Amit Sheth, "Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges," Keynote talk at the workshop on Semantic Web: Models, Architecture and Management, September 21, 2000, Lisbon, Portugal.
This was the keynote given at probably the first international event with "Semantic Web" in title (and before the well known SciAm article). As in TBL's use of Semantic Web in his 1999 book, (semantic) metadata plays central role. The use of Worldmodel/Ontology is consistent with our use of ontology for (Web) information integration in 1994 CIKM paper. Summary of the talk by event organizers and other details are at: http://knoesis.org/library/resource.php?id=735
Prof. Sheth started a Semantic Web company Taalee, Inc. in 1999 (product was called MediaAnywhere A/V search engine- discussed in this paper in the context of one of its use by a customer Redband Broadcasting). The product included Semantic Web/populated Ontology based semantic (faceted) search, semantic browsing, semantic personalization, semantic targeting (advertisement), etc as is described in U.S. Patent #6311194, 30 Oct. 2001 (filed 2000). MediaAnywhere has about 25 ontologies in News/Business, Sports, Entertainment, etc.
Taalee merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers).
Don't Handicap AI without Explicit KnowledgeAmit Sheth
Keynote at IEEE Services 2021: Abstract: https://conferences.computer.org/services/2021/keynotes/sheth.html
Video: https://lnkd.in/d-r3YXaC
Video of the same keynote given at DEXA2021: https://www.youtube.com/watch?v=u-06kK9TysA
September 9, 2021 15:00 - 16:20 UTC
ABSTRACT
Knowledge representation as expert system rules or using frames and a variety of logics played a key role in capturing explicit knowledge during the hay days of AI in the past century. Such knowledge, aligned with planning and reasoning is part of what we refer to as Symbolic AI. The resurgent AI of this century in the form of Statistical AI has benefitted from massive data and computing. On some tasks, deep learning methods have even exceeded human performance levels. This gave the false sense that data alone is enough, and explicit knowledge is not needed. But as we start chasing machine intelligence that is comparable with human intelligence, there is an increasing realization that we cannot do without explicit knowledge. Neuroscience (role of long-term memory, strong interactions between different specialized regions of data on tasks such as multimodal sensing), cognitive science (bottom brain versus top brain, perception versus cognition), brain-inspired computing, behavioral economics (system 1 versus system 2), and other disciplines point to need for furthering AI to neuro-symbolic AI (i.e., hybrid of Statistical AI and Symbolic AI, also referred to as the third wave of AI). As we make this progress, the role of explicit knowledge becomes more evident. I will specifically look at our endeavor to support human-like intelligence, our desire for AI systems to interact with humans naturally, and our need to explain the path and reasons for AI systems’ workings. Nevertheless, the variety of knowledge needed to support understanding and intelligence is varied and complex. Using the example of progressing from NLP to NLU, I will demonstrate the dimensions of explicit knowledge, which may include, linguistic, language syntax, common sense, general (world model), specialized (e.g., geographic), and domain-specific (e.g., mental health) knowledge. I will also argue that despite this complexity, such knowledge can be scalability created and maintained (even dynamically or continually). Finally, I will describe our work on knowledge-infused learning as an example strategy for fusing statistical and symbolic AI in a variety of ways.
RPA (Robotic Process Automation), POA (Process Oriented Architecture) And BPM...Alan McSweeney
RPA (Robotic Process Automation) is an opportunity to add value by creating (partially of completely) automated meta processes that control one or more existing applications to automate the interactions with those applications and thus enable the successful operation of the process.
RPA can reduce manual effort, reduce manuals errors, improve quality, accuracy and ensure consistency. RPA based processes are always available, can respond to changes more quickly and are more scalable that manual processes. They captures process information for reporting, analysis and process improvement and provide greater visibility and control.
Successful RPA is a pre-requisite to exploiting other technologies and approaches such as artificial intelligence.
POA (Process Oriented Architecture) is concerned with linking process areas to actual (desired) interactions – customer (external interacting party) service journeys through the organisation.
BPM (Business Process Management) is the disciplined approach to identify, design, execute, document, measure, monitor and control both automated and non-automated business processes to achieve consistent, targeted results aligned with an organisation’s strategic goals.
Increasing velocity of change means that informal, undocumented expertise makes reaction slow, exceptions are only known and understood locally – process architecture ensures knowledge is documented and change can happen quickly.
A change to digital operations means that internal processes are exposed – the potentially inefficient and manual processes must be made efficient and external interactions must be masked from the internal complexity.
Moving the organisation from one that is internally focussed around its siloed structures to one that is focussed on customer (external interacting party) straight-through interactions.
Automating existing processes requires a structured approach to process analysis.
A structured approach to designing new optimised processes is important to successful RPA implementation.
Generative AI Use-cases for Enterprise - First SessionGene Leybzon
In this presentation, we will delve into the exciting applications of Generative AI across various business domains. Leveraging the capabilities of artificial intelligence and machine learning, Generative AI allows for dynamic, context-aware user interfaces that adapt in real-time to provide personalized user experiences. We will explore how this transformative technology can streamline design processes, facilitate user engagement, and open the doors to new forms of interactivity.
Introduction:
The frontier of innovation is increasingly shaped by the convergence of transformative technologies. Among these, Generative AI and Quantum Computing stand out, offering untapped potential when harmonized. This proposal delves deep into the fusion of these technologies to sculpt Digital Twins - dynamic, real-time virtual replicas with heightened predictive prowess.
Understanding the Technologies:
Generative AI: This refers to sophisticated algorithms with the ability to autonomously generate new, high-quality content. Think of it as the artistic hand of AI, crafting images, texts, and other forms of data.
Quantum Computing: Beyond the realm of classical bits lies the quantum bit. Quantum Computing uses these bits to enact computations at speeds that classical computers can only dream of. This velocity is not just about speed but also the proficiency in managing immense datasets and intricate computations.
Why Quantum Computing is Integral to AI's Evolution:
Classic AI models, despite their strengths, hit a wall when faced with monumental data and complex optimization. Quantum Machine Learning (QML) bridges this gap. QML, a vibrant sub-discipline within AI, channels quantum algorithms to supercharge each phase of machine learning. The marriage of Generative AI and Quantum Computing births an ecosystem where training elaborate models not only becomes swift but also diversifies the realm of content generation.
Applications within the BFSI Sector:
BFSI - Banking, Financial Services, and Insurance - is a realm characterized by data-heavy processes. Imagine a world where:
Risk Assessment: Real-time insights from Digital Twins predict market fluctuations with unparalleled precision.
Fraud Detection: By harnessing vast datasets, systems instantly flag and mitigate unusual transactions.
Portfolio Optimization: Investors access optimized portfolios, benefiting from quantum-enhanced computations that account for countless variables.
Algorithmic Trading: The trading world becomes more fluid and responsive, with algorithms making informed decisions in near real-time.
Roadmap to Implementation:
To bring this vision to life, our interdisciplinary team is poised to:
Design models bespoke to BFSI needs.
Engage with industry stakeholders, forging strong partnerships.
Pilot and refine the approach, ensuring tangible, real-world validation.
Keynote at Advantech's AI+Smart Manufacturing event. Shared the AI trend in smart manufacturing as well as a demo regarding how to use Azure Cognitive Services to empower employees and customers.
Don't Handicap AI without Explicit KnowledgeAmit Sheth
Keynote at IEEE Services 2021: Abstract: https://conferences.computer.org/services/2021/keynotes/sheth.html
Video: https://lnkd.in/d-r3YXaC
Video of the same keynote given at DEXA2021: https://www.youtube.com/watch?v=u-06kK9TysA
September 9, 2021 15:00 - 16:20 UTC
ABSTRACT
Knowledge representation as expert system rules or using frames and a variety of logics played a key role in capturing explicit knowledge during the hay days of AI in the past century. Such knowledge, aligned with planning and reasoning is part of what we refer to as Symbolic AI. The resurgent AI of this century in the form of Statistical AI has benefitted from massive data and computing. On some tasks, deep learning methods have even exceeded human performance levels. This gave the false sense that data alone is enough, and explicit knowledge is not needed. But as we start chasing machine intelligence that is comparable with human intelligence, there is an increasing realization that we cannot do without explicit knowledge. Neuroscience (role of long-term memory, strong interactions between different specialized regions of data on tasks such as multimodal sensing), cognitive science (bottom brain versus top brain, perception versus cognition), brain-inspired computing, behavioral economics (system 1 versus system 2), and other disciplines point to need for furthering AI to neuro-symbolic AI (i.e., hybrid of Statistical AI and Symbolic AI, also referred to as the third wave of AI). As we make this progress, the role of explicit knowledge becomes more evident. I will specifically look at our endeavor to support human-like intelligence, our desire for AI systems to interact with humans naturally, and our need to explain the path and reasons for AI systems’ workings. Nevertheless, the variety of knowledge needed to support understanding and intelligence is varied and complex. Using the example of progressing from NLP to NLU, I will demonstrate the dimensions of explicit knowledge, which may include, linguistic, language syntax, common sense, general (world model), specialized (e.g., geographic), and domain-specific (e.g., mental health) knowledge. I will also argue that despite this complexity, such knowledge can be scalability created and maintained (even dynamically or continually). Finally, I will describe our work on knowledge-infused learning as an example strategy for fusing statistical and symbolic AI in a variety of ways.
RPA (Robotic Process Automation), POA (Process Oriented Architecture) And BPM...Alan McSweeney
RPA (Robotic Process Automation) is an opportunity to add value by creating (partially of completely) automated meta processes that control one or more existing applications to automate the interactions with those applications and thus enable the successful operation of the process.
RPA can reduce manual effort, reduce manuals errors, improve quality, accuracy and ensure consistency. RPA based processes are always available, can respond to changes more quickly and are more scalable that manual processes. They captures process information for reporting, analysis and process improvement and provide greater visibility and control.
Successful RPA is a pre-requisite to exploiting other technologies and approaches such as artificial intelligence.
POA (Process Oriented Architecture) is concerned with linking process areas to actual (desired) interactions – customer (external interacting party) service journeys through the organisation.
BPM (Business Process Management) is the disciplined approach to identify, design, execute, document, measure, monitor and control both automated and non-automated business processes to achieve consistent, targeted results aligned with an organisation’s strategic goals.
Increasing velocity of change means that informal, undocumented expertise makes reaction slow, exceptions are only known and understood locally – process architecture ensures knowledge is documented and change can happen quickly.
A change to digital operations means that internal processes are exposed – the potentially inefficient and manual processes must be made efficient and external interactions must be masked from the internal complexity.
Moving the organisation from one that is internally focussed around its siloed structures to one that is focussed on customer (external interacting party) straight-through interactions.
Automating existing processes requires a structured approach to process analysis.
A structured approach to designing new optimised processes is important to successful RPA implementation.
Generative AI Use-cases for Enterprise - First SessionGene Leybzon
In this presentation, we will delve into the exciting applications of Generative AI across various business domains. Leveraging the capabilities of artificial intelligence and machine learning, Generative AI allows for dynamic, context-aware user interfaces that adapt in real-time to provide personalized user experiences. We will explore how this transformative technology can streamline design processes, facilitate user engagement, and open the doors to new forms of interactivity.
Introduction:
The frontier of innovation is increasingly shaped by the convergence of transformative technologies. Among these, Generative AI and Quantum Computing stand out, offering untapped potential when harmonized. This proposal delves deep into the fusion of these technologies to sculpt Digital Twins - dynamic, real-time virtual replicas with heightened predictive prowess.
Understanding the Technologies:
Generative AI: This refers to sophisticated algorithms with the ability to autonomously generate new, high-quality content. Think of it as the artistic hand of AI, crafting images, texts, and other forms of data.
Quantum Computing: Beyond the realm of classical bits lies the quantum bit. Quantum Computing uses these bits to enact computations at speeds that classical computers can only dream of. This velocity is not just about speed but also the proficiency in managing immense datasets and intricate computations.
Why Quantum Computing is Integral to AI's Evolution:
Classic AI models, despite their strengths, hit a wall when faced with monumental data and complex optimization. Quantum Machine Learning (QML) bridges this gap. QML, a vibrant sub-discipline within AI, channels quantum algorithms to supercharge each phase of machine learning. The marriage of Generative AI and Quantum Computing births an ecosystem where training elaborate models not only becomes swift but also diversifies the realm of content generation.
Applications within the BFSI Sector:
BFSI - Banking, Financial Services, and Insurance - is a realm characterized by data-heavy processes. Imagine a world where:
Risk Assessment: Real-time insights from Digital Twins predict market fluctuations with unparalleled precision.
Fraud Detection: By harnessing vast datasets, systems instantly flag and mitigate unusual transactions.
Portfolio Optimization: Investors access optimized portfolios, benefiting from quantum-enhanced computations that account for countless variables.
Algorithmic Trading: The trading world becomes more fluid and responsive, with algorithms making informed decisions in near real-time.
Roadmap to Implementation:
To bring this vision to life, our interdisciplinary team is poised to:
Design models bespoke to BFSI needs.
Engage with industry stakeholders, forging strong partnerships.
Pilot and refine the approach, ensuring tangible, real-world validation.
Keynote at Advantech's AI+Smart Manufacturing event. Shared the AI trend in smart manufacturing as well as a demo regarding how to use Azure Cognitive Services to empower employees and customers.
Learn how a lab in Malaysia has utilized the Roche Digital Pathology portfolio to - improve collaboration among pathologists, provide rapid turnaround time for diagnosis and consults and enrich education and training across multiple group and individual settings. Dr. Pathmanathan at Subang Jaya Medical Center encourages those reluctant to embrace digital pathology to try it for all the benefits it offers and get ahead of the curve.
Challenges in AI LLMs adoption in the EnterpriseGeorge Bara
The presentation "ITDays_2023_GeorgeBara" discusses challenges in adopting AI large language models (LLMs) in enterprise settings.
The presentation covers:
1. **Challenges in AI LLMs adoption**: It highlights the noise in the current AI landscape and questions the practical use of AI in real businesses.
2. **The DNA of an Enterprise**: Defines enterprise sizes and discusses the new solutions adoption process, emphasizing effective integration and minimizing disruption.
3. **Enterprise-Grade**: Lists qualities like robustness, reliability, scalability, performance, security, and support that are essential for enterprise-grade solutions.
4. **What are LLMs?**: Describes the pre-ChatGPT era with BERT, a model used for language understanding, and details its enterprise applications.
5. **LLM use-cases before ChatGPT**: Focuses on data triage, process automation, knowledge management, and the augmentation of business operations.
6. **EU Digital Decade Report**: Points out that AI adoption in Europe is slow and might not meet the 2030 targets.
7. **Adoption Challenges**: Addresses top challenges such as data security, predictability, performance, control, regulatory compliance, ethics, sustainability, and ROI.
8. **Conclusion**: Reflects on the slow adoption of AI in enterprises, suggesting that a surge might occur once the technology matures and is ready for enterprise use.
The presenter concludes by stating that despite the hype around technologies like ChatGPT, enterprises are cautious and will adopt new technologies at their own pace. He anticipates a gradual then sudden adoption pattern once LLMs are proven to be enterprise-ready.
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
AI has come like a big bang.
It can become more intelligent than humans in the near future trampling on aged old philosophical experiences of how to do things. We need to deepen our reflection on the philosophy of AI today
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Artificial Intelligence (AI) Productivity Tools for School and Office is a presentation by Emmanuel Bida Thomas in masterclass organised by Excellence Foundation for South Sudan as part of its personal development series. It was conducted on Saturday 28th and Sunday 29th October 2023 via Zoom and YouTube Livestream
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
Document en Français concernant la démarche ArchiMate.
De l’usage de la couche Physique de la démarche ArchiMate® et de son intégration avec les couches Métier et Système d’information
Tthe 8-step business analysis process that you can apply whether you are in an agile environment or a traditional one, whether you are purchasing off-the-shelf software or building custom code, whether you are responsible for a multi-million dollar project or a one-week project.
Depending on the size and complexity of your project, you can go through these steps quickly or slowly, but to get to a successful outcome you must go through them
Data Con LA 2020
Description
More and more organizations are embracing AI technology by infusing it in their products and services to to differentiate themselves against their competitors. AI is being utilized in some sensitive areas of human life. In this session let's look at some of principles governing adoption of AI in a responsible manner. Why companies are accelerating adoption of AI?
Increasingly organization are accelerating adoption of AI to differentiate their product and services in the market. Outcomes of this digital transformation that we have seen in the areas of optimizing operations, engaging customers, empowering employees and transforming their products and services.
*List some of the sensitive use cases where AI is being applied
*Why governing AI is important and what are those principles?
*How Microsoft is approaching it?
Speaker
Suresh Paulraj, Microsoft, Principal Cloud Solution Architect Data & AI
2019 07 Bizbok with Archimate 3 v3 [UPDATED !]COMPETENSIS
ARCHIMATE & BIZBOK templates
Here is an interpretation on how to implement the BIZBOK recommendation with Archimate 3.
This is an update of the previous documents published in 2018 and 2017.
Any comments or requirements to chdessus@competensis.com
Learn how a lab in Malaysia has utilized the Roche Digital Pathology portfolio to - improve collaboration among pathologists, provide rapid turnaround time for diagnosis and consults and enrich education and training across multiple group and individual settings. Dr. Pathmanathan at Subang Jaya Medical Center encourages those reluctant to embrace digital pathology to try it for all the benefits it offers and get ahead of the curve.
Challenges in AI LLMs adoption in the EnterpriseGeorge Bara
The presentation "ITDays_2023_GeorgeBara" discusses challenges in adopting AI large language models (LLMs) in enterprise settings.
The presentation covers:
1. **Challenges in AI LLMs adoption**: It highlights the noise in the current AI landscape and questions the practical use of AI in real businesses.
2. **The DNA of an Enterprise**: Defines enterprise sizes and discusses the new solutions adoption process, emphasizing effective integration and minimizing disruption.
3. **Enterprise-Grade**: Lists qualities like robustness, reliability, scalability, performance, security, and support that are essential for enterprise-grade solutions.
4. **What are LLMs?**: Describes the pre-ChatGPT era with BERT, a model used for language understanding, and details its enterprise applications.
5. **LLM use-cases before ChatGPT**: Focuses on data triage, process automation, knowledge management, and the augmentation of business operations.
6. **EU Digital Decade Report**: Points out that AI adoption in Europe is slow and might not meet the 2030 targets.
7. **Adoption Challenges**: Addresses top challenges such as data security, predictability, performance, control, regulatory compliance, ethics, sustainability, and ROI.
8. **Conclusion**: Reflects on the slow adoption of AI in enterprises, suggesting that a surge might occur once the technology matures and is ready for enterprise use.
The presenter concludes by stating that despite the hype around technologies like ChatGPT, enterprises are cautious and will adopt new technologies at their own pace. He anticipates a gradual then sudden adoption pattern once LLMs are proven to be enterprise-ready.
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
AI has come like a big bang.
It can become more intelligent than humans in the near future trampling on aged old philosophical experiences of how to do things. We need to deepen our reflection on the philosophy of AI today
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Artificial Intelligence (AI) Productivity Tools for School and Office is a presentation by Emmanuel Bida Thomas in masterclass organised by Excellence Foundation for South Sudan as part of its personal development series. It was conducted on Saturday 28th and Sunday 29th October 2023 via Zoom and YouTube Livestream
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
Document en Français concernant la démarche ArchiMate.
De l’usage de la couche Physique de la démarche ArchiMate® et de son intégration avec les couches Métier et Système d’information
Tthe 8-step business analysis process that you can apply whether you are in an agile environment or a traditional one, whether you are purchasing off-the-shelf software or building custom code, whether you are responsible for a multi-million dollar project or a one-week project.
Depending on the size and complexity of your project, you can go through these steps quickly or slowly, but to get to a successful outcome you must go through them
Data Con LA 2020
Description
More and more organizations are embracing AI technology by infusing it in their products and services to to differentiate themselves against their competitors. AI is being utilized in some sensitive areas of human life. In this session let's look at some of principles governing adoption of AI in a responsible manner. Why companies are accelerating adoption of AI?
Increasingly organization are accelerating adoption of AI to differentiate their product and services in the market. Outcomes of this digital transformation that we have seen in the areas of optimizing operations, engaging customers, empowering employees and transforming their products and services.
*List some of the sensitive use cases where AI is being applied
*Why governing AI is important and what are those principles?
*How Microsoft is approaching it?
Speaker
Suresh Paulraj, Microsoft, Principal Cloud Solution Architect Data & AI
2019 07 Bizbok with Archimate 3 v3 [UPDATED !]COMPETENSIS
ARCHIMATE & BIZBOK templates
Here is an interpretation on how to implement the BIZBOK recommendation with Archimate 3.
This is an update of the previous documents published in 2018 and 2017.
Any comments or requirements to chdessus@competensis.com
Amit P. Sheth, “Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships,” Keynote at the 29th Conference on Current Trends in Theory and Practice of Informatics (SOFSEM 2002), Milovy, Czech Republic, November 22–29, 2002.
Keynote: http://www.sofsem.cz/sofsem02/keynote.html
Related paper: http://knoesis.wright.edu/?q=node/2063
Final Next Generation Content ManagementScott Abel
Presented by Tony Pietricola at the CM Pros Fall 2007 Summit, November 26, 2007.
This slide deck addresses:
- The future of websites and WCM
- Understanding the staying power of Web 2.0
- Integrating Web 2.0 and WCM strategies even though they are counter to each other
- Companies putting this to use
Semantic Interoperability and Information Brokering in Global Information Sys...Amit Sheth
Amit Sheth, "Semantic Interoperability and Information Brokering in Global Information Systems," Keynote talk at IEEE-Metadata Conference, Bethesda, MD, USA, April 6, 1999.
Semantic Interoperability in Infocosm: Beyond Infrastructural and Data Intero...Amit Sheth
Amit Sheth, Keynote: International Conference on Interoperating Geographic Systems (Interop’97), Santa Barbara, December 3-4 1997.
Related technical paper: http://knoesis.org/library/resource.php?id=00230
Concept and example of a semantic solution implemented with SQL views to cooperate with users on queries over structured data with independence from database schema knowledge and technology.
X api chinese cop monthly meeting feb.2016Jessie Chuang
Topics
XAPI Vocabulary spec. From ADL
Linked Data / Semantic web. / Web 3.0
Linked Data in education and content recommender
Semantic search and Google Knowledge Graph
APIs eat software (connect with partners and services)
How should we exploit data and build intelligence layer?
Case Study (Hong Ding Educational Technology)
Monetize your data and add value (intelligence)
SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITYAmit Sheth
Amit Sheth, SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITY, Keynote at:
CONTENT- AND SEMANTIC-BASED INFORMATION RETRIEVAL @ SCI 2002.
A Semantic Web Primer: The History and Vision of Linked Open Data and the Web 3.0
There is a transformational change coming to the world-wide-web that will fundamentally alter how its vast array of data is structured, and as a result greatly enhance the way humans and machines interact with this indispensable resource. Given the inertia of existing infrastructure, this segue will be evolutionary as opposed to revolutionary, and indeed has been envisioned since the inception of the web. Come join us for a layman's look at the nature of the Web 3.0, its historical underpinnings, and the opportunities it presents.
Similar to Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges (20)
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
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Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges
1. Workshop on Semantic Web: Models, Architecture and Management September 21, 2000 – Lisbon, Portugal by Amit Sheth Director, Large-Scale Distributed Information Systems Lab. University of Georgia, Athens, GA USA http://lsdis.cs.uga.edu Founder/Chairman, Taalee, Inc. http://www.taalee.com Special thanks, Digital Library project team at LSDIS Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges
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7. Example of searching on DAML-centric semantic Web Source: http://www.zdnet.com/pcweek/stories/jumps/0,4270,2432946,00.html
8. Value of Information Directory Targeting Search = Table of Contents = Index The Power of Semantics Semantics = Meaning with Context Semantics results in deep understanding of content, allowing highly relevant and fresh results, better personalization, and exceptional targeting.
14. Taalee Metadata on Football Assets Rich Media Reference Page Baltimore 31, Pit 24 http://www.nfl.com Quandry Ismail and Tony Banks hook up for their third long touchdown, this time on a 76-yarder to extend the Raven’s lead to 31-24 in the third quarter. Professional Ravens, Steelers Bal 31, Pit 24 Quandry Ismail, Tony Banks Touchdown NFL.com 2/02/2000 League: Teams: Score: Players: Event: Produced by: Posted date: Semantic Cataloging Virage Search on football touchdown Jimmy Smith Interview Part Seven Jimmy Smith explains his philosophy on showboating. URL: http://cbs.sportsline... Brian Griese Interview Part Four Brian Griese talks about the first touchdown he ever threw. URL: http://cbs.sportsline... Metadata from Typical Cataloging of Football Assets
15. Metadata What else can a context do? (a commercial perspective) Semantic Enrichment
16. Simply the most precise and freshest A/V search Semantic Search Context and Domain Specific Attributes Uniform Metadata for Content from Multiple Sources, Can be sorted by any field Delightful, relevant information, exceptional targeting opportunity
17. Creating a Web of related information What can a context do?
18. System recognizes ENTITY & CATEGORY Relevant portion of the Directory is automatically presented. Semantic Directory
19. Users can explore Semantically related Information. Semantic Directory
21. Looking ahead TO: Information requests Content search Semantic retrieval Interpretation Knowledge creation Knowledge sharing FROM: Browsing Lexical search Data exchange Data retrieval Semantic Information Brokering Semantic Web
22. Evolving targets and approaches in integrating data and information (a personal perspective) Mermaid DDTS Multibase, MRDSM, ADDS, IISS, Omnibase, ... Generation I (multidatabases) 1980s DL-II/DARPA/KA2 projects, OntoBroker, … Taalee, Observer ADEPT, InfoQuilt Generation III (information brokering) 1997... InfoSleuth, KMed, DL-I projects Infoscopes, HERMES, SIMS, Garlic,TSIMMIS,Harvest, RUFUS,... Generation II (mediators) 1990s VisualHarness InfoHarness Semantic Information Brokering Semantic Web
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25. Information brokering is an architecture that guides creation and management of information systems and semantic-level solutions to serve a variety of information stakeholders (participants), including providers, facilitators, consumers, and the business involved in creating, enhancing and using of information. Semantic Information Brokering Kashyap & Sheth 1993
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27. Taking advantage of the Web for learning Graduate students in a College of Geography have a final project in which a case of study is proposed. In the case, they are supposed to help a City Council in making decisions over the planning of a new landfill. This is a hands-on learning exercise through the interaction with a Digital Earth and the starting point would be to find the best location for the landfill*. Tacoma Landfill * This scenario comes in support of one of the suggestions for Digital Earth scenarios sampled by the “First Inter-Agency Digital Earth Working Group, an effort on behalf of NASA’s inter-agency Digital Earth Program.
28. An example scenario of learning on the Web by definition by semantics by synonymy A first cut refinement leads us to the following information request: Find a proper soil in sites not subject to flooding or high groundwater levels for a new landfill near the industrial zone . Liquefaction phenomenon cannot occur . Find a landfill site for a new landfill near the source of the wastes . The earthquakes’ impacts must be evaluated . A high level information request would be:
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30. Partial sample ontologies for semantic information brokering: An example scenario of learning on the Web
39. Example Ontology NATURAL DISASTER Volcano Magnitude Range Damage in $ Damage Type Number of deaths Magnitude Flood Earthquake Tsunami
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43. Design of “affects” How do volcanoes affect the environment? AFFECTS VOLCANO LOCATION ASH RAIN PYROCLASTIC FLOW ENVIRON. LOCATION PEOPLE ATMOSPHERE PLANT BUILDING DESTROYS COOLS TEMP DESTROYS KILLS
44. [Area (Pyroclastic Flows) INTERSECT Area (Crop)] => [Pyroclastic Flows d estroy Crop] [Size (Ash Particles) < 2] => [Ash Rain c ools the Atmosphere] [Pyroclastic Flows d estroy Crop] and [Ash Rain cools the Atmosphere] => [Volcanoes affect the Environment] ( x | x ASC) and ( y | y BSC) [ FN(x) operator FN(y) ]* => [ ASC relation BSC ] [ ASC relation BSC ]* => A affects B Design of “affects”
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49. For additional details on Information Brokering Architecture: Realizing Semantic Information Brokering and S emantic Web ITC-IRST/University of Trento Seminar Series on Perspectives on Agents: Theories and Technologies, April, 27, 2000, Trento, Italy http://lsdis.cs.uga.edu/~adept/presenta.html For additional details on ISCAPE specification and Execution: Project Overview and Detailed Presentation at: http://lsdis.cs.uga.edu/~adept/presenta.html Demonstrations at: http://lsdis.cs.uga.edu/~adept Backup/Detail Slides
50. <! -- A template collection for all iscapes -- > <?xml version = “1.0” ?> <!DOCYPE IscapeCollection SYSTEM “IscapeCollection.dtd” > <! -- All Iscapes -- > <IscapeCollection> <!-- An iscape specification for how stratovolcanoes affect the environment -- > <Iscape> < -- Identifying this iscape -- > <ID>Volcano – Env </ID> <Name> How do stratovolcanoes affect the environment </Name> <Description> An iscape using the affects relationship </Description > <! – All ontologies which participate -- > <Ontologies> <Ontology>Volcano</Ontology> <Ontology>Environment</Ontology> </Ontologies> <! – Operations involved -- > <Operation> <Relation>Affects</Relation> </Operation> Iscape specification using XML
51. Iscape specification using XML <!— Constraints on ontologies -- > <Ontological Constraints> <Constraint> Volcano morphology is stratovolcano </Constraint> <Constraint> Volcano start year is 1950 </Constraint> </Ontological Constraints> <!—Metadata to present in the result --> <Presentation> Volcano and Environment Metadata </Presentation> <!—What can the student configure -- > <Student> <Config> Location of Environment </Config> </Student> </Iscape> <!—This Iscape Ends -- > <! – Next Iscape starts -- > <Iscape> … … </Iscape> </IscapeCollection> <!—Iscape Collection ends here -- >
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58. Clarke’s Urban Growth Model (UGM) Demonstrates the utility of integrating existing historic maps with remotely sensed data and related geographic information to dynamically map urban land characteristics for large metropolitan areas. San Francisco Bay Area prediction of urban extent in 2100 Domain of Learning – URBAN DYNAMICS
62. Realizing Semantic Information Brokering and Semantic Web in summary Popular Alternative perspective/approach: Linguistics, IR, AI Text Structured Databases Data Syntax, System Federated DB Semi-structured Metadata Structural, Schematic Mediator, Federated IS Visual, Scientific/Eng. Knowledge, Semantic Knowledge Mgmt., Information Brokering, Cooperative IS
Editor's Notes
This is a more formal definition of an iscape. W e say “distributed” because the information to answer the request can lie in different sources.
In the context of digital earth , iscapes serve a very important role. We have developed a framework to specify iscapes declaratively. The primary usage of iscapes is meant by students. Iscapes serves as a ideal platform for students to lean about phenomena as the the requests are preformulated by the administrator and all students need to do is to select parameters and click on the request.
This is the specification framework for an iscape. An iscape can basically consist of 6 components as described below Ontologies serve as the shared vocabulary. Relationships serve as the smantic correlation layer. We could use simulation to demonstrate a concept graphically. Ontology Constraints are constraints that we can specify on the ontologies involved in the iscape. Iscapes can yield a lot of metadata. The presentation serves to filter the metadata. Finally one of the most important components is the student component where a student can configure parameters and learn interactively from the iscape.
This is an example ontology developed for the geographic domain. We shall get back to this topic later.
For e.g., x < y is a relationship that may hold between x and y. We have come across relations like…”equals”, “less_than”, “is_a”, etc
Most of these relations are not powerful enough to correlate complex entities in many common (and natural) domain like geography.
Now, lets take an example to see how we design the “affects” relationship. We see that different components of a volcano can affect different components of the environment. Put together, they can describe completely how volcanoes affect the environment. In this case, lets look at a few example components that affect others. Pyroclastic flows, if they flow across crops, will destroy them. So, we can say, if area of Pyroclastic flows intersect the area of crops, Pyroclastic flows destroy crops. Also, if the ash particles strewn from the volcano disperse into the atmosphere as tiny particles, their size can determine if they have a cooling effect on the atmosphere.
Let us put these sub-relations down into words. We see here that all these follow a specific pattern. [Function (something) operator Function (something else)]. If we generalize this, we can see that FN(x) op FN(y) where x and y are sub-components of A and B ontologies respectively. This schema can be used to define relationships in any domain and examples in 6-7 domains are shown in the thesis report.
Let us first take the case of comparison of locations. When we say location of volcano = location of the environment, we don’t expect to match the exact point of the volcano and the location specified. In general, the volcano’s effects would be felt around a certain area surrounding the volcano. We model this by including a tolerance level within which we match the location points. In this way, we perform a sort of imprecise of fuzzy match and helps us remove geo-spatial inconsistencies. This mapping technique is standardized by the use of enclosing functions and overloading the operator. We have developed mapping functions for the geographic domain and we need only to plug-in any function if we need other functionality.
This is an example of temporal matching. We can find out whether the given volcano had an affect on the environment on the given date. We know that a volcano’s effects like lava flows, etc would continue for a couple of days. We can assume this as tolerance. If the given date falls within this tolerance, we return a successful match. In the case of an earthquake, the time period is in the range of minutes.
.All iscapes and their components are specified using the Extendible Markup Language. Every iscape has an id , name and description. The ontologies involved and the name of the remaining components are then embedded in the iscape.
For example, if the iscape administrator wanted tp specify that the volcano was a stratovolcano, he could specify the name of the constraints within the constraint tags.
Relations have mapping conditions and value conditions. Mapping conditions are functions that you could apply on ontological terms , for example the area function equates the bounding coordinates of two ontologies. Value conditions denote configurable relationship parameters.
This component specifies the actual constraint. Here , we see that the iscape id and constraint name are the same as in the base iscape . This is then followed by the actual constraint specfication.
We can see that several metadata attributes can be included in the result presentation. The presentation layer is needed as we can then filter out the metadata returned by the system as result.
In this component , we encode what parameters can be configured in the iscape. Here we see that , the location of the environment ontology can be configured and the values that this parameter can take are Hawaii and Kileau.
1. Operations are important for the ADEPT system as they lend themselves easily for changing parameters and viewing different results for every set of parameters which are entered by the user. 2. Geography instructors use a lot of simulation models to explain various concepts of geography to their students.
1. A cellular automaton model of urban growth 2. Urbanization, agricultural intensification, resource extraction, and water resources development are examples of human-induced phenomena that have significant impact on people, economy and resources 3. Based on an understanding of the land use changes, it may be possible to understand the impacts associated with them and contribute to a productive national environmental sustainability
This screen shows the student interface to ADEPT. We can see that the ontologies, volcanoes and environment are used here as well as the ontology country. All ontological terms and iscapes along with configurable parameters are embedded in the same screen.