The document discusses multi-perspective ontology engineering and outlines background, motivations, and contributions in the field. It presents a goal-aware ontology editor that uses an ontology of purposes to suggest ontologies for reuse based on matching purposes. The research aims to develop a vocabulary of ontology purposes by deriving one from a corpus and representing purposes formally in OWL.
Heterogeneity is Here to Stay and Semantics is Not About Agreementkjanowicz
The data retrieval problem is real as major data hubs still rely on keyword search with unreliable metadata. Finding data fit for a specific purpose is difficult due to heterogeneity caused by cultural, scientific, and granular differences that will remain. Semantics helps address this by making data provider meanings explicit through ontologies rather than focusing on agreement over term meanings. Ensuring meaningful analysis and combination of heterogeneous data sources is non-trivial and requires smart data approaches that provide information on intended data use.
AAG 2014 Talk on Ontology Views, Reusue, Alignmentkjanowicz
This document discusses challenges with linking and querying data from multiple sources using ontologies and vocabularies. It describes issues with reusing, aligning, and modeling ontologies and vocabularies. Examples show difficulties with ontology and vocabulary alignment due to differences in concepts and logical definitions. The document advocates addressing these challenges through semantic shortcuts and views that bridge the gaps between ontologies and linked data.
Why the Data Train Needs Semantic Rails -- The Case of Linked Scientometrics ...kjanowicz
This document discusses the need for semantics and linked data in analyzing large datasets like those found in scientometrics. It argues that semantics can help address issues with data retrieval, sensemaking, and interoperability by providing metadata, unique identifiers, and standardized models. The document also examines keyword trends and citation patterns relating to semantic web research to analyze how the field is evolving. It concludes that both inductive and deductive techniques are needed to fully understand complex topics like whether the semantic web is disappearing, diversifying, or radiating into new areas.
The document discusses ontology alignment representation. It outlines three levels of knowledge abstraction: the ground level uses SPARQL for instance transformation, the intermediate level uses the Alignment format and Ontology, and the top level uses correspondence patterns. It provides examples of using SPARQL and its extensions to represent mappings between ontologies at the ground level.
1) The document discusses using semantic web technologies like linked data for scientometrics research questions. It provides examples of simple, boring, and interesting scientometrics questions.
2) Limitations of current approaches are described, such as data retrieval, sensemaking, and interoperability issues. The argument for "smart data" over smart applications is also made.
3) Examples are given of linked data driven scientometrics installations, including analyzing keyword trends and mapping institutional publication locations. This helps address whether fields like semantic web are growing or diversifying.
4) Challenges remain around data enrichment, conflation, and developing richer ontologies beyond academic publishing. Stronger conceptual models and approaches are needed to
A Method for Reusing and Re-engineering Non-ontological Resources for Buildin...Boris Villazón-Terrazas
To speed up the ontology development by reusing and re-engineering
non-ontological resources that have already reached some consensus by standardization bodies.
This document provides an overview of Debopriyo Roy's research portfolio from 2011. It outlines his areas of focus which include document design practices, procedural visual design, usability testing processes, statistical analysis of web interactions, cognitive and behavioral frameworks, online collaboration/interface design, the technical writing market in India, and the tools/interfaces used for research. It also lists selected publications, research projects/funding, accomplishments, and research initiatives.
Heterogeneity is Here to Stay and Semantics is Not About Agreementkjanowicz
The data retrieval problem is real as major data hubs still rely on keyword search with unreliable metadata. Finding data fit for a specific purpose is difficult due to heterogeneity caused by cultural, scientific, and granular differences that will remain. Semantics helps address this by making data provider meanings explicit through ontologies rather than focusing on agreement over term meanings. Ensuring meaningful analysis and combination of heterogeneous data sources is non-trivial and requires smart data approaches that provide information on intended data use.
AAG 2014 Talk on Ontology Views, Reusue, Alignmentkjanowicz
This document discusses challenges with linking and querying data from multiple sources using ontologies and vocabularies. It describes issues with reusing, aligning, and modeling ontologies and vocabularies. Examples show difficulties with ontology and vocabulary alignment due to differences in concepts and logical definitions. The document advocates addressing these challenges through semantic shortcuts and views that bridge the gaps between ontologies and linked data.
Why the Data Train Needs Semantic Rails -- The Case of Linked Scientometrics ...kjanowicz
This document discusses the need for semantics and linked data in analyzing large datasets like those found in scientometrics. It argues that semantics can help address issues with data retrieval, sensemaking, and interoperability by providing metadata, unique identifiers, and standardized models. The document also examines keyword trends and citation patterns relating to semantic web research to analyze how the field is evolving. It concludes that both inductive and deductive techniques are needed to fully understand complex topics like whether the semantic web is disappearing, diversifying, or radiating into new areas.
The document discusses ontology alignment representation. It outlines three levels of knowledge abstraction: the ground level uses SPARQL for instance transformation, the intermediate level uses the Alignment format and Ontology, and the top level uses correspondence patterns. It provides examples of using SPARQL and its extensions to represent mappings between ontologies at the ground level.
1) The document discusses using semantic web technologies like linked data for scientometrics research questions. It provides examples of simple, boring, and interesting scientometrics questions.
2) Limitations of current approaches are described, such as data retrieval, sensemaking, and interoperability issues. The argument for "smart data" over smart applications is also made.
3) Examples are given of linked data driven scientometrics installations, including analyzing keyword trends and mapping institutional publication locations. This helps address whether fields like semantic web are growing or diversifying.
4) Challenges remain around data enrichment, conflation, and developing richer ontologies beyond academic publishing. Stronger conceptual models and approaches are needed to
A Method for Reusing and Re-engineering Non-ontological Resources for Buildin...Boris Villazón-Terrazas
To speed up the ontology development by reusing and re-engineering
non-ontological resources that have already reached some consensus by standardization bodies.
This document provides an overview of Debopriyo Roy's research portfolio from 2011. It outlines his areas of focus which include document design practices, procedural visual design, usability testing processes, statistical analysis of web interactions, cognitive and behavioral frameworks, online collaboration/interface design, the technical writing market in India, and the tools/interfaces used for research. It also lists selected publications, research projects/funding, accomplishments, and research initiatives.
Applying NLP (natural language processing) to the patent genreUny Cao
Patents are different from text (web pages, news stories, etc), or research papers or other genres. It is necessary to adapt NLP (natural language processing) tools in order to fulfill tasks arising from the life cycle of patents as documents, patents as core elements to product development, as well as patents as financial assets.
The Distributed Ontology Language (DOL): Use Cases, Syntax, and ExtensibilityChristoph Lange
The document discusses the Distributed Ontology Language (DOL) which aims to support semantic integration and interoperability across heterogeneous ontologies. DOL allows for logically heterogeneous ontologies, modular ontologies, and formal and informal links between ontologies. It has a formal semantics and can be serialized in XML, RDF, and text. Examples of applications that could benefit from DOL include an ontology repository engine and a multilingual map user interface driven by aligned ontologies.
Ontology Building and its Application using HozoKouji Kozaki
The document provides information about an upcoming tutorial on ontology building and its applications using the Hozo ontology development tool. The tutorial will take place on November 9th, 2014 in Chiang Mai, Thailand and will cover how to build ontologies using Hozo, some characteristic functions of Hozo, and examples of ontology-based application developments. The tutorial agenda outlines the topics to be covered in each time block, including hands-on experience building ontologies with Hozo.
Neural Text Embeddings for Information Retrieval (WSDM 2017)Bhaskar Mitra
The document describes a tutorial on using neural networks for information retrieval. It discusses an agenda for the tutorial that includes fundamentals of IR, word embeddings, using word embeddings for IR, deep neural networks, and applications of neural networks to IR problems. It provides context on the increasing use of neural methods in IR applications and research.
Unfolding Data - Interaction Design for Visualizations of Geospatial DataTill Nagel
This document summarizes a presentation on interaction design for visualizations of geospatial data. It discusses projects at FH Potsdam involving interface design, cartography for broad audiences, natural user interfaces, and geo visualization design patterns. Specific projects are described, including Virtual Water, Fritzing, a tsunami early warning system, neogeography tools like Flickr Maps, and data visualization tools like Eigenfactor maps. It also covers tangible interfaces like mæve and Venice Unfolding for exploring architectural and urban planning data, as well as the Muse tool for visualizing scientific collaboration networks.
Pal gov.tutorial4.session1 2.whatisontologyMustafa Jarrar
This document provides an overview of an ontology engineering tutorial being conducted as part of the PalGov project. It includes information about the project consortium and funding, a tutorial map outlining the sessions and their learning objectives, and background information about ontologies. Specifically, it defines an ontology as an explicit specification of a conceptualization written in logic as a set of axioms. It distinguishes between ontologies and conceptual data schemas by noting that ontologies provide meaning for concepts and relations in addition to structure.
This document provides an overview of the ontology development process including the following key steps: requirements definition, term extraction, ontology conceptualization, initial and detailed model drafting, ontology implementation, non-ontological resource transformation, and ontology evaluation. It discusses considerations for each step such as tools, focus, and best practices.
This document provides an outline for a tutorial on ontology engineering and lexical semantics. The tutorial aims to teach participants how to build ontologies, tackle challenges in ontology engineering, and develop multilingual ontologies. It will include sessions on population ontologies, bank customer ontologies, legal person ontologies, ontology tools, and using existing linguistic ontologies like WordNets. Participants will learn about the Palestinian eGovernment interoperability framework called Zinnar and how to use ontologies in web services. The goal is to help participants gain knowledge and skills in ontology engineering, multilingual knowledge representation, and applying ontologies in eGovernment systems.
Analyzing and Ranking Multimedia Ontologies for their ReuseEURECOM
The document discusses analyzing and ranking multimedia ontologies for reuse in developing a new multimedia ontology called M3. It outlines the state of the art in existing multimedia ontologies, including those describing multimedia objects, shapes and images, visual resources, audio and music. The goal is to search, assess and select suitable existing ontologies to reuse in M3 based on the NeOn methodology guidelines for ontology reuse.
The document discusses challenges with ontology engineering including reuse, axiomatization, and alignment. It proposes using ontology design patterns and modular, local ontologies to address issues of heterogeneity and avoid overly abstract conceptualizations. Pattern-based ontology engineering aims to mine ontological primitives from data and assist domain experts while deferring heavy ontological commitments.
The document discusses the basics of ontologies, including their origin in philosophy, definitions, types, benefits and application areas. Some key points are:
- An ontology is a formal specification of a conceptualization used to help humans and programs share knowledge. It establishes a shared vocabulary for exchanging information.
- Ontologies describe domain knowledge and provide an agreed-upon understanding of a domain through concepts and relations. They help solve problems of ambiguity and enable knowledge sharing.
- Ontologies benefit applications like information retrieval, digital libraries, knowledge engineering and natural language processing by facilitating semantic search and integration of data.
This document summarizes a talk given by Pär-Ola Zander on the relationship between participatory design and development research. Some key points of similarity and difference are discussed. Both fields employ qualitative methods and iterative design processes. However, development research typically operates on a larger scale and scope, with methods often used as practitioner tools. Participatory design also maintains more control over research processes. Overall, both fields could learn from each other, with development research exposing new forms of participation and participatory design emphasizing reflection.
Smart Specifications - On the Move to Ontology-Supported Requirements Enginee...Advanced-Concepts-Team
Requirements Engineering deals with the definition, documentation and maintenance of
requirements in systems and software engineering.
It is one of the most crucial areas in systems development, as mistakes introduced
during the requirements elicitation often result in faulty systems or systems that do
not comply with the stakeholder needs. Typical problems are, e.g., missing requirements
(the stakeholders were not aware of them or did not communicate them), ambiguously
formulated or competing requirements. To correct errors introduced in the requirements
later during the development process, or even after delivery, is costly and not always
possible. This is a high risk, especially in safety critical domains such as aerospace
engineering. Requirements engineering tasks are in their quintessence knowledge management
tasks. Knowledge about the system-to-be and its
application domain is gathered from many stakeholders and other sources and captured in
the requirements. Documentation of requirements
and later management tasks, such as change management, require not only domain knowledge,
but also knowledge about requirements engineering practices. The utilisation of ontologies
to formalise this knowledge seems promising, as these are especially suited to support
machine reasoning and semantic interoperability for consistency and completeness checks
The document discusses socio-technical patterns for collaboration in e-research. It introduces the concept of pattern languages from architecture that capture recurring problems and solutions. Several patterns are identified as potentially relevant for e-research collaboration, including community of communities, reality check, radical co-location, small successes early, and support conversation at boundaries. The document argues that patterns can help identify, formulate, and prevent problems in e-research collaboration.
The document discusses using inductive logic programming (ILP) to perform information extraction (IE) on biomedical texts. It outlines applying ILP to learn recursive theories from annotated examples to fill slots in templates and extract entities. The learning strategy involves searching for theories for each concept in parallel before discovering dependencies between concepts. Text processing involves tokenization, POS tagging and domain dictionaries before description generation for ILP.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Applying NLP (natural language processing) to the patent genreUny Cao
Patents are different from text (web pages, news stories, etc), or research papers or other genres. It is necessary to adapt NLP (natural language processing) tools in order to fulfill tasks arising from the life cycle of patents as documents, patents as core elements to product development, as well as patents as financial assets.
The Distributed Ontology Language (DOL): Use Cases, Syntax, and ExtensibilityChristoph Lange
The document discusses the Distributed Ontology Language (DOL) which aims to support semantic integration and interoperability across heterogeneous ontologies. DOL allows for logically heterogeneous ontologies, modular ontologies, and formal and informal links between ontologies. It has a formal semantics and can be serialized in XML, RDF, and text. Examples of applications that could benefit from DOL include an ontology repository engine and a multilingual map user interface driven by aligned ontologies.
Ontology Building and its Application using HozoKouji Kozaki
The document provides information about an upcoming tutorial on ontology building and its applications using the Hozo ontology development tool. The tutorial will take place on November 9th, 2014 in Chiang Mai, Thailand and will cover how to build ontologies using Hozo, some characteristic functions of Hozo, and examples of ontology-based application developments. The tutorial agenda outlines the topics to be covered in each time block, including hands-on experience building ontologies with Hozo.
Neural Text Embeddings for Information Retrieval (WSDM 2017)Bhaskar Mitra
The document describes a tutorial on using neural networks for information retrieval. It discusses an agenda for the tutorial that includes fundamentals of IR, word embeddings, using word embeddings for IR, deep neural networks, and applications of neural networks to IR problems. It provides context on the increasing use of neural methods in IR applications and research.
Unfolding Data - Interaction Design for Visualizations of Geospatial DataTill Nagel
This document summarizes a presentation on interaction design for visualizations of geospatial data. It discusses projects at FH Potsdam involving interface design, cartography for broad audiences, natural user interfaces, and geo visualization design patterns. Specific projects are described, including Virtual Water, Fritzing, a tsunami early warning system, neogeography tools like Flickr Maps, and data visualization tools like Eigenfactor maps. It also covers tangible interfaces like mæve and Venice Unfolding for exploring architectural and urban planning data, as well as the Muse tool for visualizing scientific collaboration networks.
Pal gov.tutorial4.session1 2.whatisontologyMustafa Jarrar
This document provides an overview of an ontology engineering tutorial being conducted as part of the PalGov project. It includes information about the project consortium and funding, a tutorial map outlining the sessions and their learning objectives, and background information about ontologies. Specifically, it defines an ontology as an explicit specification of a conceptualization written in logic as a set of axioms. It distinguishes between ontologies and conceptual data schemas by noting that ontologies provide meaning for concepts and relations in addition to structure.
This document provides an overview of the ontology development process including the following key steps: requirements definition, term extraction, ontology conceptualization, initial and detailed model drafting, ontology implementation, non-ontological resource transformation, and ontology evaluation. It discusses considerations for each step such as tools, focus, and best practices.
This document provides an outline for a tutorial on ontology engineering and lexical semantics. The tutorial aims to teach participants how to build ontologies, tackle challenges in ontology engineering, and develop multilingual ontologies. It will include sessions on population ontologies, bank customer ontologies, legal person ontologies, ontology tools, and using existing linguistic ontologies like WordNets. Participants will learn about the Palestinian eGovernment interoperability framework called Zinnar and how to use ontologies in web services. The goal is to help participants gain knowledge and skills in ontology engineering, multilingual knowledge representation, and applying ontologies in eGovernment systems.
Analyzing and Ranking Multimedia Ontologies for their ReuseEURECOM
The document discusses analyzing and ranking multimedia ontologies for reuse in developing a new multimedia ontology called M3. It outlines the state of the art in existing multimedia ontologies, including those describing multimedia objects, shapes and images, visual resources, audio and music. The goal is to search, assess and select suitable existing ontologies to reuse in M3 based on the NeOn methodology guidelines for ontology reuse.
The document discusses challenges with ontology engineering including reuse, axiomatization, and alignment. It proposes using ontology design patterns and modular, local ontologies to address issues of heterogeneity and avoid overly abstract conceptualizations. Pattern-based ontology engineering aims to mine ontological primitives from data and assist domain experts while deferring heavy ontological commitments.
The document discusses the basics of ontologies, including their origin in philosophy, definitions, types, benefits and application areas. Some key points are:
- An ontology is a formal specification of a conceptualization used to help humans and programs share knowledge. It establishes a shared vocabulary for exchanging information.
- Ontologies describe domain knowledge and provide an agreed-upon understanding of a domain through concepts and relations. They help solve problems of ambiguity and enable knowledge sharing.
- Ontologies benefit applications like information retrieval, digital libraries, knowledge engineering and natural language processing by facilitating semantic search and integration of data.
This document summarizes a talk given by Pär-Ola Zander on the relationship between participatory design and development research. Some key points of similarity and difference are discussed. Both fields employ qualitative methods and iterative design processes. However, development research typically operates on a larger scale and scope, with methods often used as practitioner tools. Participatory design also maintains more control over research processes. Overall, both fields could learn from each other, with development research exposing new forms of participation and participatory design emphasizing reflection.
Smart Specifications - On the Move to Ontology-Supported Requirements Enginee...Advanced-Concepts-Team
Requirements Engineering deals with the definition, documentation and maintenance of
requirements in systems and software engineering.
It is one of the most crucial areas in systems development, as mistakes introduced
during the requirements elicitation often result in faulty systems or systems that do
not comply with the stakeholder needs. Typical problems are, e.g., missing requirements
(the stakeholders were not aware of them or did not communicate them), ambiguously
formulated or competing requirements. To correct errors introduced in the requirements
later during the development process, or even after delivery, is costly and not always
possible. This is a high risk, especially in safety critical domains such as aerospace
engineering. Requirements engineering tasks are in their quintessence knowledge management
tasks. Knowledge about the system-to-be and its
application domain is gathered from many stakeholders and other sources and captured in
the requirements. Documentation of requirements
and later management tasks, such as change management, require not only domain knowledge,
but also knowledge about requirements engineering practices. The utilisation of ontologies
to formalise this knowledge seems promising, as these are especially suited to support
machine reasoning and semantic interoperability for consistency and completeness checks
The document discusses socio-technical patterns for collaboration in e-research. It introduces the concept of pattern languages from architecture that capture recurring problems and solutions. Several patterns are identified as potentially relevant for e-research collaboration, including community of communities, reality check, radical co-location, small successes early, and support conversation at boundaries. The document argues that patterns can help identify, formulate, and prevent problems in e-research collaboration.
The document discusses using inductive logic programming (ILP) to perform information extraction (IE) on biomedical texts. It outlines applying ILP to learn recursive theories from annotated examples to fill slots in templates and extract entities. The learning strategy involves searching for theories for each concept in parallel before discovering dependencies between concepts. Text processing involves tokenization, POS tagging and domain dictionaries before description generation for ILP.
Similar to Multi perspective Ontology Engineering (20)
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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.
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.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
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.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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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.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Multi perspective Ontology Engineering
1. Background and Motivation
Contributions
Multi-Perspective Ontology Engineering
R. Denaux1 A. G. Cohn1 V. Dimitrova1 G. Hart2
1 School of Computing
University of Leeds
2 Ordnance Survey Research
Invited Talk at Department of Computer Science
Sheffield, 2010
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
2. Background and Motivation
Contributions
Outline
1 Background and Motivation
Multi-perspective Ontology Engineering
Ontology Purposes
2 Contributions
Goal-aware Ontology Editor
Ontology Purpose Vocabulary
Formalisation
Current Work
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
3. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Outline
1 Background and Motivation
Multi-perspective Ontology Engineering
Ontology Purposes
2 Contributions
Goal-aware Ontology Editor
Ontology Purpose Vocabulary
Formalisation
Current Work
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
4. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
5. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
6. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
7. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
8. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
9. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
10. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
11. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
12. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
13. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
14. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Context of Research
Multi-perspective Ontology Engineering.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
15. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
ROO: Rabbit to OWL Ontology Authoring.
Example of adapting to ontology contributors
Domain experts:
Good knowledge of the domain to be represented
Limited or no Ontology Engineering experience
Limited or no knowledge of OWL, Protégé, etc.
ROO provides tool support for domain experts:
Guidance through ontology construction methodology
Controlled Natural Language interface
No OWL specific terminology
Adaptation at design time, not at runtime
Re-use techniques from User Modelling and
Personalisation
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
16. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Outline
1 Background and Motivation
Multi-perspective Ontology Engineering
Ontology Purposes
2 Contributions
Goal-aware Ontology Editor
Ontology Purpose Vocabulary
Formalisation
Current Work
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
17. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
Ontology Development 101
Natalya F. Noy and Deborah L. McGuinness
There is no one correct way to model a domain there
are always viable alternatives. The best solution
almost always depends on the application that you
have in mind and the extensions that you anticipate.
. . . deciding what we are going to use the ontology for
. . . will guide many of the modeling decisions down the
road.1
1
N. F. Noy and D. Mcguinness. Ontology development 101: A guide to
creating your first ontology, 2000
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
18. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
METHONTOLOGY
The goal of the specification phase is to produce
either an informal, semi-formal or formal ontology
specification document. METHONTOLOGY proposes
that at least the following information be included:
(a) The purpose of the ontology, including its
intended uses, scenarios of use, end-users, etc.
... 2
2
M. Fernandez-Lopez, A. Gomez-Perez, and N. Juristo. Methontology:
from ontological art towards ontological engineering. In Proceedings of the
AAAI97 Spring Symposium Series on Ontological Engineering, pages 33–40,
1997
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
19. Background and Motivation Multi-perspective Ontology Engineering
Contributions Ontology Purposes
DILIGENT
Local adaptation: once the core ontology is available,
users work with it and adapt it locally to their own
needs. Typically, they will have their own business
requirements and correspondingly change their local
ontologies. 3
3
Denny Vrandecic, H. Sofia Pinto, York Sure, and Christoph Tempich. The
diligent knowledge processes. Journal of Knowledge Management,
9(5):85–96, OCT 2005
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
20. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Outline
1 Background and Motivation
Multi-perspective Ontology Engineering
Ontology Purposes
2 Contributions
Goal-aware Ontology Editor
Ontology Purpose Vocabulary
Formalisation
Current Work
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
21. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Purpose-driven Adaptive Ontology Reuse.
Derive model of Ontology Purposes
Use this model to capture new ontology purposes
Suggest ontology to reuse based on matching ontology
purpose
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
22. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Goal-aware Ontology Editor
Use case: Ontology Re-use
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
23. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Goal-aware Ontology Editor
Use case: Ontology Re-use
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
24. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Goal-aware Ontology Editor
Use case: Ontology Re-use
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
25. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Goal-aware Ontology Editor
Use case: Ontology Re-use
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
26. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Outline
1 Background and Motivation
Multi-perspective Ontology Engineering
Ontology Purposes
2 Contributions
Goal-aware Ontology Editor
Ontology Purpose Vocabulary
Formalisation
Current Work
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
27. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Purpose Example
ontology: Ordnance Survey Hydrology Ontology v2
source: ontology annotation
free text: "Purpose: To describe in an unambiguous
manner the inland hydrology feature classes surveyed by
Ordnance Survey with the intention of improving the use of
the surveyed data by our customers and enabling
semi-automatic processing of these data."
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
28. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Representing Ontology Purposes.
Deriving Vocabulary from a corpus
4
4
Ronald Denaux, Anthony G. Cohn, Vania Dimitrova, and Glen Hart.
Towards modelling the intended purpose of ontologies: A case study in
geography. In Proceedings of the Terra Cognita Workshop, collocated with
the 8th International Semantic Web Conference (ISWC-2009), volume 518.
CEUR-WS, 2009
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
29. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Purpose Categories
Domain Defining
Ontology Process Related
Data Process Related
Investigative
Collaboration Enhancing
External Application
Analogous
Example
Code Task Focus Restrictions
OS1 Describe Domain Domain is restricted
to feature classes
surveyed by
Ordnance Survey
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
30. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Purpose Categories
Domain Defining
Ontology Process Related
Data Process Related
Investigative
Collaboration Enhancing
External Application
Analogous
Example
Code Task Focus Restrictions
Pont5 Facilitate Ontology Process is the
Process alignment of
ontologies
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
31. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Purpose Categories
Domain Defining
Ontology Process Related
Data Process Related
Investigative
Collaboration Enhancing
External Application
Analogous
Example
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
32. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Outline
1 Background and Motivation
Multi-perspective Ontology Engineering
Ontology Purposes
2 Contributions
Goal-aware Ontology Editor
Ontology Purpose Vocabulary
Formalisation
Current Work
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
33. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Representing Ontology Purposes in OWL
Goals
Enable ontology contributors to formalise their ontology
purpose
Allows users to express themselves in a manner that is
close to the way they would normally use (free text)
classify ontologies based on their purpose
Can be extended by ontology contributors
Starting point: usable but not intended to cover all possible
purposes
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
34. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Example Formalisation
OS Example
OS Hydrology Ontology aims to describe the OS
Hydrology Feature Classes.
OS Hydrology Ontology intends to enable the
Semi-automatic Processing of Ordnance Survey Data.
OS Hydrology Ontology intends to improve the Data Usage
of Ordnance Survey Customers.
Inferences we want
OS Hydrology Ontology is a Data Processing Ontology.
OS Hydrology Ontology is a Domain Specifying Ontology.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
35. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Abstract and Concrete levels
Abstract Concepts and Relations
Agent, Artifact, Creation Action, has (purpose) focus, Purpose
Focus, Purpose Task.
A Data Processing Ontology is anything that: is a kind of
Ontology; has purpose focus at least one Data Process.
Concrete Concepts and Relations
aims to describe, describes, aims to facilitate, aims to provide,
intends to enable, data process.
The relationship intends to improve is a special type of the
relationship has purpose focus.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
36. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Outline
1 Background and Motivation
Multi-perspective Ontology Engineering
Ontology Purposes
2 Contributions
Goal-aware Ontology Editor
Ontology Purpose Vocabulary
Formalisation
Current Work
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
37. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Purpose Elicitation Dialogue
Elicit formal description of ontology purpose in order to
classify ontology.
Aid user to make transition from free text to formal
representation.
Use Ontology of Purpose Ontologies to guide dialogue
episode.
Dialogue at concrete level, reasoning at abstract level.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
38. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Basic Dialogue Structure
Elicit free text purpose description
NLP analysis to generate purpose hypotheses
Clarify and confirm hypotheses:
elicit instances linked to concrete concepts
succeeds when classification can be made
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
39. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Basic Dialogue Structure
Elicit free text purpose description
NLP analysis to generate purpose hypotheses
Clarify and confirm hypotheses:
elicit instances linked to concrete concepts
succeeds when classification can be made
Annotations
To describe in an unambiguous manner the inland hydrology
feature classes surveyed by Ordnance Survey with the intention
of improving the use of the surveyed data by our customers and
enabling semi-automatic processing of these data.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
40. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Basic Dialogue Structure
Elicit free text purpose description
NLP analysis to generate purpose hypotheses
Clarify and confirm hypotheses:
elicit instances linked to concrete concepts
succeeds when classification can be made
Hypothesis Domain Describing Ontology
OS Hydrology Ontology aims to describe X. X is a Knowledge
Domain.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
41. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Basic Dialogue Structure
Elicit free text purpose description
NLP analysis to generate purpose hypotheses
Clarify and confirm hypotheses:
elicit instances linked to concrete concepts
succeeds when classification can be made
Hypothesis Data Processing Ontology
OS Hydrology Ontology intends to enable X. X is a Data Usage.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
42. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Architecture
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
43. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
Plan
Implement Dialogue Plan generation for a few purpose
types
Evaluate Dialogue Plan generation without Rabbit
interpreter
Add Rabbit interpreter and evaluate with real users
Suggest ontology to reuse based on matching ontology
purpose
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
44. Goal-aware Ontology Editor
Background and Motivation Ontology Purpose Vocabulary
Contributions Formalisation
Current Work
The End
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering
45. Acknowledgements
Creative Commons Images from flckr.com
Surveyor image by Wessex Archeology
Water Ecologist by lindenbaum
Flood Rescuer by Tree & J Hensdill
Orienteerer by Tarnie
Dilbert visionary strip, copyright UFS, Inc.
Denaux, Cohn, Dimitrova and Hart Multi-Perspective Ontology Engineering