The document discusses using semantic diff as the basis for knowledge base versioning. It motivates the need for a versioning system to maintain different knowledge base versions over time, reason across versions, and determine how versions differ in meaning. The authors outline an approach using semantic diff, which highlights differences in logical meaning between knowledge bases, analogous to how file diff tools work for syntax. This would allow determining what statements hold in one version but not another.
On Action Theory Change: Semantics for Contraction and its PropertiesIvan Varzinczak
The document discusses action theory change and contracting action laws. It begins by motivating the need to change laws about the behavior of actions based on observations that contradict the current laws. It then outlines the topics to be covered, including preliminaries on action theories using multimodal logic, contracting action laws through semantics and algorithms, and properties of the contraction approach. The goal is to develop techniques for revising action theory laws in response to observations.
The document summarizes the notes from the IJCAI-09 Workshop on Automated Reasoning about Context and Ontology Evolution (ARCOE-09). The workshop was held on July 11-12, 2009 in Pasadena, California, USA as part of the International Joint Conference on Artificial Intelligence. It brought together researchers from areas related to knowledge representation and reasoning, contexts, and ontologies. The notes include abstracts from submitted works grouped into sections on common sense and non-monotonic reasoning, context and ontology, and automated ontology evolution.
The document discusses modularity in description logics ontologies. It proposes a more fine-grained approach to modularity where an ontology can be partitioned into sub-ontologies based on roles, with one module containing role-free axioms and other modules containing axioms for each individual role. The paper presents algorithms to check if an ontology is modular and help make it modular, and proves their correctness for a fragment of ALC. Benefits of modular ontologies include only needing to consider relevant modules when answering queries.
On the Revision of Action Laws: an Algorithmic ApproachIvan Varzinczak
This document discusses revising the laws that govern the behavior of actions in action theories. It presents an algorithmic approach to revising action laws based on observations that contradict the existing knowledge base. The document is presented at the NRAC'2009 conference and is divided into sections on preliminaries regarding action theories in multimodal logic, the semantics and algorithms for revising action laws, and a conclusion.
This document discusses modal logic and introduces the concept of pertinent entailment. It begins with an overview of modal logic, including defining a modal language with normal modal operators and standard semantics. It then discusses classes of models in modal logic that are constrained by additional axioms or properties, focusing on the class of reflexive models. The document outlines that it will next discuss the concept of pertinent entailment, infra-modal entailment, properties of pertinent entailment, and examples.
This document provides an overview of key physics concepts covered in Unit 2, including:
1) Velocity, acceleration, and graphs related to distance and time.
2) Forces such as friction and weight, and concepts like terminal velocity.
3) Energy, work, and transformations between different types of energy.
4) Static electricity, circuits, and electrical safety concepts.
5) Radioactive decay and atomic structure.
1) The document covers various topics in physics including distance-time graphs, velocity, acceleration, weight, forces, work, energy, electricity, atomic structure, radiation, and the universe.
2) Key concepts explained include the relationship between force, mass, and acceleration, calculating work and power, types of radiation and their properties, and the life cycle of stars ending in red giants, supernovae, neutron stars or black holes.
3) Safety issues around electricity, radiation, and nuclear processes are addressed.
This document covers several topics in biology including diet and exercise, pathogens, white blood cells, sense organs, the central nervous system, plant and animal hormones, testing medicines, adaptations, competition, environmental indicators, and genetic concepts like genes, chromosomes, DNA, variation, sexual and asexual reproduction, cloning, and genetic engineering. It provides information on these topics in a structured format with headings and subheadings.
On Action Theory Change: Semantics for Contraction and its PropertiesIvan Varzinczak
The document discusses action theory change and contracting action laws. It begins by motivating the need to change laws about the behavior of actions based on observations that contradict the current laws. It then outlines the topics to be covered, including preliminaries on action theories using multimodal logic, contracting action laws through semantics and algorithms, and properties of the contraction approach. The goal is to develop techniques for revising action theory laws in response to observations.
The document summarizes the notes from the IJCAI-09 Workshop on Automated Reasoning about Context and Ontology Evolution (ARCOE-09). The workshop was held on July 11-12, 2009 in Pasadena, California, USA as part of the International Joint Conference on Artificial Intelligence. It brought together researchers from areas related to knowledge representation and reasoning, contexts, and ontologies. The notes include abstracts from submitted works grouped into sections on common sense and non-monotonic reasoning, context and ontology, and automated ontology evolution.
The document discusses modularity in description logics ontologies. It proposes a more fine-grained approach to modularity where an ontology can be partitioned into sub-ontologies based on roles, with one module containing role-free axioms and other modules containing axioms for each individual role. The paper presents algorithms to check if an ontology is modular and help make it modular, and proves their correctness for a fragment of ALC. Benefits of modular ontologies include only needing to consider relevant modules when answering queries.
On the Revision of Action Laws: an Algorithmic ApproachIvan Varzinczak
This document discusses revising the laws that govern the behavior of actions in action theories. It presents an algorithmic approach to revising action laws based on observations that contradict the existing knowledge base. The document is presented at the NRAC'2009 conference and is divided into sections on preliminaries regarding action theories in multimodal logic, the semantics and algorithms for revising action laws, and a conclusion.
This document discusses modal logic and introduces the concept of pertinent entailment. It begins with an overview of modal logic, including defining a modal language with normal modal operators and standard semantics. It then discusses classes of models in modal logic that are constrained by additional axioms or properties, focusing on the class of reflexive models. The document outlines that it will next discuss the concept of pertinent entailment, infra-modal entailment, properties of pertinent entailment, and examples.
This document provides an overview of key physics concepts covered in Unit 2, including:
1) Velocity, acceleration, and graphs related to distance and time.
2) Forces such as friction and weight, and concepts like terminal velocity.
3) Energy, work, and transformations between different types of energy.
4) Static electricity, circuits, and electrical safety concepts.
5) Radioactive decay and atomic structure.
1) The document covers various topics in physics including distance-time graphs, velocity, acceleration, weight, forces, work, energy, electricity, atomic structure, radiation, and the universe.
2) Key concepts explained include the relationship between force, mass, and acceleration, calculating work and power, types of radiation and their properties, and the life cycle of stars ending in red giants, supernovae, neutron stars or black holes.
3) Safety issues around electricity, radiation, and nuclear processes are addressed.
This document covers several topics in biology including diet and exercise, pathogens, white blood cells, sense organs, the central nervous system, plant and animal hormones, testing medicines, adaptations, competition, environmental indicators, and genetic concepts like genes, chromosomes, DNA, variation, sexual and asexual reproduction, cloning, and genetic engineering. It provides information on these topics in a structured format with headings and subheadings.
The document discusses propositional Horn contraction, which involves changing an agent's beliefs in a logic that only includes Horn clauses rather than full propositional logic. It considers three types of contraction - entailment-based contraction, inconsistency-based contraction, and package contraction. It argues that the standard partial meet contraction approach is too strong for Horn logic. The goal is to define more appropriate basic contraction operations for these three types and characterize them using representation results. This research is motivated by applications in ontology reasoning, where correcting errors in a concept hierarchy can be viewed as a contraction problem.
On the Revision of Action Laws: An Algorithmic ApproachIvan Varzinczak
This document proposes an algorithmic approach for revising action laws in domain descriptions for reasoning about actions. It begins by introducing the motivation for revising action theories when new information is received. It then provides logical preliminaries on representing action theories using multimodal logic and defines various components of action theories including models, static laws, executability laws and supra-models. The document aims to answer how to semantically define revising an action theory by a new law, how to do this with minimal change, and how to syntactically revise an action theory accordingly.
This document summarizes a paper about changing action domain descriptions in dynamic logic. The paper revisits the semantics of action theory contraction and proposes new operators that express minimal change based on distance between models. It then defines syntactic contraction operators and establishes their correctness with respect to the semantics. Finally, it shows these operators satisfy standard postulates for theory change adopted in the literature.
Cohesion, Coupling and the Meta-theory of ActionsIvan Varzinczak
This document discusses adapting principles of software engineering design to the design and analysis of domain descriptions for reasoning about actions. Specifically, it explores how the informal concepts of cohesion and coupling from software engineering can provide criteria for evaluating domain description modules. Cohesion measures how related elements are within a module, while coupling measures interdependence between modules - both aim to minimize interactions and dependencies between modules. The document proposes organizing a domain description into modules for effects, non-effects, executabilities, inexecutabilities, and state constraints based on these software engineering principles.
This document discusses properties that a good domain description for reasoning about actions should have beyond mere consistency. It introduces the concept of modularity for action theories, where the different types of laws (static, effect, executability, inexecutability) are arranged in separate components with limited interaction. Violations of the proposed postulates about modularity can lead to unexpected conclusions from logically consistent theories. The document outlines algorithms to check whether an action theory satisfies the postulates of modularity.
The document discusses the problem of defining modular domain descriptions for reasoning about actions. It proposes three postulates for modularity:
1) No implicit executability or inexecutability laws - if a law can be inferred, it should be explicitly stated.
2) No implicit static laws - static laws should not be implicitly inferred from other laws.
3) Laws should not interfere with each other more than necessary - static laws can infer action laws but not vice versa.
It provides examples where existing domain descriptions violate these postulates by implicitly inferring laws. Algorithms are proposed to check for violations and suggest ways to repair domain descriptions to satisfy the postulates.
The document discusses elaborating domain descriptions expressed in dynamic logic. It defines a general method for contracting formulas in a version of propositional dynamic logic that solves the frame problem. The method presents the semantics of theory change and defines syntactic operators for contracting a domain description. It establishes the soundness and completeness of the operators with respect to the semantics for descriptions that satisfy a principle of modularity.
What Is a Good Domain Description? Evaluating and Revising Action Theories in...Ivan Varzinczak
This document appears to be a doctoral thesis submitted by Ivan Jos ́ Varzinczak in fulfillment of requirements for a Doctor of Artificial Intelligence degree. It discusses modularity in reasoning about actions and dynamic logic. The thesis was supervised by Andreas Herzig and defended on October 27, 2006 at the Universit ́ Paul Sabatier in Toulouse, France. It is written in French and includes typical thesis elements such as an acknowledgments section, table of contents, list of figures, and abstract. The document focuses on describing action theories, modular representations of actions, solving problems like the frame problem, and computing implicit laws.
This document summarizes research on encoding Reiter's solution to the frame problem in modal logic. Specifically, it presents a modal logic counterpart to Reiter's regression technique. The paper introduces a version of deterministic PDL with quantification over actions and equality. It then describes how Reiter's approach can be encoded in this logic by representing action preconditions, possible causes of state changes, and successor state axioms that enable regression. The paper claims this provides a way to perform reasoning about actions using a modal logic framework with computational advantages over the Situation Calculus.
The document discusses the concept of modularity in logical theories. It defines what it means for a theory to be modular and propositionally modular. A theory is modular if its consequences only depend on the part of the theory containing the same modal operators as the consequence. A theory is propositionally modular if its propositional consequences only depend on its propositional part. The paper proves that if a theory is propositionally modular, then it is modular, and discusses checking and ensuring the modular property of action theories.
The document discusses action theory contraction, which is the process of changing the laws that govern the behavior of actions in a knowledge base. It notes that observations of actions not having their expected effects, like buying something but not receiving the expected outcome, indicate a need to change the laws about how actions behave. The document outlines that it will cover preliminaries on action theories in dynamic logic, discuss semantic contraction of laws and relevant postulates, and draw a conclusion.
The document discusses first steps in contracting ontologies represented in the description logic EL. It introduces belief change operations like contraction and describes applying the AGM approach to EL. Contraction in EL is defined based on remainder sets and selection functions. Different types of contraction operations like partial meet, maxichoice and full meet contraction are defined based on how the selection function selects from the remainder sets. An example illustrates the definitions on contracting an axiom from an EL TBox.
What Is a Good Domain Description? Evaluating & Revising Action Theories in D...Ivan Varzinczak
The document discusses reasoning about actions and domains using logical formulas. It describes how to represent actions, their effects, executability, and domain constraints. The goal is to enable inference tasks like prediction, explanation, and planning. The document outlines decomposing action theories into modules to avoid unwanted conclusions and exploit logical modularity when evaluating and revising theories.
The document discusses approaches to Horn contraction, which is a type of belief change where clauses are removed from a Horn theory or belief set. It presents Delgrande's approach to entailment-based Horn contraction using Horn e-remainder sets. An example is used to illustrate maxichoice, full meet, and limitations of the partial meet construction. The concept of infra e-remainder sets is introduced to address these limitations and define a more general Horn contraction operator.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
The document discusses propositional Horn contraction, which involves changing an agent's beliefs in a logic that only includes Horn clauses rather than full propositional logic. It considers three types of contraction - entailment-based contraction, inconsistency-based contraction, and package contraction. It argues that the standard partial meet contraction approach is too strong for Horn logic. The goal is to define more appropriate basic contraction operations for these three types and characterize them using representation results. This research is motivated by applications in ontology reasoning, where correcting errors in a concept hierarchy can be viewed as a contraction problem.
On the Revision of Action Laws: An Algorithmic ApproachIvan Varzinczak
This document proposes an algorithmic approach for revising action laws in domain descriptions for reasoning about actions. It begins by introducing the motivation for revising action theories when new information is received. It then provides logical preliminaries on representing action theories using multimodal logic and defines various components of action theories including models, static laws, executability laws and supra-models. The document aims to answer how to semantically define revising an action theory by a new law, how to do this with minimal change, and how to syntactically revise an action theory accordingly.
This document summarizes a paper about changing action domain descriptions in dynamic logic. The paper revisits the semantics of action theory contraction and proposes new operators that express minimal change based on distance between models. It then defines syntactic contraction operators and establishes their correctness with respect to the semantics. Finally, it shows these operators satisfy standard postulates for theory change adopted in the literature.
Cohesion, Coupling and the Meta-theory of ActionsIvan Varzinczak
This document discusses adapting principles of software engineering design to the design and analysis of domain descriptions for reasoning about actions. Specifically, it explores how the informal concepts of cohesion and coupling from software engineering can provide criteria for evaluating domain description modules. Cohesion measures how related elements are within a module, while coupling measures interdependence between modules - both aim to minimize interactions and dependencies between modules. The document proposes organizing a domain description into modules for effects, non-effects, executabilities, inexecutabilities, and state constraints based on these software engineering principles.
This document discusses properties that a good domain description for reasoning about actions should have beyond mere consistency. It introduces the concept of modularity for action theories, where the different types of laws (static, effect, executability, inexecutability) are arranged in separate components with limited interaction. Violations of the proposed postulates about modularity can lead to unexpected conclusions from logically consistent theories. The document outlines algorithms to check whether an action theory satisfies the postulates of modularity.
The document discusses the problem of defining modular domain descriptions for reasoning about actions. It proposes three postulates for modularity:
1) No implicit executability or inexecutability laws - if a law can be inferred, it should be explicitly stated.
2) No implicit static laws - static laws should not be implicitly inferred from other laws.
3) Laws should not interfere with each other more than necessary - static laws can infer action laws but not vice versa.
It provides examples where existing domain descriptions violate these postulates by implicitly inferring laws. Algorithms are proposed to check for violations and suggest ways to repair domain descriptions to satisfy the postulates.
The document discusses elaborating domain descriptions expressed in dynamic logic. It defines a general method for contracting formulas in a version of propositional dynamic logic that solves the frame problem. The method presents the semantics of theory change and defines syntactic operators for contracting a domain description. It establishes the soundness and completeness of the operators with respect to the semantics for descriptions that satisfy a principle of modularity.
What Is a Good Domain Description? Evaluating and Revising Action Theories in...Ivan Varzinczak
This document appears to be a doctoral thesis submitted by Ivan Jos ́ Varzinczak in fulfillment of requirements for a Doctor of Artificial Intelligence degree. It discusses modularity in reasoning about actions and dynamic logic. The thesis was supervised by Andreas Herzig and defended on October 27, 2006 at the Universit ́ Paul Sabatier in Toulouse, France. It is written in French and includes typical thesis elements such as an acknowledgments section, table of contents, list of figures, and abstract. The document focuses on describing action theories, modular representations of actions, solving problems like the frame problem, and computing implicit laws.
This document summarizes research on encoding Reiter's solution to the frame problem in modal logic. Specifically, it presents a modal logic counterpart to Reiter's regression technique. The paper introduces a version of deterministic PDL with quantification over actions and equality. It then describes how Reiter's approach can be encoded in this logic by representing action preconditions, possible causes of state changes, and successor state axioms that enable regression. The paper claims this provides a way to perform reasoning about actions using a modal logic framework with computational advantages over the Situation Calculus.
The document discusses the concept of modularity in logical theories. It defines what it means for a theory to be modular and propositionally modular. A theory is modular if its consequences only depend on the part of the theory containing the same modal operators as the consequence. A theory is propositionally modular if its propositional consequences only depend on its propositional part. The paper proves that if a theory is propositionally modular, then it is modular, and discusses checking and ensuring the modular property of action theories.
The document discusses action theory contraction, which is the process of changing the laws that govern the behavior of actions in a knowledge base. It notes that observations of actions not having their expected effects, like buying something but not receiving the expected outcome, indicate a need to change the laws about how actions behave. The document outlines that it will cover preliminaries on action theories in dynamic logic, discuss semantic contraction of laws and relevant postulates, and draw a conclusion.
The document discusses first steps in contracting ontologies represented in the description logic EL. It introduces belief change operations like contraction and describes applying the AGM approach to EL. Contraction in EL is defined based on remainder sets and selection functions. Different types of contraction operations like partial meet, maxichoice and full meet contraction are defined based on how the selection function selects from the remainder sets. An example illustrates the definitions on contracting an axiom from an EL TBox.
What Is a Good Domain Description? Evaluating & Revising Action Theories in D...Ivan Varzinczak
The document discusses reasoning about actions and domains using logical formulas. It describes how to represent actions, their effects, executability, and domain constraints. The goal is to enable inference tasks like prediction, explanation, and planning. The document outlines decomposing action theories into modules to avoid unwanted conclusions and exploit logical modularity when evaluating and revising theories.
The document discusses approaches to Horn contraction, which is a type of belief change where clauses are removed from a Horn theory or belief set. It presents Delgrande's approach to entailment-based Horn contraction using Horn e-remainder sets. An example is used to illustrate maxichoice, full meet, and limitations of the partial meet construction. The concept of infra e-remainder sets is introduced to address these limitations and define a more general Horn contraction operator.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Full-RAG: A modern architecture for hyper-personalization
Semantic Diff as the Basis for Knowledge Base Versioning
1. Semantic Diff as the Basis for
Knowledge Base Versioning
Enrico Franconi1 Thomas Meyer2 Ivan Varzinczak2
1 Free University of Bozen/Bolzano 2 Meraka Institute, CSIR
Bolzano, Italy Pretoria, South Africa
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 1 / 24
2. Motivation
Knowledge Base
Ontology (DL, RDF)
Agents’ beliefs
Regulations or norms
...
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 2 / 24
3. Motivation
Knowledge Base
Ontology (DL, RDF)
K1
Agents’ beliefs
Regulations or norms
...
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 2 / 24
4. Motivation
Knowledge Base
Ontology (DL, RDF)
K1 K2
Agents’ beliefs
Regulations or norms
...
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 2 / 24
5. Motivation
K3
Knowledge Base
Ontology (DL, RDF)
K1 K2 K5
Agents’ beliefs
Regulations or norms
...
K4
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 2 / 24
7. Motivation
K3 ...
Knowledge Base
Ontology (DL, RDF)
K1 K2 K5 ...
Agents’ beliefs
Regulations or norms
...
K4 K6
Need for a versioning system
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 2 / 24
8. Motivation
Issues K6
Maintaining different versions
Parsimonious representation K5 K1
Reasoning with versions Kc
In which of the KBs does α hold,
K2
but not β?
K4
Difference between versions
K3
How they differ in meaning
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 3 / 24
9. Motivation
Issues K6
Maintaining different versions
Parsimonious representation K5 K1
Reasoning with versions Kc
In which of the KBs does α hold,
K2
but not β?
K4
Difference between versions
K3
How they differ in meaning
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 3 / 24
10. Motivation
Issues K6
Maintaining different versions
Parsimonious representation K5 K1
Reasoning with versions Kc
In which of the KBs does α hold,
K2
but not β?
K4
Difference between versions
K3
How they differ in meaning
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 3 / 24
11. Motivation
Issues K6
Maintaining different versions
Parsimonious representation K5 K1
Reasoning with versions Kc
In which of the KBs does α hold,
K2
but not β?
K4
Difference between versions
K3
How they differ in meaning
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 3 / 24
12. Outline
1 Logical Preliminaries
2 Knowledge Base Versioning
Semantic Diff
A General Framework
Compiled Representation
3 Conclusion
Contributions
Future Work
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 4 / 24
13. Outline
1 Logical Preliminaries
2 Knowledge Base Versioning
Semantic Diff
A General Framework
Compiled Representation
3 Conclusion
Contributions
Future Work
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 4 / 24
14. Outline
1 Logical Preliminaries
2 Knowledge Base Versioning
Semantic Diff
A General Framework
Compiled Representation
3 Conclusion
Contributions
Future Work
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 4 / 24
15. Logical Preliminaries
Knowledge bases
A knowledge base K is a (possibly infinite) set of formulas
Cn(K) = {α | K |= α}
Cn(.) is called Tarskian iff it satisfies
Inclusion: X ⊆ Cn(X )
Idempotence: Cn(Cn(X )) ⊆ Cn(X )
Monotonicity: X ⊆ Y implies Cn(X ) ⊆ Cn(Y )
[α] = {β | α ≡ β}
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 5 / 24
16. Logical Preliminaries
Knowledge bases
A knowledge base K is a (possibly infinite) set of formulas
Cn(K) = {α | K |= α}
Cn(.) is called Tarskian iff it satisfies
Inclusion: X ⊆ Cn(X )
Idempotence: Cn(Cn(X )) ⊆ Cn(X )
Monotonicity: X ⊆ Y implies Cn(X ) ⊆ Cn(Y )
[α] = {β | α ≡ β}
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 5 / 24
17. Logical Preliminaries
Knowledge bases
A knowledge base K is a (possibly infinite) set of formulas
Cn(K) = {α | K |= α}
Cn(.) is called Tarskian iff it satisfies
Inclusion: X ⊆ Cn(X )
Idempotence: Cn(Cn(X )) ⊆ Cn(X )
Monotonicity: X ⊆ Y implies Cn(X ) ⊆ Cn(Y )
[α] = {β | α ≡ β}
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 5 / 24
18. Logical Preliminaries
Knowledge bases
A knowledge base K is a (possibly infinite) set of formulas
Cn(K) = {α | K |= α}
Cn(.) is called Tarskian iff it satisfies
Inclusion: X ⊆ Cn(X )
Idempotence: Cn(Cn(X )) ⊆ Cn(X )
Monotonicity: X ⊆ Y implies Cn(X ) ⊆ Cn(Y )
[α] = {β | α ≡ β}
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 5 / 24
19. Outline
1 Logical Preliminaries
2 Knowledge Base Versioning
Semantic Diff
A General Framework
Compiled Representation
3 Conclusion
Contributions
Future Work
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 6 / 24
20. Semantic Diff
Difference in meaning between knowledge bases K and K
Analogy with the Unix diff command
diff distinguishes between syntactically different files
Semantic diff highlights the difference in (logical) meaning
Assume a logic with a Tarskian consequence relation
Example
Let the (propositional) knowledge bases:
K1 = {p, q} and K2 = {p, p → q}
K1 and K2 differ in syntax
But K1 and K2 convey the same meaning (K1 ≡ K2 )
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 7 / 24
21. Semantic Diff
Difference in meaning between knowledge bases K and K
Analogy with the Unix diff command
diff distinguishes between syntactically different files
Semantic diff highlights the difference in (logical) meaning
Assume a logic with a Tarskian consequence relation
Example
Let the (propositional) knowledge bases:
K1 = {p, q} and K2 = {p, p → q}
K1 and K2 differ in syntax
But K1 and K2 convey the same meaning (K1 ≡ K2 )
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 7 / 24
22. Semantic Diff
Difference in meaning between knowledge bases K and K
Analogy with the Unix diff command
diff distinguishes between syntactically different files
Semantic diff highlights the difference in (logical) meaning
Assume a logic with a Tarskian consequence relation
Example
Let the (propositional) knowledge bases:
K1 = {p, q} and K2 = {p, p → q}
K1 and K2 differ in syntax
But K1 and K2 convey the same meaning (K1 ≡ K2 )
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 7 / 24
23. Semantic Diff
Difference in meaning between knowledge bases K and K
Analogy with the Unix diff command
diff distinguishes between syntactically different files
Semantic diff highlights the difference in (logical) meaning
Assume a logic with a Tarskian consequence relation
Example
Let the (propositional) knowledge bases:
K1 = {p, q} and K2 = {p, p → q}
K1 and K2 differ in syntax
But K1 and K2 convey the same meaning (K1 ≡ K2 )
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 7 / 24
24. Characterizing Semantic Diff
KBs closed under logical consequence
(P1) K = Cn(K) and K = Cn(K )
Semantic diff of K and K : pair A, R
A is the add-set of (K, K )
R as the remove-set of (K, K )
(P2) K = (K ∪ A) R
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 8 / 24
25. Characterizing Semantic Diff
KBs closed under logical consequence
(P1) K = Cn(K) and K = Cn(K )
Semantic diff of K and K : pair A, R
A is the add-set of (K, K )
R as the remove-set of (K, K )
(P2) K = (K ∪ A) R
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 8 / 24
26. Characterizing Semantic Diff
KBs closed under logical consequence
(P1) K = Cn(K) and K = Cn(K )
Semantic diff of K and K : pair A, R
A is the add-set of (K, K )
R as the remove-set of (K, K )
(P2) K = (K ∪ A) R
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 8 / 24
27. Characterizing Semantic Diff
Minimal change and no redundancy
(P3) A ⊆ K
(P4) R ⊆ K
Duality of semantic diff
(P5) K = (K ∪ R) A
‘Undo’ operation when moving between versions
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 9 / 24
28. Characterizing Semantic Diff
Minimal change and no redundancy
(P3) A ⊆ K
(P4) R ⊆ K
Duality of semantic diff
(P5) K = (K ∪ R) A
‘Undo’ operation when moving between versions
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 9 / 24
29. Characterizing Semantic Diff
Minimal change and no redundancy
(P3) A ⊆ K
(P4) R ⊆ K
Duality of semantic diff
(P5) K = (K ∪ R) A
‘Undo’ operation when moving between versions
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 9 / 24
30. Characterizing Semantic Diff
Definition
K and K knowledge bases, A and R sets of sentences
A, R is semantic diff compliant w.r.t. (K, K ) iff (K, K ) and A, R
satisfy Postulates (P1)–(P5)
(P1) K = Cn(K) and K = Cn(K )
(P2) K = (K ∪ A) R
(P3) A ⊆ K
(P4) R ⊆ K
(P5) K = (K ∪ R) A
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 10 / 24
31. Characterizing Semantic Diff
Definition
K and K knowledge bases, A and R sets of sentences
A, R is semantic diff compliant w.r.t. (K, K ) iff (K, K ) and A, R
satisfy Postulates (P1)–(P5)
(P1) K = Cn(K) and K = Cn(K )
(P2) K = (K ∪ A) R
(P3) A ⊆ K
(P4) R ⊆ K
(P5) K = (K ∪ R) A
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 10 / 24
32. Characterizing Semantic Diff
Specific construction for the semantic diff operator:
Definition
The ideal semantic diff of (K, K ) is the pair A, R , where
A = K K and R = K K
Neither A nor R are logically closed:
Example
Let K = Cn(p ∧ q) and K = Cn(¬q)
A = {[¬q], [¬p ∨ ¬q]}
R = {[p ∧ q], [p], [q], [p ↔ q], [p ∨ q], [¬p ∨ q]}
p ∨ ¬q ∈ Cn(A), p ∨ ¬q ∈ Cn(R), but p ∨ ¬q ∈ A and p ∨ ¬q ∈ R
/ /
In fact, for any ideal semantic diff A, R , ∈ A and
/ ∈R
/
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 11 / 24
33. Characterizing Semantic Diff
Specific construction for the semantic diff operator:
Definition
The ideal semantic diff of (K, K ) is the pair A, R , where
A = K K and R = K K
Neither A nor R are logically closed:
Example
Let K = Cn(p ∧ q) and K = Cn(¬q)
A = {[¬q], [¬p ∨ ¬q]}
R = {[p ∧ q], [p], [q], [p ↔ q], [p ∨ q], [¬p ∨ q]}
p ∨ ¬q ∈ Cn(A), p ∨ ¬q ∈ Cn(R), but p ∨ ¬q ∈ A and p ∨ ¬q ∈ R
/ /
In fact, for any ideal semantic diff A, R , ∈ A and
/ ∈R
/
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 11 / 24
34. Characterizing Semantic Diff
Specific construction for the semantic diff operator:
Definition
The ideal semantic diff of (K, K ) is the pair A, R , where
A = K K and R = K K
Neither A nor R are logically closed:
Example
Let K = Cn(p ∧ q) and K = Cn(¬q)
A = {[¬q], [¬p ∨ ¬q]}
R = {[p ∧ q], [p], [q], [p ↔ q], [p ∨ q], [¬p ∨ q]}
p ∨ ¬q ∈ Cn(A), p ∨ ¬q ∈ Cn(R), but p ∨ ¬q ∈ A and p ∨ ¬q ∈ R
/ /
In fact, for any ideal semantic diff A, R , ∈ A and
/ ∈R
/
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 11 / 24
35. Characterizing Semantic Diff
Specific construction for the semantic diff operator:
Definition
The ideal semantic diff of (K, K ) is the pair A, R , where
A = K K and R = K K
Neither A nor R are logically closed:
Example
Let K = Cn(p ∧ q) and K = Cn(¬q)
A = {[¬q], [¬p ∨ ¬q]}
R = {[p ∧ q], [p], [q], [p ↔ q], [p ∨ q], [¬p ∨ q]}
p ∨ ¬q ∈ Cn(A), p ∨ ¬q ∈ Cn(R), but p ∨ ¬q ∈ A and p ∨ ¬q ∈ R
/ /
In fact, for any ideal semantic diff A, R , ∈ A and
/ ∈R
/
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 11 / 24
36. Characterizing Semantic Diff
Specific construction for the semantic diff operator:
Definition
The ideal semantic diff of (K, K ) is the pair A, R , where
A = K K and R = K K
Neither A nor R are logically closed:
Example
Let K = Cn(p ∧ q) and K = Cn(¬q)
A = {[¬q], [¬p ∨ ¬q]}
R = {[p ∧ q], [p], [q], [p ↔ q], [p ∨ q], [¬p ∨ q]}
p ∨ ¬q ∈ Cn(A), p ∨ ¬q ∈ Cn(R), but p ∨ ¬q ∈ A and p ∨ ¬q ∈ R
/ /
In fact, for any ideal semantic diff A, R , ∈ A and
/ ∈R
/
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 11 / 24
37. Characterizing Semantic Diff
There is a unique ideal semantic diff associated with any two KBs
Theorem
Let A, R be the ideal semantic diff of K and K . Then
A, R is semantic diff compliant with respect to (K, K )
A, R is unique w.r.t. (K, K )
Corollary
For the ideal semantic diff A, R of (K, K ), A ∩ R = ∅
Ideal semantic diff and symmetric difference: (K K) ∪ (K K )
Corollary
For the ideal semantic diff A, R of (K, K ), A, R = ∅, ∅ iff K = K
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 12 / 24
38. Characterizing Semantic Diff
There is a unique ideal semantic diff associated with any two KBs
Theorem
Let A, R be the ideal semantic diff of K and K . Then
A, R is semantic diff compliant with respect to (K, K )
A, R is unique w.r.t. (K, K )
Corollary
For the ideal semantic diff A, R of (K, K ), A ∩ R = ∅
Ideal semantic diff and symmetric difference: (K K) ∪ (K K )
Corollary
For the ideal semantic diff A, R of (K, K ), A, R = ∅, ∅ iff K = K
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 12 / 24
39. Characterizing Semantic Diff
There is a unique ideal semantic diff associated with any two KBs
Theorem
Let A, R be the ideal semantic diff of K and K . Then
A, R is semantic diff compliant with respect to (K, K )
A, R is unique w.r.t. (K, K )
Corollary
For the ideal semantic diff A, R of (K, K ), A ∩ R = ∅
Ideal semantic diff and symmetric difference: (K K) ∪ (K K )
Corollary
For the ideal semantic diff A, R of (K, K ), A, R = ∅, ∅ iff K = K
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 12 / 24
40. Characterizing Semantic Diff
There is a unique ideal semantic diff associated with any two KBs
Theorem
Let A, R be the ideal semantic diff of K and K . Then
A, R is semantic diff compliant with respect to (K, K )
A, R is unique w.r.t. (K, K )
Corollary
For the ideal semantic diff A, R of (K, K ), A ∩ R = ∅
Ideal semantic diff and symmetric difference: (K K) ∪ (K K )
Corollary
For the ideal semantic diff A, R of (K, K ), A, R = ∅, ∅ iff K = K
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 12 / 24
41. Outline
1 Logical Preliminaries
2 Knowledge Base Versioning
Semantic Diff
A General Framework
Compiled Representation
3 Conclusion
Contributions
Future Work
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 13 / 24
42. A Framework for Knowledge Base Versioning
Scenario:
n versions, K1 , . . . , Kn , of a KB that need to be stored
A core knowledge base Kc
For 1 ≤ i, j ≤ n:
Ideal semantic diff of (Ki , Kj ): Dij , Dji
Ideal semantic diff of (Kc , Ki ): Dci , Dic
From Properties
(P2) K = (K ∪ A) R
(P5) K = (K ∪ R) A
The add-set Dij of (Ki , Kj ) is also the remove-set of (Kj , Ki )
The remove-set Dji of (Ki , Kj ) is also the add-set of (Kj , Ki )
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 14 / 24
43. A Framework for Knowledge Base Versioning
Scenario:
n versions, K1 , . . . , Kn , of a KB that need to be stored
A core knowledge base Kc
For 1 ≤ i, j ≤ n:
Ideal semantic diff of (Ki , Kj ): Dij , Dji
Ideal semantic diff of (Kc , Ki ): Dci , Dic
From Properties
(P2) K = (K ∪ A) R
(P5) K = (K ∪ R) A
The add-set Dij of (Ki , Kj ) is also the remove-set of (Kj , Ki )
The remove-set Dji of (Ki , Kj ) is also the add-set of (Kj , Ki )
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 14 / 24
44. A Framework for Knowledge Base Versioning
Scenario:
n versions, K1 , . . . , Kn , of a KB that need to be stored
A core knowledge base Kc
For 1 ≤ i, j ≤ n:
Ideal semantic diff of (Ki , Kj ): Dij , Dji
Ideal semantic diff of (Kc , Ki ): Dci , Dic
From Properties
(P2) K = (K ∪ A) R
(P5) K = (K ∪ R) A
The add-set Dij of (Ki , Kj ) is also the remove-set of (Kj , Ki )
The remove-set Dji of (Ki , Kj ) is also the add-set of (Kj , Ki )
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 14 / 24
45. A Framework for Knowledge Base Versioning
In order to access any version, it is sufficient:
To store Kc , and
To store Dic and Dci for all Ki s.t. 1 ≤ i ≤ n
By Theorem 1, Ki = (Kc ∪ Dci ) Dic for every i s.t. 1 ≤ i ≤ n
Dc6 , D6c
•
Dc5 , D5c • • Dc1 , D1c
Kc
• Dc2 , D2c
Dc4 , D4c •
• Dc3 , D3c
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 15 / 24
46. A Framework for Knowledge Base Versioning
In order to access any version, it is sufficient:
To store Kc , and
To store Dic and Dci for all Ki s.t. 1 ≤ i ≤ n
By Theorem 1, Ki = (Kc ∪ Dci ) Dic for every i s.t. 1 ≤ i ≤ n
Dc6 , D6c
•
Dc5 , D5c • • Dc1 , D1c
Kc
• Dc2 , D2c
Dc4 , D4c •
• Dc3 , D3c
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 15 / 24
47. A Framework for Knowledge Base Versioning
In order to access any version, it is sufficient:
To store Kc , and
To store Dic and Dci for all Ki s.t. 1 ≤ i ≤ n
By Theorem 1, Ki = (Kc ∪ Dci ) Dic for every i s.t. 1 ≤ i ≤ n
Dc6 , D6c
•
Dc5 , D5c • • Dc1 , D1c
Kc
• Dc2 , D2c
Dc4 , D4c •
• Dc3 , D3c
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 15 / 24
48. A Framework for Knowledge Base Versioning
We can generate the ideal semantic diff of Ki and Kj
Proposition
Dij = (Dcj Dci ) ∪ (Dic Djc ) and Dji = (Dci Dcj ) ∪ (Djc Dic )
K1
Dn1 , D1n D1i , Di1
Dc1 , D1c
Kn Kc Dci , Dic Ki
Dcn , Dnc
Dcj , Djc
Dnj , Djn Dij , Dji
Kj
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 16 / 24
49. A Framework for Knowledge Base Versioning
We can generate the ideal semantic diff of Ki and Kj
Proposition
Dij = (Dcj Dci ) ∪ (Dic Djc ) and Dji = (Dci Dcj ) ∪ (Djc Dic )
K1
Dn1 , D1n D1i , Di1
Dc1 , D1c
Kn Kc Dci , Dic Ki
Dcn , Dnc
Dcj , Djc
Dnj , Djn Dij , Dji
Kj
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 16 / 24
50. Outline
1 Logical Preliminaries
2 Knowledge Base Versioning
Semantic Diff
A General Framework
Compiled Representation
3 Conclusion
Contributions
Future Work
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 17 / 24
51. Compiled Representation
Our characterization of Semantic Diff is in the knowledge level
Need for a compiled representation of KBs and the diffs
Computationally, a compiled format is required: F (K)
Given any representation of Ki and Kj , look for an intermediate
representation of the ideal semantic diff I (Dij ), I (Dji )
From Ki together with this intermediate representation of the ideal
semantic diff, generate Kj
From this intermediate representation generate the ideal semantic diff
Dij , Dji
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 18 / 24
52. Compiled Representation
Our characterization of Semantic Diff is in the knowledge level
Need for a compiled representation of KBs and the diffs
Computationally, a compiled format is required: F (K)
Given any representation of Ki and Kj , look for an intermediate
representation of the ideal semantic diff I (Dij ), I (Dji )
From Ki together with this intermediate representation of the ideal
semantic diff, generate Kj
From this intermediate representation generate the ideal semantic diff
Dij , Dji
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 18 / 24
53. Compiled Representation
Our characterization of Semantic Diff is in the knowledge level
Need for a compiled representation of KBs and the diffs
Computationally, a compiled format is required: F (K)
Given any representation of Ki and Kj , look for an intermediate
representation of the ideal semantic diff I (Dij ), I (Dji )
From Ki together with this intermediate representation of the ideal
semantic diff, generate Kj
From this intermediate representation generate the ideal semantic diff
Dij , Dji
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 18 / 24
54. Compiled Representation
Our characterization of Semantic Diff is in the knowledge level
Need for a compiled representation of KBs and the diffs
Computationally, a compiled format is required: F (K)
Given any representation of Ki and Kj , look for an intermediate
representation of the ideal semantic diff I (Dij ), I (Dji ) such that:
From Ki together with this intermediate representation of the ideal
semantic diff, generate Kj
From this intermediate representation generate the ideal semantic diff
Dij , Dji
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 18 / 24
55. Compiled Representation
Our characterization of Semantic Diff is in the knowledge level
Need for a compiled representation of KBs and the diffs
Computationally, a compiled format is required: F (K)
Given any representation of Ki and Kj , look for an intermediate
representation of the ideal semantic diff I (Dij ), I (Dji ) such that:
From Ki together with this intermediate representation of the ideal
semantic diff, generate Kj
From this intermediate representation generate the ideal semantic diff
Dij , Dji
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 18 / 24
56. Compiled Representation
With the intermediate representation
We can also generate one KB from another
Theorem
F (Ki ) = (F (Kj ) I (Dji )) ∪ I (Dij )
= (F (Kj ) ∪ I (Dij )) I (Dji )
We can generate the ideal diff (details in the NMR’10 paper)
We can get I (Dij ) and I (Dji )
Theorem
For 1 ≤ i, j ≤ n, I (Dij ) = (I (Dcj ) I (Dci )) ∪ (I (Dic ) I (Djc ))
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 19 / 24
57. Compiled Representation
With the intermediate representation
We can also generate one KB from another
Theorem
F (Ki ) = (F (Kj ) I (Dji )) ∪ I (Dij )
= (F (Kj ) ∪ I (Dij )) I (Dji )
We can generate the ideal diff (details in the NMR’10 paper)
We can get I (Dij ) and I (Dji )
Theorem
For 1 ≤ i, j ≤ n, I (Dij ) = (I (Dcj ) I (Dci )) ∪ (I (Dic ) I (Djc ))
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 19 / 24
58. Compiled Representation
With the intermediate representation
We can also generate one KB from another
Theorem
F (Ki ) = (F (Kj ) I (Dji )) ∪ I (Dij )
= (F (Kj ) ∪ I (Dij )) I (Dji )
We can generate the ideal diff (details in the NMR’10 paper)
We can get I (Dij ) and I (Dji )
Theorem
For 1 ≤ i, j ≤ n, I (Dij ) = (I (Dcj ) I (Dci )) ∪ (I (Dic ) I (Djc ))
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 19 / 24
59. Outline
1 Logical Preliminaries
2 Knowledge Base Versioning
Semantic Diff
A General Framework
Compiled Representation
3 Conclusion
Contributions
Future Work
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 20 / 24
60. Contributions
Groundwork for a semantic-driven notion of versioning
Intuitive, simple and general
Notion of semantic diff applicable to a large class of KR languages
Our results hold for any KB in a Tarskian logic
Parsimonious representation
Core KB: sufficient to reconstruct any of the versions
Diff between KBs: no direct access to any of the versions
This holds for any syntactic representation (see the NMR’10 paper)
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 21 / 24
61. Contributions
Groundwork for a semantic-driven notion of versioning
Intuitive, simple and general
Notion of semantic diff applicable to a large class of KR languages
Our results hold for any KB in a Tarskian logic
Parsimonious representation
Core KB: sufficient to reconstruct any of the versions
Diff between KBs: no direct access to any of the versions
This holds for any syntactic representation (see the NMR’10 paper)
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 21 / 24
62. Contributions
Groundwork for a semantic-driven notion of versioning
Intuitive, simple and general
Notion of semantic diff applicable to a large class of KR languages
Our results hold for any KB in a Tarskian logic
Parsimonious representation
Core KB: sufficient to reconstruct any of the versions
Diff between KBs: no direct access to any of the versions
This holds for any syntactic representation (see the NMR’10 paper)
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 21 / 24
63. Contributions
Groundwork for a semantic-driven notion of versioning
Intuitive, simple and general
Notion of semantic diff applicable to a large class of KR languages
Our results hold for any KB in a Tarskian logic
Parsimonious representation
Core KB: sufficient to reconstruct any of the versions
Diff between KBs: no direct access to any of the versions
This holds for any syntactic representation (see the NMR’10 paper)
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 21 / 24
64. Contributions
Groundwork for a semantic-driven notion of versioning
Intuitive, simple and general
Notion of semantic diff applicable to a large class of KR languages
Our results hold for any KB in a Tarskian logic
Parsimonious representation
Core KB: sufficient to reconstruct any of the versions
Diff between KBs: no direct access to any of the versions
This holds for any syntactic representation (see the NMR’10 paper)
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 21 / 24
65. Outline
1 Logical Preliminaries
2 Knowledge Base Versioning
Semantic Diff
A General Framework
Compiled Representation
3 Conclusion
Contributions
Future Work
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 22 / 24
66. Ongoing and Future Work
How to choose the core knowledge base Kc
Which normal forms are more appropriate
Experiments with realistic data for evaluation of the approach
Ontology versioning in Description Logics
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67. Reference
E. Franconi, T. Meyer, I. Varzinczak. Semantic Diff as the Basis for
Knowledge Base Versioning. Workshop on Nonmonotonic Reasoning
(NMR), 2010.
Thank you!
Franconi, Meyer, Varzinczak (FUB/Meraka) Semantic Diff for KB Versioning 24 / 24
68. Reference
E. Franconi, T. Meyer, I. Varzinczak. Semantic Diff as the Basis for
Knowledge Base Versioning. Workshop on Nonmonotonic Reasoning
(NMR), 2010.
Thank you!
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