Amit Sheth, "Relationship Web: Trailblazing, Analytics and Computing for Human Experience," Keynote talk at 27th International Conference on Conceptual Modeling (ER 2008) Barcelona, October 20-23 2008.
See associated discussion at:
http://knoesis.org/amit/publications/index.php?page=9
http://knoesis.org/library/resource.php?id=00190
Semantic Web & Information Brokering: Opportunities, Commercialization and Ch...Amit Sheth
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.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...DianaGray10
📣 AI plays a crucial role in the UiPath Business Automation Platform. In this session you will learn about how the UiPath Business Automation Platform is well-suited for AI, the use of LLM and integrations you can use. Topics include the following:
Introductions.
AI powered automations overview.
Discover why the UiPath Business Automation Platform is well-suited for AI.
LLM + Automation framework and integrations with LangChain.
Generative AI Automation Patterns Demonstration.
👨🏽🤝👨🏻 Speakers:
Dhruv Patel, Senior Sales Solution Architect @UiPath
Russel Alfeche, Technology Leader, RPA @qBotica and UiPath MVP
by Lukas Masuch, Henning Muszynski and Benjamin Raethlein
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
https://maison-workshop.com/prof-amit-sheth/
Video: https://youtu.be/pRUXTuxm3as
Keynote at the 7th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2021) – Special Edition on Responsible, August 21, 2021 and is co-located with the 15th International Joint Conference on Artificial Intelligence (IJCAI 2021). Also ASONAM 2021.
With the increasing legalization of medical and recreational use of substances, more research is needed to understand the association between mental health and user behavior related to drug consumption. Specifically, drug overdose and substance use-related mental health issues have become two major topics that have been widely discussed on social media platforms. Big social media data has the potential to provide deeper insights about these associations to public health analysts for making policy decisions. Multiple national population surveys have found that about half of those who experience a mental health illness during their lives will also experience a substance use disorder and vice versa. The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context. Moreover, dark web vendors have been using social media as a new marketplace for drugs. Social media users also discuss the novel drugs emerging in dark web marketplaces and associated side effects/health conditions. These communications get complex when researchers try to annotate them or link them to a specific mental health entity. Considering the significant sensitivity of such communications and to protect user privacy on social media, a potential solution requires reliable algorithms for modeling such communications. We demonstrate the value of incorporating domain-specific knowledge in natural language understanding to identify the relationship between mental health and drug addiction. We discuss end-to-end knowledge-infused deep learning frameworks that leverage the pre-trained language representation model and domain-specific declarative knowledge source to extract entities and their relationships jointly. Our model is further tailored to focus on the entities mentioned in the sentence where ontology is used to locate the target entity’s position. We also demonstrate the capabilities of inclusion of the knowledge-aware representation in association with language models that can extract the Drug-Mental health condition associations.
Acknowledgments: Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Jyotishman Pathak, and collaborators on NIDA/NIH and NSF funded projects on Addiction and Mental Health.
http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression
Semantic Web & Information Brokering: Opportunities, Commercialization and Ch...Amit Sheth
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.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...DianaGray10
📣 AI plays a crucial role in the UiPath Business Automation Platform. In this session you will learn about how the UiPath Business Automation Platform is well-suited for AI, the use of LLM and integrations you can use. Topics include the following:
Introductions.
AI powered automations overview.
Discover why the UiPath Business Automation Platform is well-suited for AI.
LLM + Automation framework and integrations with LangChain.
Generative AI Automation Patterns Demonstration.
👨🏽🤝👨🏻 Speakers:
Dhruv Patel, Senior Sales Solution Architect @UiPath
Russel Alfeche, Technology Leader, RPA @qBotica and UiPath MVP
by Lukas Masuch, Henning Muszynski and Benjamin Raethlein
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
https://maison-workshop.com/prof-amit-sheth/
Video: https://youtu.be/pRUXTuxm3as
Keynote at the 7th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2021) – Special Edition on Responsible, August 21, 2021 and is co-located with the 15th International Joint Conference on Artificial Intelligence (IJCAI 2021). Also ASONAM 2021.
With the increasing legalization of medical and recreational use of substances, more research is needed to understand the association between mental health and user behavior related to drug consumption. Specifically, drug overdose and substance use-related mental health issues have become two major topics that have been widely discussed on social media platforms. Big social media data has the potential to provide deeper insights about these associations to public health analysts for making policy decisions. Multiple national population surveys have found that about half of those who experience a mental health illness during their lives will also experience a substance use disorder and vice versa. The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context. Moreover, dark web vendors have been using social media as a new marketplace for drugs. Social media users also discuss the novel drugs emerging in dark web marketplaces and associated side effects/health conditions. These communications get complex when researchers try to annotate them or link them to a specific mental health entity. Considering the significant sensitivity of such communications and to protect user privacy on social media, a potential solution requires reliable algorithms for modeling such communications. We demonstrate the value of incorporating domain-specific knowledge in natural language understanding to identify the relationship between mental health and drug addiction. We discuss end-to-end knowledge-infused deep learning frameworks that leverage the pre-trained language representation model and domain-specific declarative knowledge source to extract entities and their relationships jointly. Our model is further tailored to focus on the entities mentioned in the sentence where ontology is used to locate the target entity’s position. We also demonstrate the capabilities of inclusion of the knowledge-aware representation in association with language models that can extract the Drug-Mental health condition associations.
Acknowledgments: Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Jyotishman Pathak, and collaborators on NIDA/NIH and NSF funded projects on Addiction and Mental Health.
http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
Certificate in Generative AI issued by Databricks. Topics covered are:
Introducing Generative AI
Finding Success With Generative AI
Assessing Potential Risks and Challenges
A public talk "AI and the Professions of the Future", held on 29 April 2023 in Veliko Tarnovo by Svetlin Nakov. Main topics:
AI is here today --> take attention to it!
- ChatGPT: revolution in language AI
- Playground AI – AI for image generation
AI and the future professions
- AI-replaceable professions
- AI-resistant professions
AI in Education
Ethics in AI
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
Explore the risks and concerns surrounding generative AI in this informative SlideShare presentation. Delve into the key areas of concern, including bias, misinformation, job loss, privacy, control, overreliance, unintended consequences, and environmental impact. Gain valuable insights and examples that highlight the potential challenges associated with generative AI. Discover the importance of responsible use and the need for ethical considerations to navigate the complex landscape of this transformative technology. Expand your understanding of generative AI risks and concerns with this engaging SlideShare presentation.
[DSC Europe 23] Spela Poklukar & Tea Brasanac - Retrieval Augmented GenerationDataScienceConferenc1
Retrieval Augmented Generation (RAG) combines the concepts of semantic search and LLM-based text generation. When a person makes a query in natural language, the query is compared to the entries in the knowledge base and most relevant results are returned to the LLM, which uses this extra information to generate more accurate and reliable response. RAG can therefore limit hallucination and provide accurate responses from reliable source. In this talk, we will present the concept of RAG and underlying concept of semantic search, and present available libraries and vector databases.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
It briefly explains about the role of artificial intelligence in marketing. Also it talks about the future and challenges to be faced.
It also takes through various application of AI in different stages of customer life cycle
Understanding GenAI/LLM and What is Google Offering - Felix GohNUS-ISS
With the recent buzz on Generative AI & Large Language Models, the question is to what extent can these technologies be applied at work or when you're studying and how easy is it to manage/develop your own models? Hear from our guest speaker from Google as he shares some insights into how industries are evolving with these trends and what are some of Google's offerings from Duet AI in Google Workspace to the GenAI App Builder on Google Cloud.
Episode 2: The LLM / GPT / AI Prompt / Data Engineer RoadmapAnant Corporation
In this episode we'll discuss the different flavors of prompt engineering in the LLM/GPT space. According to your skill level you should be able to pick up at any of the following:
Leveling up with GPT
1: Use ChatGPT / GPT Powered Apps
2: Become a Prompt Engineer on ChatGPT/GPT
3: Use GPT API with NoCode Automation, App Builders
4: Create Workflows to Automate Tasks with NoCode
5: Use GPT API with Code, make your own APIs
6: Create Workflows to Automate Tasks with Code
7: Use GPT API with your Data / a Framework
8: Use GPT API with your Data / a Framework to Make your own APIs
9: Create Workflows to Automate Tasks with your Data /a Framework
10: Use Another LLM API other than GPT (Cohere, HuggingFace)
11: Use open source LLM models on your computer
12: Finetune / Build your own models
Series: Using AI / ChatGPT at Work - GPT Automation
Are you a small business owner or web developer interested in leveraging the power of GPT (Generative Pretrained Transformer) technology to enhance your business processes?
If so, Join us for a series of events focused on using GPT in business. Whether you're a small business owner or a web developer, you'll learn how to leverage GPT to improve your workflow and provide better services to your customers.
How can we use generative AI in learning products? A rapid introduction to generative AI. Presented at ED Games Expo 2023 at the U.S. Department of Education, September 22, 2023.
leewayhertz.com-The architecture of Generative AI for enterprises.pdfKristiLBurns
Generative AI is quickly becoming popular among enterprises, with various applications being developed that can change how businesses operate. From code generation to product design and engineering, generative AI impacts a range of enterprise applications.
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
Certificate in Generative AI issued by Databricks. Topics covered are:
Introducing Generative AI
Finding Success With Generative AI
Assessing Potential Risks and Challenges
A public talk "AI and the Professions of the Future", held on 29 April 2023 in Veliko Tarnovo by Svetlin Nakov. Main topics:
AI is here today --> take attention to it!
- ChatGPT: revolution in language AI
- Playground AI – AI for image generation
AI and the future professions
- AI-replaceable professions
- AI-resistant professions
AI in Education
Ethics in AI
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
Explore the risks and concerns surrounding generative AI in this informative SlideShare presentation. Delve into the key areas of concern, including bias, misinformation, job loss, privacy, control, overreliance, unintended consequences, and environmental impact. Gain valuable insights and examples that highlight the potential challenges associated with generative AI. Discover the importance of responsible use and the need for ethical considerations to navigate the complex landscape of this transformative technology. Expand your understanding of generative AI risks and concerns with this engaging SlideShare presentation.
[DSC Europe 23] Spela Poklukar & Tea Brasanac - Retrieval Augmented GenerationDataScienceConferenc1
Retrieval Augmented Generation (RAG) combines the concepts of semantic search and LLM-based text generation. When a person makes a query in natural language, the query is compared to the entries in the knowledge base and most relevant results are returned to the LLM, which uses this extra information to generate more accurate and reliable response. RAG can therefore limit hallucination and provide accurate responses from reliable source. In this talk, we will present the concept of RAG and underlying concept of semantic search, and present available libraries and vector databases.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
It briefly explains about the role of artificial intelligence in marketing. Also it talks about the future and challenges to be faced.
It also takes through various application of AI in different stages of customer life cycle
Understanding GenAI/LLM and What is Google Offering - Felix GohNUS-ISS
With the recent buzz on Generative AI & Large Language Models, the question is to what extent can these technologies be applied at work or when you're studying and how easy is it to manage/develop your own models? Hear from our guest speaker from Google as he shares some insights into how industries are evolving with these trends and what are some of Google's offerings from Duet AI in Google Workspace to the GenAI App Builder on Google Cloud.
Episode 2: The LLM / GPT / AI Prompt / Data Engineer RoadmapAnant Corporation
In this episode we'll discuss the different flavors of prompt engineering in the LLM/GPT space. According to your skill level you should be able to pick up at any of the following:
Leveling up with GPT
1: Use ChatGPT / GPT Powered Apps
2: Become a Prompt Engineer on ChatGPT/GPT
3: Use GPT API with NoCode Automation, App Builders
4: Create Workflows to Automate Tasks with NoCode
5: Use GPT API with Code, make your own APIs
6: Create Workflows to Automate Tasks with Code
7: Use GPT API with your Data / a Framework
8: Use GPT API with your Data / a Framework to Make your own APIs
9: Create Workflows to Automate Tasks with your Data /a Framework
10: Use Another LLM API other than GPT (Cohere, HuggingFace)
11: Use open source LLM models on your computer
12: Finetune / Build your own models
Series: Using AI / ChatGPT at Work - GPT Automation
Are you a small business owner or web developer interested in leveraging the power of GPT (Generative Pretrained Transformer) technology to enhance your business processes?
If so, Join us for a series of events focused on using GPT in business. Whether you're a small business owner or a web developer, you'll learn how to leverage GPT to improve your workflow and provide better services to your customers.
How can we use generative AI in learning products? A rapid introduction to generative AI. Presented at ED Games Expo 2023 at the U.S. Department of Education, September 22, 2023.
leewayhertz.com-The architecture of Generative AI for enterprises.pdfKristiLBurns
Generative AI is quickly becoming popular among enterprises, with various applications being developed that can change how businesses operate. From code generation to product design and engineering, generative AI impacts a range of enterprise applications.
Profiling User Interests on the Social Semantic WebFabrizio Orlandi
Fabrizio Orlandi's PhD Viva @Insight NUI Galway (ex-DERI) - 31/03/2014.
Supervisors: Alexandre Passant and John G. Breslin.
Examiners: Fabien Gandon and Stefan Decker
Semantic Wiki: Social Semantic Web in UseJesse Wang
This is my invited talk on Semantic Wiki to the Key Lab of Intelligent Information Processing at Fudan University in Shanghai during ASWC 2009 when I gave a similar tutorial on semantic mediawiki and applications.
Social Semantic Web on Facebook Open Graph protocol and Twitter AnnotationsMyungjin Lee
This Presentation show what the Social Semantic Web is and how Facebook Open Graph protocol and Twitter Annotations colligate with the Social Semantic Web.
Using narratives in enterprise gamification for sales, training, service and ...Centrical
How using enterprise gamification that is based on narratives - such as car racing, sports, team fantasy sports, song contests and more - helps communicate nuanced goals and drive lasting change in employee behavior.
Even as Google retained the overall PPC Marketing crown in 2015, Bing Ads has risen as a strong contender, Yahoo tried some novel things with its ad platform and Polyvore emerged as a formidable social commerce platform. Learn how these changes should inform your Search Marketing strategy in the year(s) ahead.
Slides to accompany Dr Louise Cooke's workshop session "An introduction to social network analysis" presented at DREaM Event 2.
For more information about the event, please visit http://lisresearch.org/dream-project/dream-event-2-workshop-tuesday-25-october-2011/
From Data Platforms to Dataspaces: Enabling Data Ecosystems for Intelligent S...Edward Curry
Digital transformation is driving a new wave of large-scale datafication in every aspect of our world. Today our society creates data ecosystems where data moves among actors within complex information supply chains that can form around an organization, community, sector, or smart environment. These ecosystems of data can be exploited to transform our world and present new challenges and opportunities in the design of intelligent systems. This talk presents my recent work on using the dataspace paradigm as a best-effort approach to data management within data ecosystems. The talk explores the theoretical foundations and principles of dataspaces and details a set of specialized best-effort techniques and models to enable loose administrative proximity and semantic integration of heterogeneous data sources. Finally, I share my perspectives on future dataspace research challenges, including multimedia data, data governance and the role of dataspaces to enable large-scale data sharing within Europe to power data-driven AI.
Semantic Technologies in Learning EnvironmentsDragan Gasevic
Invited talk delivered in the scope of an open online course: Introduction to Learning and Knowledge Analytics
Details about the course, and the recorded presentation can be found at
http://www.learninganalytics.net/?page_id=71
Open Grid Forum workshop on Social Networks, Semantic Grids and WebNoshir Contractor
Workshop organized by David De Roure at the Open Grid Forum XIX. Other participants included Carole Gobler, Jeremy Frey, Pamela Fox.
January 29, 2007, Chapel Hill, NC
Opening Keynote for Taxonomy Bootcamp. Co-located with Knowledge Management World 2018.
Abstract: Taxonomies and ontologies are seeing a resurgence of interest and usage as Big Data proliferates, machine learning advances, and integration of data becomes more paramount. The previous models of labor-intensive, centralized vocabulary construction and maintenance do not mesh well in today’s interdisciplinary world. Learn about how information professionals can play a starring role in this new world. McGuinness gives a real-world view of building and maintaining large collaborative, interdisciplinary vocabularies along with the data repositories and services they empower, such as the National Institutes of Environmental Health Sciences’ Child Health Exposure Analysis Resource.
http://www.taxonomybootcamp.com/2018/Schedule.aspx
Ontologies are seeing a resurgence of interest and usage as big data proliferates, machine learning advances, and integration of data becomes more paramount. The previous models of sometimes labor-intensive, centralized ontology construction and maintenance do not mesh well in today’s interdisciplinary world that is in the midst of a big data, information extraction, and machine learning explosion. In this talk, we will discuss a model of building and maintaining large collaborative, interdisciplinary ontologies along with the data repositories and data services that they empower. We will also introduce the National Institutes of Environmental Health Science’s Child Health Exposure Analysis Resource and describe how we used our methodology to assemble the broad interdisciplinary ontology that covers exposure science and health and integrates with numerous long standing, well used ontologies. We will also describe how this ontology powers an integrated data resource and provide some examples of how it can be used and re-used for interdisciplinary work. If time permits, we will also describe how the methodology and the integrated ontology has been and is being used in other interdisciplinary health and wellness settings.
This is a version of series of talks given at NCSA-UIUC's director seminar, IBM Almaden, HP Labs, DERI-Galway, City Univ of Dublin, and KMI-Open University during Aug-Oct 2010 (replaces earlier keynote version). It deals with couple of items of the vision outlined at http://bit.ly/4ynB7A
A video of this presentation: http://www.ncsa.illinois.edu/News/Video/2010/sheth.html
Link to this talk as http://bit.ly/CHE-talk
Digital Humanities in a Linked Data World - Semantic Annotations
Dov Winer
1st International Seminar on Digital Humanities
University of Sao Paulo - Brasiliana Mindlin Library
October 2013
Digital Humanities in a Linked Data World - Semnantic AnnotationsDov Winer
Presentation by Dov Winer at the 1st International Seminar on Digital Humanities
University of Sao Paulo, Brazil, 23-25 October 2013
Primeiro Seminario Internacional em Humanidades Digitais,
Universidade Sao Paulo, Biblioteca Brasiliana Mindlin23-25 de Outubro 2013
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachAndre Freitas
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
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.
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.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
A Strategic Approach: GenAI in EducationPeter 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.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Digital Artifact 2 - Investigating Pavilion Designs
Relationship Web: Trailblazing, Analytics and Computing for Human Experience
1. Knowledge Enabled Information and Services Science
Relationship Web:
Trailblazing , Analytics and Computing for Human Experience
27th International Conference on Conceptual Modeling (ER 2008)
Barcelona, Oct 20-23 2008
Amit Sheth
Kno.e.sis Center, Wright State University, Dayton, OH
This talk also represents work of several members of Kno.e.sis team, esp. the
Semantic Discovery and Semantic Sensor Data. http://knoesis.wright.edu
Thanks, K. Godam, C. Henson, M. Perry, C. Ramakrishnan, C. Thomas.
Also thanks to sponsors: NSF (SemDis and STT), NIH, AFRL, IBM, HP, Microsoft.
2. Knowledge Enabled Information and Services Science
Evolution of the Web
2005
1990s
Web of databases
- dynamically generated pages
- web query interfaces
Web of pages
- text, manually created links
- extensive navigation
Web of services
- data = service = data, mashups
- ubiquitous computing
Web of people
- social networks, user-created content
- GeneRIF, Connotea
Web as an oracle / assistant / partner
- “ask to the Web”
- using semantics to leverage
text + data + services + people
2010
Computing for Human Experience
3. Knowledge Enabled Information and Services Science
A Compelling Vision: Trailblazing
“(Human mind works) by association. With one item
in its grasp, it snaps instantly to the next that is
suggested by the association of thoughts, in accordance
with some intricate web of trails carried by the cells of
the brain.“
Dr. Vannevar Bush, As We May Think, 1945
4. Knowledge Enabled Information and Services Science
Keywords, Documents, Objects, Events,
Insight, Experience
“An object by itself is intensely uninteresting”.
– Grady Booch, Object Oriented Design with Applications, 1991
Keywords
|
Search
(data)
Entities
|
Integration
(information)
Relationships,
Events
|
Analysis,
Insight
(knowledge)
5. Knowledge Enabled Information and Services Science
Semantics and Relationships on a
Web scale
Increasing depth and sophistication in dealing with
semantics
–From searching to integration to analysis, insight, discovery
and decision making
–Relationships
• Heart of semantics
• Allows to continue progress from syntax and structure to
semantics
A. Sheth, et al. Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex
Semantic Relationships, in Enhancing the Power of the Internet (Studies in Fuzziness and Soft Computing, V. 139).
SpringerVerlag., 2004. http://knoesis.wright.edu/library/resource.php?id=00190
6. Knowledge Enabled Information and Services Science
Relationship Web takes you away
from “which document” could have
data/information I need, to
interconnecting Web of information
embedded in the resources to give
knowledge, insights and answers
I seek.
Amit P. Sheth, Cartic Ramakrishnan: Relationship Web: Blazing Semantic
Trails between Web Resources. IEEE Internet Computing July 2007.
7. Knowledge Enabled Information and Services Science
Structured text
(biomedical
literature)
Informal Text
(Social Network
chatter)
Multimedia Content
and Web data
Web
Services
Metadata Extraction
Patterns / Inference / Reasoning
Domain
Models
Meta data /
Semantic
Annotations
Relationship Web
Search
Integration
Analysis
Discovery
Question
Answering
8. Knowledge Enabled Information and Services Science
Issues - Relationships
Understanding and modeling relationships
Identifying Relationship (extraction)
Discovering and Exploring Relationships (reasoning)
Hypothesizing and Validating Complex Relationships
Using/exploiting Relationships
for Semantic Applications
(in search, querying, analysis, insight, discovery)
10. Knowledge Enabled Information and Services Science
EventWeb [Jain]
RelationshipWeb [Sheth]
A Web in which each node is an event or object, and
connected to other nodes using
–Linguistic Relationships
–Referential Links: hypertext links to related information
(HREF); links with metadata (MREF), links referring to
model (model reference in SAWSDL, SA-REST)
–Structural Links: showing spatial and temporal relationships
–Causal Links: establishing causality
–Relational Links: giving similarity or any other relationship
Adapted/extended from Ramesh Jain
11. Knowledge Enabled Information and Services Science
Supporting Relationships
in the Real World
Objects and event represent or model real world
– More complex relationships
• Living organisms: Saprotropism, Antagonism,
Exploitation, Predation, Ammensalism or Antibiosis,
Symbiosis
• Relationships between humans: …
12. Knowledge Enabled Information and Services Science
Karthik
Gomadam
Amit
Sheth
Attended
Google IO
Moscone Center, SFO
May 28-29, 2008
Is advised by
Ph.D Student Researcher
is_advised_by
Assistant
Professor
Professor
Research Paper
Journal Conference
Location
published_in published_in
has_location
Image Metadata
Event
Causal
kno.e.sis
DirectsRelational
13. Knowledge Enabled Information and Services Science
Identifying & Representing Relationships
Implicit Relationships
–Statistical representation of interactions between entities,
co-occurrence of terms in the same cluster, tag cloud...
Linking of one document to another via a hyperlink…
–Two documents’ belonging to categories that are siblings in
a concept hierarchy.
14. Knowledge Enabled Information and Services Science
Identifying & Representing Relationships
Explicit Linguistic Relationships
“He beat Randy Johnson”
converted into the triple
Dontrelle WillisbeatRandy Johnson
15. Knowledge Enabled Information and Services Science
Identifying & Representing Relationships
Formal Relationships
–Subsumption, partonomy, ….
Domain specific vs domain-independent relationships
–Example of domain independent relationships: time,
space/location
Complex entity and relationships (example later)
16. Knowledge Enabled Information and Services Science
Why is This a Hard Problem?
Are objects/entities equivalent/equal(same)?
How (well) are they related?
–Implicit vs explicit:
• Statistical representation of interactions between entities, co-
occurrence of terms in the same cluster, tag cloud...
• formal/assertional vs social consensus based
• powerful (beyond FOL): partial, probilistic and fuzzy match
–Degrees of relatedness and relevance: semantic similarity,
semantic proximity, semantic distance, …
• [differentiation, disjointedness]
• related in a “context”
17. Knowledge Enabled Information and Services Science
Why is This a Hard Problem?
Semantic ambiguity
When information (knowledge) is
–Incomplete
– inconsistent
–Approximate
–Even is-a link involves different notions: identify, unity,
essense (Guarino and Wetley 2002)
A.Sheth, et al, “Semantics for The Semantic Web: the Implicit, the Formal and the Powerful,”
International Journal on Semantic Web & Information Systems, 1 (no. 1), 2005, pp. 1–18.
18. Knowledge Enabled Information and Services Science
Faceted Search and Semantic Analytics –
Some Early Work where relationships played key role
InfoHarness/VisualHarness (1993-1998)
http://www.knoesis.org/library/resource.php?id=00275
http://www.knoesis.org/library/resource.php?id=00245
http://knoesis.wright.edu/library/resource.php?id=00267
InfoQuilt (1996-2000)
http://www.knoesis.org/library/resource.php?id=00178
MREF (1996-1998)
OBSERVER (1996-2000)
http://www.knoesis.org/library/resource.php?id=00273
http://www.knoesis.org/library/resource.php?id=00093
Taalee Semantic (Faceted) Search, Semantic Directory
and Semagix Freedom enabled analytics (1999-2006)
http://www.knoesis.org/library/resource.php?id=00193
http://knoesis.wright.edu/library/resource.php?id=00149
19. Knowledge Enabled Information and Services Science
InfoQuilt (1996-2000): Using metadata PatchQuilt and user models/ontologies to
support queries and analytics over globally distributed heterogeneous media repositories
20. Knowledge Enabled Information and Services Science
Physical Link to Relationship
<TITLE> A Scenic Sunset at Lake Tahoe </TITLE>
<p>
Lake Tahoe is a popular tourist spot and
<A HREF = “http://www1.server.edu/lake_tahoe.txt”>
some interesting facts</A> are available here. The scenic
beauty of Lake Tahoe can be viewed in this photograph:
<center>
<IMG SRC=“http://www2.server.edu/lake_tahoe.img”>
</center>
Traditionally, correlation is achieved by using physical links
Done manually by user publishing the HTML document
21. Knowledge Enabled Information and Services Science
MREF (1996-1998)
Metadata Reference Link -- complementing HREF
Creating “logical web” through
Media independent metadata based on correlation
http://www.knoesis.org/library/resource.php?id=00274
http://knoesis.org/library/resource.php?id=00294
22. Knowledge Enabled Information and Services Science
Metadata Reference Link
(<A MREF …>)
<A HREF=“URL”>Document Description</A>
physical link between document (components)
<A MREF KEYWORDS=<list-of-keywords>;
THRESH=<real>>Document Description</A>
<A MREF ATTRIBUTES(<list-of-attribute-value-
pairs>)>Document Description</A>
23. Knowledge Enabled Information and Services Science
Correlation Based on
Content-Based Metadata
Some interesting information on dams is available here
“information on dams” defined by MREF to
keywords and metadata (may be used for a query)
height, width
and size
water.gif (Data)
Metadata Storage
water.gif
……mpeg
……ppm
Major component(RGB)
Blue
Content based Metadata
Content
Dependent
Metadata
24. Knowledge Enabled Information and Services Science
Abstraction Layers
METADATA
DATA
METADATA
DATA
ONTOLOGY
NAMESPACE
ONTOLOGY
NAMESPACE
MREF
in RDF
25. Knowledge Enabled Information and Services Science
MREF (1998)
Model for Logical
Correlation using
Ontological Terms
and Metadata
Framework for
Representing
MREFs
Serialization
(one implementation
choice)
K. Shah and A. Sheth, "Logical Information Modeling of Web-accessible Heterogeneous Digital Assets",
Proc. of the Forum on Research and Technology Advances in Digital Libraries," (ADL'98),
Santa Barbara, CA, May 28-30, 1998, pp. 266-275.
M R E F
R D F
X M L
Figure 3: XML, RDF, and MREF
26. Knowledge Enabled Information and Services Science
An Example RDF Model for MREF (1998)
<?namespace href="http://www.foo.com/IQ" as="IQ"?>
<?namespace href="http://www.w3.org/schemas/rdf-schema" as="RDF"?>
<RDF:serialization>
<RDF:bag id="MREF:12345>
<IQ:keyword>
<RDF:resource id="constraint_001">
<IQ:threshold>0.5</IQ:threshold>
<RDF:PropValue>dam</RDF:PropValue>
</RDF:resource>
</IQ:keyword>
<IQ:attribute>
<RDF:resource id="constraint_002">
<IQ:name>majorRGB</IQ:color>
<IQ:type>string</IQ:type>
<RDF:PropValue>blue</RDF:PropValue>
</RDF:resource>
</IQ:attribute>
</RDF:bag>
</RDF:serialization>
27. Knowledge Enabled Information and Services Science
Domain Specific Correlation
Potential locations for a future shopping mall
identified by all regions having a population greater
than 500 and area greater than 50 sq meters having
an urban land cover and moderate relief <A MREF
ATTRIBUTES(population < 500; area < 50 &
region-type = ‘block’ & land-cover = ‘urban’ & relief
= ‘moderate’)>can be viewed here</A>
28. Knowledge Enabled Information and Services Science
Domain Specific Correlation
=> media-independent relationships between domain
specific metadata: population, area, land cover,
relief
=> correlation between image and structured data at a
higher domain specific level as opposed to physical
“link-chasing” in the WWW
29. Knowledge Enabled Information and Services Science
TIGER/Line DB
Population:
Area:
Boundaries:
Land cover:
Relief:
Census DB
Map DB
Regions
(SQL)
Boundaries
Image Features
(IP routines)
Repositories and the Media Types
31. Knowledge Enabled Information and Services Science
Complex Relationships
Some relationships may not be manually asserted, but
according to statistical analyses of text, experimental
data, etc.
– allow association of provenance data with classes,
instances, relationship types and direct relationships or
statements
32. Knowledge Enabled Information and Services Science
Complex Relationships
Relationships (mappings) are not always simple
mathematical / string transformations
Examples of complex relationships
–Associations / paths between classes
–Graph based / form fitting functions
–Probabilistic relational
E 1 :Reviewer
E 6:Person
E 5 :Person
E 2:Paper
E4 :Paper
E 7 :Submission
E 3 :Person
author _of
author _of
author _of
author _of
author _of
knows
knows
33. Knowledge Enabled Information and Services Science
A Simple Relationship ?
Smoking CancerCauses
Graph based / form fitting functions
34. Knowledge Enabled Information and Services Science
Complex Relationships
Cause-Effects & Knowledge Discovery
VOLCANO
LOCATION
ASH RAIN
PYROCLASTIC
FLOW
ENVIRON.
LOCATION
PEOPLE
WEATHER
PLANT
BUILDING
DESTROYS
COOLS TEMP
DESTROYS
KILLS
35. Knowledge Enabled Information and Services Science
Knowledge Discovery - Example
Earthquake Sources Nuclear Test Sources
Nuclear Test May Cause Earthquakes
Is it really true?
Complex
Relationship:
How do you
model this?
36. Knowledge Enabled Information and Services Science
Complex Relationships – Several
Challenges
–Probabilistic relations
Number of earthquakes with
magnitude > 7 almost constant.
So if at all, then nuclear tests
only cause earthquakes with
magnitude < 7
37. Knowledge Enabled Information and Services Science
Complex Relationships …
For each group of earthquakes with magnitudes in the ranges
5.8-6, 6-7, 7-8, 8-9, and >9 on the Richter scale per year
starting from 1900, find number of earthquakes
Number of earthquakes with
magnitude > 7 almost constant.
So nuclear tests probably only
cause earthquakes with
magnitude < 7
38. Knowledge Enabled Information and Services Science
Inter-Ontological Relationships
A nuclear test could have caused an earthquake if the
earthquake occurred some time after the nuclear test
was conducted and in a nearby region.
NuclearTest Causes Earthquake
<= dateDifference( NuclearTest.eventDate,
Earthquake.eventDate ) < 30
AND distance( NuclearTest.latitude,
NuclearTest.longitude,
Earthquake,latitude,
Earthquake.longitude ) < 10000
39. Knowledge Enabled Information and Services Science
Entity, Relationship, Event Extraction
and Semantic Annotation
From content with structure
–Web pages
–Deep Web
From well-formed text (edited for rules of grammar)
From informal or casual text
–social networking sites
From digital media
41. Knowledge Enabled Information and Services Science
WWW, Enterprise
Repositories
METADATA
EXTRACTORS
Digital Maps
Nexis
UPI
AP
Feeds/
Documents
Digital Audios
Data Stores
Digital Videos
Digital Images
. . .
. . . . . .
Create/extract as much (semantics)
metadata automatically as possible;
Use ontlogies to improve and enhance
extraction
Information Extraction
for Metadata Creation
43. Knowledge Enabled Information and Services Science
Limited tagging
(mostly syntactic)
COMTEX Tagging
Content
‘Enhancement’
Rich Semantic
Metatagging
Value-added Voquette Semantic Tagging
Value-added
relevant metatags
added by Voquette
to existing
COMTEX tags:
• Private companies
• Type of company
• Industry affiliation
• Sector
• Exchange
• Company Execs
• Competitors
Semantic Annotation
(Extraction + Enhancement)
44. Knowledge Enabled Information and Services Science
Enabling powerful linking
of actionable information
and facilitating important
semantic applications
such as knowledge
discovery and link
analysis
(user’s task of manually
retrieving all the information he
needs to know is greatly
minimized; he can spend more
time making effective decisions)
Semantic Metadata Content Tags
Company: Cisco Systems, Inc.
Classification: Channel Partners,
E-Business Solutions
Channel Partner: Siemens Network
Channel Partner: Voyager Network
Channel Partner: Siemens Network
Channel Partner: Wipro Group
E-Business Solution: CIS-1270 Security
E-Business Solution: CIS-320 Learning
E-Business Solution: CIS-6250 Finance
E-Business Solution: CIS-1005 e-Market
Ticker: CSCO
Industry: Telecommunication, . . .
Sector: Computer Hardware
Executive: John Chambers
Competition: Nortel Networks
Syntactic Metadata
Producer: BusinessWire
Source: Bloomberg
Date: Sept. 10 2001
Location: San Jose, CA
URL: http://bloomberg.com/1.htm
Media: Text
XML content item with
enriched semantic tagging,
ready to be queried
E-Business SolutionOntology
Cisco
Systems
Voyager
Network
Siemens
Network
Wipro
Group
Ulysys
Group
CIS-1270
Security
CIS-320
Learning
CIS-6250
Finance
CIS-1005
e-Market
Channel Partner
belongs to
- - -
Ticker
representedby
- - -
- - -
- - -
- - -
Industry
channelpartnerof
- - -
- - -
- - -
- - -
Competition
competes with
provider of
- - -
- - -
- - -
- - -
Executives
w
orks
for
- - -
- - -
- - -
- - -
Sectorbelongsto
Semantic Enhancement
Uniquely
exploiting
real-world
semantic
associations
in the right
context
Semantic
Metadata
Extraction
(also syntactic)
Content Tags
Semantic Metadata
Classification: Channel Partners,
E-Business Solutions
Company: Cisco Systems, Inc.
Syntactic Metadata
Producer: BusinessWire
Source: Bloomberg
Date: Sept. 10 2001
Location: San Jose, CA
URL: http://bloomberg.com/1.htm
Media: Text
Channel
Partners
E-Business
SolutionsClassification
Content Tags
Semantic Metadata
Classification: Channel Partners,
E-Business Solutions
Classification Committee
Knowledge-base, Machine Learning &
Statistical Techniques
Semantic Metadata Enhancement
45. Knowledge Enabled Information and Services Science
Video with
Editorialized
Text on the
WebAuto
Categorization
Semantic Metadata
Automatic Classification & Metadata
Extraction (Web Page)
46. Knowledge Enabled Information and Services Science
Extraction
Agent
Enhanced Metadata AssetWeb Page
Ontology-Directed Metadata Extraction
(Semi-Structured Data)
51. Knowledge Enabled Information and Services Science
CreatingaWebof
relatedinformation
Whatcanacontextdo?
(acommercialperspective)
52. Knowledge Enabled Information and Services Science
Whatelsecanacontextdo?
(acommercialperspective)
SemanticEnrichment
Semantic Targeting
53. Knowledge Enabled Information and Services Science
BLENDED BROWSING & QUERYING INTERFACE
ATTRIBUTE & KEYWORD
QUERYING
uniform view of worldwide
distributed assets of similar
type
SEMANTIC BROWSING
Targeted e-shopping/e-commerce
assets access
VideoAnywhere and Taalee Semantic Querying
and Browsing (1998-2001)
54. Knowledge Enabled Information and Services Science
Semantic/Interactive Targeting (1999-2001)
Buy Al Pacino Videos
Buy Russell Crowe Videos
Buy Christopher Plummer Videos
Buy Diane Venora Videos
Buy Philip Baker Hall Videos
Buy The Insider Video
Precisely targeted through the use of Structured Metadata and integration from multiple sources
55. Knowledge Enabled Information and Services Science
Keyword, Attribute
and Content Based Access
Blended Semantic Browsing and Querying
(Intelligence Analyst Workbench) 2001-2003
56. Knowledge Enabled Information and Services Science
Search for
company
‘Commerce One’
Links to news on companies
that compete against
Commerce One
Links to news on companies
Commerce One competes
against
(To view news on Ariba,
click on the link for Ariba)
Crucial news on
Commerce One’s
competitors (Ariba)
can be accessed easily
and automatically
Semantic Browsing/Directory (2001-….)
57. Knowledge Enabled Information and Services Science
System recognizes ENTITY & CATEGORY
Relevant portion
of the Directory is
automatically
presented.
Semantic Directory
58. Knowledge Enabled Information and Services Science
Users can explore
Semantically related
Information.
Semantic Directory
60. Knowledge Enabled Information and Services Science
Focused relevant
content
organized
by topic
(semantic
categorization)
Automatic Content
Aggregation
from multiple content
providers and feeds
Related relevant
content not explicitly
asked for (semantic
associations)
Competitive
research
inferred
automatically
Automatic
3rd party
content
integration
Semantic Application Example
– Research Dashboard (Voquette/Semagix: 2001-2004)
61. Knowledge Enabled Information and Services Science
Watch list Organizatio
n
Company
Hamas
WorldCom
FBI Watchlist
Ahmed
Yaseer
appears on Watchlist
member of organization
works for Company
Ahmed Yaseer:
• Appears on
Watchlist ‘FBI’
• Works for
Company
‘WorldCom’
• Member of
organization
‘Hamas’
Early Semantic Association: An Application in Risk &
Compliance (Semagix 2004-2006)
62. Knowledge Enabled Information and Services Science
Global Investment Bank
Example of Fraud
prevention application
used in financial services
User will be able to navigate
the ontology using a number
of different interfaces
World Wide
Web content
Public
Records
BLOGS,
RSS
Un-structure text, Semi-structured Data
Watch Lists
Law
Enforcement Regulators
Semi-structured Government Data
Scores the entity
based on the
content and entity
relationships
Establishing
New Account
65. Knowledge Enabled Information and Services Science
A Community’s Pulse is often Informal
•Wealth of information available in blogs, social networks,
chats etc.
•Free medium of self-expression makes mass opinions /
interests available
•Polling for popular culture opinions is easier
•Social Production undeniably affects markets
• geo-specific retail ads, demographic interests in music
66. Knowledge Enabled Information and Services Science
Background Knowledge Improves Content
Analysis
Metadata creation
Example – music comments
Spot Artist, Track names and associated sentiments
Example comment
“Keep your smile on Lil.”
Smile here is a track from Artist Lilly Allen’s album
Background knowledge from Music Brainz taxonomy provides
evidence
Annotate ‘smile’ as Track
‘Lil’ as Lilly Allen
Background knowledge from Urban Dictionary for understanding
Slang
I say: “Your music is wicked”
What I really mean: “Your music is good”
67. Knowledge Enabled Information and Services Science
Results - Pulse of a Music Community
•Mining artist popularity from chatter on MySpace
- Lists close to listeners preferences vs. Bill Boards
BB
User Comments:
May 07
User Comments:
Jun 07
Rihanna
Biffy Clyro
Twang
Maroon 5
McCartney
Winehouse
Rascal
Rihanna
Winehouse
Maroon 5
Mccartney
Biffy Clyro
Twang
Rascal
Rihanna
Winehouse
Maroon 5
Mccartney
Biffy Clyro
Rascal
Twang
68. Knowledge Enabled Information and Services Science
830.9570 194.9604 2
580.2985 0.3592
688.3214 0.2526
779.4759 38.4939
784.3607 21.7736
1543.7476 1.3822
1544.7595 2.9977
1562.8113 37.4790
1660.7776 476.5043
parent ion m/z
fragment ion m/z
ms/ms peaklist data
fragment ion
abundance
parent ion
abundance
parent ion charge
Mass Spectrometry (MS) Data
Semantic Extraction/Annotation of
Experimental Data
71. Knowledge Enabled Information and Services Science 72
Person
Company
Coordinates
Coordinate System
Time Units
Timezone
Spatial
Ontology
Domain
Ontology
Temporal
Ontology
Mike Botts, "SensorML and Sensor Web Enablement,"
Earth System Science Center, UAB Huntsville
Semantic Sensor ML – Adding Ontological
Metadata
72. Knowledge Enabled Information and Services Science 73
Semantic Query
Semantic Temporal Query
Model-references from SML to OWL-Time ontology concepts provides the ability
to perform semantic temporal queries
Supported semantic query operators include:
– contains: user-specified interval falls wholly within a sensor reading
interval (also called inside)
– within: sensor reading interval falls wholly within the user-specified interval
(inverse of contains or inside)
– overlaps: user-specified interval overlaps the sensor reading interval
Example SPARQL query defining the temporal operator ‘within’
73. Knowledge Enabled Information and Services Science
demo of Semantic Sensor Web
http://wiki.knoesis.org/index.php/SSW
74. Knowledge Enabled Information and Services Science
Relationship/Fact Extraction from Text
Knowledge Engineering approach
–Manually crafted rules
• Over lexical items <person> works for <organization>
• Over syntactic structures – parse trees
–GATE
75. Knowledge Enabled Information and Services Science
Relationship/Fact Extraction from Text
Machine learning approaches
–Supervised
–Semi-supervised
–Unsupervised
76. Knowledge Enabled Information and Services Science
Schema-Driven Extraction of
Relationships from Biomedical Text
Cartic Ramakrishnan, Krys Kochut, Amit P. Sheth: A Framework for Schema-
Driven Relationship Discovery from Unstructured Text. International Semantic
Web Conference 2006: 583-596 [.pdf]
77. Knowledge Enabled Information and Services Science
Method – Parse Sentences
in PubMed
SS-Tagger (University of Tokyo)
SS-Parser (University of Tokyo)
(TOP (S (NP (NP (DT An) (JJ excessive) (ADJP (JJ endogenous) (CC or)
(JJ exogenous) ) (NN stimulation) ) (PP (IN by) (NP (NN estrogen) ) ) )
(VP (VBZ induces) (NP (NP (JJ adenomatous) (NN hyperplasia) ) (PP
(IN of) (NP (DT the) (NN endometrium) ) ) ) ) ) )
• Entities (MeSH terms) in sentences occur in modified forms
• “adenomatous” modifies “hyperplasia”
• “An excessive endogenous or exogenous stimulation” modifies “estrogen”
• Entities can also occur as composites of 2 or more other entities
• “adenomatous hyperplasia” and “endometrium” occur as “adenomatous
hyperplasia of the endometrium”
78. Knowledge Enabled Information and Services Science
Method – Identify entities and
Relationships in Parse Tree
TOP
NP
VP
S
NP
VBZ
induces
NP
PP
NP
IN
of
DT
the
NN
endometrium
JJ
adenomatous
NN
hyperplasia
NP PP
IN
by
NN
estrogenDT
the
JJ
excessive ADJP NN
stimulation
JJ
endogenous
JJ
exogenous
CC
or
Modifiers
Modified entities
Composite Entities
79. Knowledge Enabled Information and Services Science
Resulting Semantic Web Data in RDF
Modifiers
Modified entities
Composite Entities
estrogen
An excessive
endogenous or
exogenous stimulation
modified_entity1
composite_entity1
modified_entity2
adenomatous hyperplasia
endometrium
hasModifier
hasPart
induces
hasPart
hasPart
hasModifier
hasPart
80. Knowledge Enabled Information and Services Science
Blazing Semantic Trails in Biomedical
Literature
Cartic Ramakrishnan, Extracting, Representing and Mining Semantic Metadata
from Text: Facilitating Knowledge Discovery in Biomedicine, PhD Thesis,
Wright State University, August 2008.
Amit Sheth and Cartic Ramakrishnan, “Relationship Web: Blazing Semantic
Trails between Web Resources,” IEEE Internet Computing, July–August 2007,
pp. 84–88.
81. Knowledge Enabled Information and Services Science
Relationships -- Blazing the Trails
“The physician, puzzled by her patient's reactions, strikes the
trail established in studying an earlier similar case, and runs
rapidly through analogous case histories, with side references
to the classics for the pertinent anatomy and histology.
The chemist, struggling with the synthesis of an organic
compound, has all the chemical literature before him in his
laboratory, with trails following the analogies of compounds,
and side trails to their physical and chemical behavior.”
[V. Bush, As We May Think. The Atlantic Monthly, 1945. 176(1):
p. 101-108. ]
86. Knowledge Enabled Information and Services Science
“Everything's connected, all along the line.
Cause and effect.
That's the beauty of it.
Our job is to trace the connections and reveal them.”
Jack in Terry Gilliam’s 1985 film - “Brazil”
87. How are Harry Potter and
Dan Brown related?
Leonardo Da Vinci
The Da Vinci code
The Louvre
Victor Hugo
The Vitruvian man
Santa Maria delle
Grazie
Et in Arcadia Ego
Holy Blood, Holy Grail
Harry Potter
The Last Supper
Nicolas Poussin
Priory of Sion
The Hunchback of
Notre Dame
The Mona Lisa
Nicolas Flammel
painted_by
painted_by
painted_by
painted_by
member_of
member_of
member_of
written_by
mentioned_in
mentioned_in
displayed_at
displayed_at
cryptic_motto_of
displayed_at
mentioned_in
mentioned_in
How are Harry Potter and Dan Brown related?
88. Knowledge Enabled Information and Services Science
Semantic Trails Over All Types of Data
Semantic Trails can be built over a Web of Semantic
(Meta)Data extracted (manually, semi-automatically
and automatically) and gleaned from
–Structured data (e.g., NCBI databases)
–Semi-structured data (e.g., XML based and semantic
metadata standards for domain specific data representations
and exchanges)
–Unstructured data (e.g., Pubmed and other biomedical
literature)
and
–Various modalities (experimental data, medical images, etc.)
89. Knowledge Enabled Information and Services Science
Discovering Complex Connection Patterns
Discovering informative subgraphs
Given a pair of end-points (entities) produce a subgraph with
relationships connecting them such that the subgraph is small
enough to be visualized and contains relevant “interesting”
connections
Cartic Ramakrishnan, William H. Milnor, Matthew Perry, Amit P. Sheth. (2005).
Discovering informative connection subgraphs in multi-relational graphs. SIGKDD Explorations, 7(2), pp.
56-63.
90. Knowledge Enabled Information and Services Science
Discovering Complex Connection Patterns
We defined an interestingness measure based on the
ontology schema
–In future biomedical domain the scientist will control this
with the help of a browsable ontology
–Our interestingness measure takes into account
• Specificity of the relationships and entity classes involved
• Rarity of relationships etc.
Cartic Ramakrishnan, William H. Milnor, Matthew Perry, Amit P. Sheth. (2005).
Discovering informative connection subgraphs in multi-relational graphs. SIGKDD
Explorations, 7(2), pp. 56-63.
92. Knowledge Enabled Information and Services Science
Discovery Algorithm
• Bidirectional lock-step growth from S and T
• Choice of next node based on interestingness measure
• Stop when there are enough connections between
the frontiers
• This is treated as the candidate graph
93. Knowledge Enabled Information and Services Science
Discovery Algorithm
Model the Candidate graph as an electrical circuit
–S is the source and T the sink
–Edge weight derived from the ontology schema are treated as
conductance values
–Using Ohm’s and Kirchoff’s laws we find maximum current
flow paths through the candidate graph from S to T
–At each step adding this path to the output graph to be
displayed we repeat this process till a certain number of
predefined nodes is reached
94. Knowledge Enabled Information and Services Science
Discovery Algorithm
Results
–Arnold Schwarzenegger, Edward Kennedy
Other related work
–Semantic Associations
97. Knowledge Enabled Information and Services Science
Spatial
Temporal
Thematic
Events: 3 Dimensions – Spatial, Temporal
and Thematic
98. Knowledge Enabled Information and Services Science
Events and STT dimensions
Powerful mechanism to integrate content
–Describes the Real-World occurrences
–Can have video, images, text, audio all of the same event
–Search and Index based on events and STT relations
99. Knowledge Enabled Information and Services Science
Events and STT dimensions
Many relationship types
Spatial:
–What events happened near this event?
–What entities/organizations are located nearby?
Temporal:
–What events happened before/after/during this event?
Thematic:
–What is happening?
–Who is involved?
100. Knowledge Enabled Information and Services Science
Events and STT dimensions
Going further
Can we use
–What? Where? When? Who?
To answer
–Why? / How?
Use integrated STT analysis to explore cause and effect
101. Knowledge Enabled Information and Services Science 102
High-level Sensor (S-H)
Low-level Sensor (S-L)
A-H E-H
A-L E-L
H L
• How do we determine if A-H = A-L? (Same
time? Same place?)
• How do we determine if E-H = E-L? (Same
entity?)
• How do we determine if E-H or E-L
constitutes a threat?
Example Scenario: Sensor Data Fusion and
Analysis
102. Knowledge Enabled Information and Services Science 103
Sensor Data Pyramid
Raw Sensor (Phenomenological) Data
Feature Metadata
Entity Metadata
Relationship
Metadata
Data
Information
Semantics/Understanding/I
nsight
Data Pyramid
103. Knowledge Enabled Information and Services Science 104
Collection
Feature Extraction
Entity Detection
RDF
KB
Semantic Analysis
Data
• Raw Phenomenological
Data
TML
Fusion
SML-S
SML-S
SML-S
Ontologies
Information
• Entity Metadata
• Feature Metadata
Knowledge
• Object-Event Relations
• Spatiotemporal
Associations
• Provenance Pathways
Sensors (RF, EO, IR, HIS, acoustic)
• Object-Event
Ontology
• Space-Time
Ontology
Analysis
Processes
Annotation
Processes
O&M
Sensor Data Architecture
104. Knowledge Enabled Information and Services Science
Current Research Towards STT
Relationship Analysis
Modeling Spatial and Temporal data using SW
standards (RDF(S))1
–Upper-level ontology integrating thematic and spatial
dimensions
–Use Temporal RDF3 to encode temporal properties of
relationships
–Demonstrate expressiveness with various query operators
built upon thematic contexts
1. Matthew Perry, Farshad Hakimpour, Amit Sheth. "Analyzing Theme, Space and Time: An Ontology-based Approach", Fourteenth
International Symposium on Advances in Geographic Information Systems (ACM-GIS '06), Arlington, VA, November 10 - 11, 2006
2. Matthew Perry, Amit Sheth, Farshad Hakimpour, Prateek Jain. “Supporting Complex Thematic, Spatial and Temporal Queries
over Semantic Web Data", Second International Conference on Geospatial Semantics (GeoS ‘07), Mexico City, MX, November 29 –
30, 2007
3. Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”, ESWC 2005: 93-107
105. Knowledge Enabled Information and Services Science
Current Research Towards STT
Relationship Analysis
Graph Pattern queries over spatial and temporal RDF
data2
–Extended ORDBMS to store and query spatial and temporal
RDF
–User-defined functions for graph pattern queries involving
spatial variables and spatial and temporal predicates
–Implementation of temporal RDFS inferencing
1. Matthew Perry, Farshad Hakimpour, Amit Sheth. "Analyzing Theme, Space and Time: An Ontology-based Approach", Fourteenth
International Symposium on Advances in Geographic Information Systems (ACM-GIS '06), Arlington, VA, November 10 - 11, 2006
2. Matthew Perry, Amit Sheth, Farshad Hakimpour, Prateek Jain. “Supporting Complex Thematic, Spatial and Temporal Queries
over Semantic Web Data", Second International Conference on Geospatial Semantics (GeoS ‘07), Mexico City, MX, November 29 –
30, 2007
3. Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”, ESWC 2005: 93-107
106. Knowledge Enabled Information and Services Science
Upper-Level Ontology Modeling Theme
and Space
Occurrent
Continuant
Named_PlaceSpatial_Occurrent
Dynamic_Entity
Spatial_Region
Occurrent: Events – happen and then don’t exist
Continuant: Concrete and Abstract Entities – persist over time
Named_Place: Those entities with static spatial behavior (e.g. building)
Dynamic_Entity: Those entities with dynamic spatial behavior (e.g. person)
Spatial_Occurrent: Events with concrete spatial locations (e.g. a speech)
Spatial_Region: Records exact spatial location (geometry objects,
coordinate system info)
occurred_at located_at
occurred_at: Links Spatial_Occurents to their geographic locations
located_at: Links Named_Places to their geographic locations
rdfs:subClassOf
property
Spatio-Temporal-Thematic Query Processing @ Kno.e.sis
107. Knowledge Enabled Information and Services Science
Occurrent
Continuant
Named_Place
Spatial_Occurrent
Dynamic_Entity
Person
City
Politician
Soldier
Military_Unit
Battle
Vehicle
Bombing
Speech
Military_Event
assigned_to
on_crew_of
used_in
gives
participates_in
trains_at
Spatial_Region
located_at occurred_at
Upper-level Ontology
Domain Ontology
rdfs:subClassOf used for integration
rdfs:subClassOf
relationship type
108. Knowledge Enabled Information and Services Science
Temporal RDF Graph: Platoon Membership
E1:Soldier
E3:Platoon E5:Soldier
E2:Platoon
E4:Soldier
assigned_to [1, 10]
assigned_to [11, 20]
assigned_to [5, 15]
assigned_to [5, 15]
E1 is assigned to E2 from time 1 to 10 and then
assigned to E3 from time 11 to 20
Also need to handle inferencing:
(x rdf:type Grad_Student):[2004, 2006] AND
(x rdf:type Undergrad_Student):[2000, 2004]
(x rdf:type Student):[2000, 2006]
Time interval represents
valid time of the
relationship
109. Knowledge Enabled Information and Services Science
ORDBMS Implementation: DB Structures
Unlike thematic relationships which are explicitly stated in the RDF
graph, many spatial and temporal relationships (e.g., distance) are
implicit and require additional computation
110. Knowledge Enabled Information and Services Science
Sample STT Query
Scenario (Biochemical Threat Detection): Analysts must
examine soldiers’ symptoms to detect possible biochemical
attack
Query specifies
a relationship between a soldier, a chemical agent and a battle
location (graph pattern 1)
a relationship between members of an enemy organization and
their known locations (graph pattern 2)
a spatial filtering condition based on the proximity of the soldier
and the enemy group in this context (spatial Constraint)
113. Knowledge Enabled Information and Services Science
TECHNOLOGY THAT FITS RIGHT IN
“The most profound technologies are those that
disappear. They weave themselves into the fabric
of everyday life until they are indistinguishable
from it.
Machines that fit the human environment instead
of forcing humans to enter theirs will make using
a computer as refreshing as a walk in the woods.”
Mark Weiser, The Computer for the 21st Century (Ubicomp vision)
114
Get citation
117. Knowledge Enabled Information and Services Science
with this
Latitude: 38° 57’36” N
Longitude: 95° 15’12” W
Date: 10-9-2007
Time: 1345h
118. Knowledge Enabled Information and Services Science
that is sent to
Geocoder
Farm Helper
Services Resource
Sensor Data Resource
Structured Data Resource
Agri DB
Soil
Survey
Lat-Long
Soil
Information
Pest
information
…
Weather
Resource
Location
Date
/Time
Weathe
r Data
124. Knowledge Enabled Information and Services Science
Computing For Human Experience
Prof. Amit P. Sheth,
LexisNexis Eminent Scholar and
Director, kno.e.sis center
Wright State University
http://knoesis.org
Editor's Notes
phrase “he beat Randy Johnson” in the first paragraph, after being passed through a relationship extraction [12] and pronominal resolution [14] engine, can be converted into the triple Dontrelle WillisbeatRandy Johnson. The notions of Model and Model Theoretic Semantics: Expressions in a formal language are interpreted in models. The structure common to all models in which a given language is interpreted (the model structure for the model-theoretic interpretation of the given language) reflects certain basic presuppositions about the “structure of the world” that are implicit in the language. The Principle of Compositionality: The meaning of an expression is a function of the meanings of its parts and the way they are syntactically combined. In other words, the semantics of an expression are computed using the semantics of its parts, obtained using an interpretation function.
phrase “he beat Randy Johnson” in the first paragraph, after being passed through a relationship extraction [12] and pronominal resolution [14] engine, can be converted into the triple Dontrelle WillisbeatRandy Johnson. The notions of Model and Model Theoretic Semantics: Expressions in a formal language are interpreted in models. The structure common to all models in which a given language is interpreted (the model structure for the model-theoretic interpretation of the given language) reflects certain basic presuppositions about the “structure of the world” that are implicit in the language. The Principle of Compositionality: The meaning of an expression is a function of the meanings of its parts and the way they are syntactically combined. In other words, the semantics of an expression are computed using the semantics of its parts, obtained using an interpretation function.
What is deep web ?Example for well formed text ?
Animation needs work
Animation needed
Going from syntactic to semantic metadata – Semantic Sensor MLInsert model references into tagsDomain Ontology e.g. people, companiesSpatial Ontology e.g. coordinate systems and coordinatesTemporal Ontology e.g. time units, time zones
Example Scenario – Sensor WorkDifferent sensors capture different aspects of the same entities and eventsChallenges for entity identification and resolution – being worked on by our colleaguesAfter these steps – more detailed STT analysis e.g. is a vehicle a threat?
Realizing this scenario – Different types of data/metadata needed
Overall big picture.SML + ontologies large RDF KBNow can do STT analytics over this large graph semantically integrating the content
Weiser’s human-centered vision of ubiquitous computing… new mode ofhuman–computer interaction.