Applications of DRMs
KG Completion
Semantic Parsing
Natural Language Inference
Similar to The Role Of Ontology In Modern Expert Systems Dallas 2008 (20)
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.
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.
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
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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/
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.
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
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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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
8. The AI Value Proposition Why care about AI/ expert systems at all? By reasoning about information using applied knowledge, expert systems help stakeholders make timely and reliable decisions. => Modern businesses need to make complex decisions. 1 Complex decisions require lots of information and applied knowledge. 2 Such decisions must be made quickly and reliably. 3
9. Market Drivers & Enablers What is driving the apparent renaissance in AI? An incubator for new technologies and a source for new markets – a “killer-app”! 3 INTERNET Dramatic increases in CPU speed, RAM capacity, storage, etc. 1 Hardware Power Mass-production has lowered technology costs. 2 Hardware Cost
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12. Linking ES and Ontologies Ontology Ontologies disambiguate meaning. =
16. From the Engineer’s POV Our knowledge engineer would prefer that her project get recognition here… Rather than here.
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18. Parlez-Vous AI? “ Instances are the actual data in your knowledge base … If you have to make changes to your class or slot structure after instances have been entered, you may lose some information .” Anything odd about this? From a popular ontology editor help file…
19. Noise Defined as the universe of all possible invariants … … an endless sea of qualitative and quantitative values without a cognitive pattern .
20. Data Noise is filtered and sampled to separate useful measurements (facts) and to form data. Thus, in a sense, data is created via our cognitive attention.
21. Information Data is analyzed and interpreted, to uncover meaning and relationships , producing actionable information. Information aids decisions . x y
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23. Declarative Knowledge Rules We can represent them by asserting a fact or facts when certain other facts are present. IF fact(s) THEN assert fact(s) These represent the invariants that can be inferred when one or more invariants hold. Context Invariant(s) Invariants(s) =>
24. Procedural Knowledge Rules These represent the actions to take when certain invariants hold. Context We can represent them by calling functions when certain facts are present. IF fact(s) THEN call function foo Invariant(s) Actions(s) =>
25. Declarative vs. Procedural An example of declarative knowledge. IF (instance-of ?x THING) (composed-of ?x CLAY) (composed-of ?x SAND) (composed-of ?x SILT) THEN (instance-of ?x SOIL) THEN we can compute the soil property IF we have the soil property values An example of procedural knowledge.
26. Ontological Commitment “ An agreement to use a vocabulary (i.e., ask queries and make assertions) in a way that is consistent (but not complete) with respect to the theory specified by an ontology … An agent commits to an ontology if its observable actions are consistent with the definitions in the ontology.” – Tom Gruber
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28. The “Bottleneck” Issue Converting Expert Knowledge To Rules Elicitation How can they make each other understand what each knows?
29. Part I: Ontology Fundamentals The How, What, Where, When, and Why of Ontologies
30. Ontological Classes CLASS Represents a “thing” or concept. CLASS FACET 1 FACET m FACET An allowed value for a slot [optional]. SLOT 1 SLOT ( n – 1) SLOT n SLOT A data field within a class. Type is optional.
31. Classes vs. Instances organic_carbon symbol “_6A1” value 0.06 units “kg/kg” stdev 0.01 id 42 Soil Property symbol STRING value FLOAT units STRING stdev STRING id INTEGER
32. Specializing vs. Inheriting CLASS A CLASS B CLASS C Subclassing or extending a parent class, superclass, or base-class CLASS X CLASS Y CLASS Z Inheritance from one or more parent classes
33. Subclass / Inherit Examples Soil Acrisol Vertisol Acrisol and Vertisol are specializations of Soil. Horizon A Horizon B Horizon AB A soil horizon can inherit properties from distinct types.
38. Implied Ontologies (deftemplate soil-property "A fact describing a soil property“ (slot symbol) (slot value) (slot error) (slot units)) Using these templates, what could an expert system reason about implicitly? (deftemplate ptf "A fact describing a pedotransfer function" (slot symbol) (multislot args) (slot value) (slot units))
39. How do ontologies help? Ontologies Semantic Search (Web 2.0) Business Rules General Inferencing Rules Knowledge Layer Entity Relationship Diagrams SQL Queries Data Exchange Data Layer Design Layer OOP Object Models UML Diagrams
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43. Problem Solving Methods In “Preliminary Steps Towards a Taxonomy of Problem Solving Methods”, McDermott says… “… In traditional expert systems terminology, a problem-solving method is called an inference engine .” In our case, we are using a rule-engine as our inference engine. So, forward-chaining through rules is our problem-solving method.
45. Ontology Bifurcation Doing this allows developers to make explicit tradeoffs between reusability and granularity of the required domain knowledge. Separates out control and procedural knowledge. Domain Ontology Defines a domain’s set of terms and relations independent of any problem-solving method. Application Ontology Contains terms and relations unique to a particular problem-solving method. Ontology
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48. Part II: Ontology Creation How to design and build a domain ontology.
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54. Part IV: SINFERS Example Lessons Learned from the Soil Inferencing System (SINFERS) Project
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56. SINFERS Design Task PTF Rulebase Working Memory PTF Database Jess Rule Engine Given an initial set, { P } of n soil properties p * rule compute-ptf-p* IF { q }: { q } { P } PTF({q}) THEN compute p * and add to working memory p 1 p 2 p ( n -1) p n
57. SINFERS in Protégé Script Tab Lets you drive the Protégé API via your favorite script engine Jess Tab Lets you write and test Jess rules based on your ontological instances Queries Tab Used to define queries for looking up all instances matching some pattern Instances Tab Used to browse and edit current instances Forms Tab Automatically generates forms for populating the knowledgebase with instances Slots Tab Used to define and edit all slots through out the ontology Class Tab Used to define abstract and concrete ontological classes
61. Famous Ontology Folks Many of their colleagues and students are also great sources of ontology literature. Tom Gruber Deb McGuinness Dieter Fensel
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65. The entire archive of ontology papers used in this presentation is available as a ZIP Please ask me or James Owen if you’d like a copy.
66. Thank you all for your kind attention!! 15 Minutes for Questions
Editor's Notes
OPENING REMARKS ============= I’d like to thank James Owens for asking me to speak today – it is an honor and a privilege to present to all of you. Many of the topics that you all will be attending over these three days will be very specific, technical talks. Some will paint the future with a broad brush, others will detail the latest new thing. What I’m hoping that you will take away in the VERY short hour that I have with you is a sense of awareness and fundamental understanding of the most basic problem that you will face in building any kind of rule-based system. I am not pitching anything new or original – just a practical way to help alleviate that problem. Along the way, I will try to make the business case for this approach, but the talk will mostly be a high-level technical survey. At the risk of being cliché, it is true that people don’t plan to fail but rather they fail to plan. And we have all heard the statistics that some incredible percentage of software projects either fail outright or are at least challenged . While it is generally accepted that this is bad, I did see an article by Anthony Berglas on TheServerSide.com this month that argued that it was actually good for the economy. http://berglas.org/Articles/ImportantThatSoftwareFails/ImportantThatSoftwareFails.html In any case, for sake of argument, let’s grant that a disproportionately high percentage of software development projects fail outright. Common sense says that the more complex the project, the more opportunities there are for things to go wrong, hence the risk of failure is greater. Expert systems are arguably more complex than your average web app, therefore their risk of failure is commensurately higher. As developers and architects, we are approach this problem from the architectural and design perspectives. We’ll assume in our talk today that we have the authority to effect and enforce change at this level. So the questions is… how can you mitigate the risk of failure of your expert systems projects from an architectural and design perspective? Let’s move on…