NeOn Tool Support for Building Ontologies By Reuse - ICBO 09
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The NeOn Toolkit is an ontology development environment that supports ontology reuse through various tools and features. It allows users to find, understand, modularize, and visualize ontologies for reuse. It also helps diagnose and repair inconsistencies that may arise from integrating multiple ontologies. The toolkit is extensible and supports the full ontology development lifecycle.
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Pal gov.tutorial4.session11.lab zinnarontologybasedwebservices
Pal gov.tutorial4.session11.lab zinnarontologybasedwebservices
More from Mathieu d'Aquin
A factorial study of neural network learning from differences for regression
The document describes a factorial study that trained neural networks to perform regression tasks using differences between cases rather than raw data. It varied factors like the amount of training data, number of epochs, number of similar cases used to determine differences, and whether original features were included with differences. The study found that learning from differences generally required similar data amounts but converged faster. Adding original features was not always beneficial but never significantly hurt performance. The best settings depended on the specific task. Learning from differences showed potential but has limitations like difficulty scaling to large datasets.
Recentrer l'intelligence artificielle sur les connaissances
The document appears to contain rules for assigning values to variables (x[n]) based on logical conditions. It includes 14 rules using comparisons of the variable values, logical operators, and numeric values. It also reports the training and test accuracies of the rules as 92.13% and 89.3% respectively.
Data and Knowledge as Commodities
This document summarizes Mathieu d'Aquin's career path and research interests. It notes that he has worked at LORIA in Nancy, France from 2002-2006, at the Knowledge Media Institute at the Open University in Milton Keynes, UK from 2006-2017, and at the Data Science Institute at NUI Galway in Ireland from 2017-2021. His research has focused on using knowledge-driven and hybrid data-driven/knowledge-driven approaches to understand data provenance, content, and results from data analysis in order to achieve intelligent data understanding.
Unsupervised learning approach for identifying sub-genres in music scores
This document discusses an unsupervised learning approach to identify sub-genres in music scores. It explores different ways of representing musical features like pitch and timing in vector formats that can be analyzed using clustering algorithms. Evaluating different feature representations on a sample of folk tunes, the best results were obtained using a combined weighting of pitch, timing, beats extracted from audio files. This approach shows potential for applications like music information retrieval, studying musical genres and connections between tunes.
Is knowledge engineering still relevant?
Knowledge engineering remains relevant for developing knowledge-based systems and representing knowledge on the semantic web and in knowledge graphs. It also has applications in data science for understanding the relationships between data, models, and techniques. Recent work has applied knowledge engineering to explain data patterns, propagate data policies, and make technological artifacts more accessible to non-experts. The field can help scale and integrate tools for knowledge curation, explanation, and knowledge-driven data access and interpretation.
A data view of the data science process
This document discusses the need to study data science as a discipline through examining the processes, techniques, and outputs. It presents data science as consisting of iterative steps like forming hypotheses, collecting and analyzing data, and extracting results. Ontologies and platforms are proposed as tools to systematically describe datasets, licenses, models, and tasks. Case studies examine modeling data flows and understanding patterns in large data science systems. The document argues for an interdisciplinary approach and using techniques like science fiction to ensure data science is developed and applied responsibly through considering social and ethical implications.
Dealing with Open Domain Data
This document discusses dealing with open domain data and recent examples. It begins by explaining that typical knowledge systems are closed domain, while open domain systems can answer unknown questions. It then discusses early work using the Watson ontology and Semantic Web to build open domain question answering. A core assumption was that the Semantic Web would know everything if it continued growing, which did not occur. However, recent projects like AFEL have shown the Semantic Web and DBpedia can represent data from many domains and be used for tasks like detecting topics in activity streams, explaining patterns in data, and finding biases. While applications using open domain linked data are still limited, the ability to represent diverse data in a single graph remains important.
Web Analytics for Everyday Learning
This document discusses web analytics and personal analytics for learning. It describes how web analytics can analyze user activities on websites and online systems. Personal analytics can help users improve their behavior by self-tracking. Learning analytics analyze student activities and data from university systems to provide recommendations and applications like vital signs dashboards for doctors. The goal of analytics for everyday learning (AFEL) is to create theory-backed methods and tools that support self-directed learners in making effective use of online resources according to their goals. A scenario is described of a learner who uses an AFEL dashboard to track her progress on different topics and set goals to focus more on areas she is weaker in, like statistics. Challenges discussed include collecting integrated personal data
Presentation a in ovive montpellier - 26%2 f06%2f2018 (1)
Presentation to in ovive network - montpellier - 26/06/2018
Learning Analytics: understand learning and support the learner
The document discusses learning analytics, which is defined as the measurement, collection, analysis, and reporting of data about learners and their contexts for the purpose of understanding and optimizing learning and the environments where it occurs. It provides examples of how learning analytics can be used for prediction, exploration, and interpretation of learning data. It also discusses challenges in recognizing and measuring learning using data from open, unconstrained online environments. Finally, it presents a cognitive model of learning and knowledge construction that involves constructive friction as the driving force behind learning.
The AFEL Project
The AFEL Project aims to create tools to support self-directed learners by analyzing data from their online activities. It collects browsing history and social media data to identify topics of interest and measure progress. Indicators show how learners engage with different topics over time. Learners can set goals which are checked against their daily activities. Recommendations guide further learning based on indicators and goals. The project developed a data platform, visual analytics tools, and a mobile app to help learners optimize their use of online resources for informal learning.
Assessing the Readability of Policy Documents: The Case of Terms of Use of On...
This document summarizes a study that assessed whether the readability of terms of use documents from various websites is adapted to the education levels of their target audiences. It finds that readability is often not well-adapted, using two main methods: analyzing over 1500 terms of use with the SMOG readability index and comparing typical education levels of website audiences in different countries. Results show mismatches between document complexity and user education levels for many US and India-based sites. The study concludes readability assessment is useful but has limitations when applied broadly.
Data ethics
Guest lecture for the Data and Analytics summer school of the Accounting and Finance Master in NUI Galway
Data for Learning and Learning with Data
This document discusses using data to support self-directed learning. It presents a simple model of online learning involving people, resources, topics, and organizations. A scenario is described of a learner named Jane who uses an online dashboard to view her learning activities and progress across different topics. The dashboard helps Jane realize she has been procrastinating on topics she enjoys less, like statistics, and set goals to focus more on those areas. Challenges discussed include recognizing and measuring learning in open online environments. The document also references a cognitive model of learning as a co-evolutionary process driven by "constructive friction," and identifies indicators of learning like coverage of topics.
Towards an “Ethics in Design” methodology for AI research projects
The document proposes an "Ethics in Design" methodology for AI research projects. It argues that current ethics debates focus too much on technical data protection and not broader societal impacts. The methodology calls for a reflective, dialectic process involving data scientists and social scientists throughout a project's lifecycle to identify ethical issues, minimize risks, and increase positive societal impact. It explores applying this approach to two case studies and outlines principles of being dialectic, reflective, creative, and all-encompassing. The document concludes by advocating adopting these guidelines and collaborating across fields to further develop ethics methodologies.
AFEL: Towards Measuring Online Activities Contributions to Self-Directed Lear...
The document describes how Jane, a 37-year-old administrative assistant, uses the AFEL platform to track and improve her self-directed online learning activities related to her hobbies, career development, and math skills. Jane connects data from her browsing history, Facebook, and MOOCs to the AFEL dashboard. By reviewing her dashboard daily, Jane realizes she has been procrastinating on statistics and sets goals to focus more on it. The dashboard will now remind Jane of her goals and recommend additional learning activities.
Profiling information sources and services for discovery
Presentation at the Digital Innovation Networks Forum on Next Generation Internet - Brussels - 27/06/2017
Analyse de données et de réseaux sociaux pour l’aide à l’apprentissage infor...
Presentation invitée à la conference EIAH - 08/06/2017
From Knowledge Bases to Knowledge Infrastructures for Intelligent Systems
1) The document discusses how knowledge representation and ontologies have evolved from closed knowledge bases for specific domains to open knowledge infrastructures that can handle large amounts of diverse data and information at scale.
2) It provides examples of how ontologies and semantic technologies are being used to build intelligent systems that can search, integrate, and automatically process and analyze large datasets.
3) Going forward, ontologies will play an important role in populating knowledge from data and dialog, enabling the automatic exploitation of data by autonomous agents, and enhancing data analytics and mining through semantic representation of datasets, tools, and policies.
Data analytics beyond data processing and how it affects Industry 4.0
The document discusses how data analytics is moving beyond just data processing to affect Industry 4.0. It summarizes the research areas and industry partnerships of the Insight Centre for Data Analytics in NUI Galway, including linked data, machine learning, and media analytics. Key applications discussed are monitoring energy consumption using stream processing and event detection, predicting future behavior through machine learning, and detecting and classifying anomalies to inform predictive maintenance decisions.
More from Mathieu d'Aquin (20)
A factorial study of neural network learning from differences for regression
A factorial study of neural network learning from differences for regression
Recentrer l'intelligence artificielle sur les connaissances
Recentrer l'intelligence artificielle sur les connaissances
Unsupervised learning approach for identifying sub-genres in music scores
Unsupervised learning approach for identifying sub-genres in music scores
Presentation a in ovive montpellier - 26%2 f06%2f2018 (1)
Presentation a in ovive montpellier - 26%2 f06%2f2018 (1)
Learning Analytics: understand learning and support the learner
Learning Analytics: understand learning and support the learner
Assessing the Readability of Policy Documents: The Case of Terms of Use of On...
Assessing the Readability of Policy Documents: The Case of Terms of Use of On...
Towards an “Ethics in Design” methodology for AI research projects
Towards an “Ethics in Design” methodology for AI research projects
AFEL: Towards Measuring Online Activities Contributions to Self-Directed Lear...
AFEL: Towards Measuring Online Activities Contributions to Self-Directed Lear...
Profiling information sources and services for discovery
Profiling information sources and services for discovery
Analyse de données et de réseaux sociaux pour l’aide à l’apprentissage infor...
Analyse de données et de réseaux sociaux pour l’aide à l’apprentissage infor...
From Knowledge Bases to Knowledge Infrastructures for Intelligent Systems
From Knowledge Bases to Knowledge Infrastructures for Intelligent Systems
Data analytics beyond data processing and how it affects Industry 4.0
Data analytics beyond data processing and how it affects Industry 4.0
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Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
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“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Essentials of Automations: Exploring Attributes & Automation Parameters
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
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Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Leveraging the Graph for Clinical Trials and Standards
Katja Glaß
OpenStudyBuilder Community Manager - Katja Glaß Consulting
Marius Conjeaud
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Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
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Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
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NeOn Tool Support for Building Ontologies By Reuse - ICBO 09
- 1. NeOn Tool Support for Building Ontologies By Reuse (Software demo at ICBO 2009) Mathieu d’Aquin Aldo Gangemi, Enrico Motta, Martin Dzbor, Peter Haase, Michael Erdmann The NeOn Consortium
- 2. Outline of the Demo The NeOn toolkit, an environment for building ontologies Reusing ontologies = finding ontologies Watson: an ontology search engine Cupboard: ontology publication and reuse made easy The Cupboard plugin for the NeOn Toolkit Reusing ontologies = understanding ontologies Key concepts summary based ontology visualization and exploration Reusing modules/patterns of ontologies Extracting ontology modules Reusing ontology design patterns with the XD plugin Reusing ontologies = risk of inconsistency/incoherence RaDON for diagnosis and repair of ontologies Slide 2
- 3. Slide 3 Core ontology editor with Support ontology engineering and management Support for complete ontology lifecycle Support for different languages (OWL, F-Logic) Support for networked ontologies (modules, mappings) Built on the Eclipse platform Extensible architecture Via Eclipse pluginmechanism Community support http://neon-toolkit.org Slide 3 The NeOn Toolkit ontology development environment
- 4. Finding ontologies: Watson and Cupboard Watson: Semantic Web Search Engine Cupboard: Ontology publication portal Slide 4 http://watson.kmi.open.ac.uk http://cupboard.open.ac.uk
- 7. Slide 7
- 9. Based on a summary of the ontology
- 11. Modularization Support in the NeOn Toolkit Slide 10 Ontology Partitioning Module Composition Module Specification Module Extraction
- 12. Pattern-Based Design Pattern-based ontology design is the activity of searching, selecting, and composing different patterns Based on a catalogue of design patterns: Slide 11 http://www.ontologydesignpatterns.org From Aldo Gangemi
- 13. Support for Extreme Ontology Design Slide 12 From ValentinaPresutti
- 15. When integrated ontologies are inconsistent, how do we debug the cause of the inconsistency and repair it?From Peter Haase
- 16. Slide 14 Conclusion NeOn offers a variety of tools for ontology reuse But this is only one aspect of the the NeOn project. Many elements (and corresponding tools) not covered: Methodology for building ontologies and ontology based application Ontology matching and alignment Collaboration and editorial workflow for ontologies development Ontology localization Reuse of non-ontological resources Concrete applications in 2 domains And it is extensible! Anybody can develop a plugin and extend existing plugins More at http://neon-project.org and http://neon-toolkit.org