The document provides an overview of semantic technologies and discusses their increasing mainstream adoption. It notes that Microsoft purchased Powerset in 2008, Apple purchased Siri in 2010, and Google bought Metaweb and released semantic search in 2013. It discusses how semantic technologies allow for interoperability through shared representations and reasoning. Examples are given of early semantic search applications from 1999-2002 and an operational semantic electronic medical record application deployed in 2006.
Semantic Web in Action: Ontology-driven information search, integration and a...Amit Sheth
Amit Sheth's Keynote talk given at: “Semantic Web in Action: Ontology-driven information search, integration and analysis,” Net Object Days 2003 and MATES03, Erfurt, Germany, September 23, 2003. http://knoesis.org
Note: slides 51-55 have audio.
Semantic Web in Action: Ontology-driven information search, integration and a...Amit Sheth
Amit Sheth's Keynote talk given at: “Semantic Web in Action: Ontology-driven information search, integration and analysis,” Net Object Days 2003 and MATES03, Erfurt, Germany, September 23, 2003. http://knoesis.org
Note: slides 51-55 have audio.
Towards digitizing scholarly communicationSören Auer
Slides of the VIVO 2016 Conference keynote: Despite the availability of ubiquitous connectivity and information technology, scholarly communication has not changed much in the last hundred years: research findings are still encoded in and decoded from linear, static articles and the possibilities of digitization are rarely used. In this talk, we will discuss strategies for digitizing scholarly communication. This comprises in particular: the use of machine-readable, dynamic content; the description and interlinking of research artifacts using Linked Data; the crowd-sourcing of multilingual
educational and learning content. We discuss the relation of these developments to research information systems and how they could become part of an open ecosystem for scholarly communication.
This invited keynote at the Social Computing Track at WI-IAT21 gives an introduction to Knowledge Graphs and how they are built collaboratively by us. It gives also presents a brief analysis of the links in Wikidata.
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...Khirulnizam Abd Rahman
Application of Ontology in Semantic Information Retrieval
by Prof Shahrul Azman from FSTM, UKM
Presentation for MyREN Seminar 2014
Berjaya Hotel, Kuala Lumpur
27 November 2014
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment Paris Sud University
Today, we are experiencing an unprecedented production of resources, published as Linked Open Data (LOD, for short). This is leading to the creation of knowledge graphs (KGs) containing billions of RDF (Resource Description Framework) triples, such as DBpedia, YAGO and Wikidata on the academic side, and the Google Knowledge Graph or Microsoft’s Satori graph on the commercial side. These KGs contain millions of entities (such as people, proteins, or books), and millions of facts about them. This knowledge is typically expressed in RDF (Resource Description Framework), i.e., as triples of the form ⟨Macron, presidentOf, France⟩. Some KGs provide an ontology expressed in OWL2 (Web Ontology Language), which describes the vocabulary (the classes and properties) for the RDF facts. However, to exploit and take benefits from the richness of this available data and knowledge, several problems have to be faced, namely, data linking, data fusion and knowledge discovery, when data is of big volume, heterogeneous and evolving. In this tutorial we will first give an overview of exiting data linking and key discovery approaches. Then, we will discuss the problem of identity crisis caused by the misuse of owl:sameAs predicate and give some possible solutions. We will finish by highlighting some current challenges in this research area.
Forms part of a training course in ontology given in Buffalo in 2009. For details and accompanying video see http://ontology.buffalo.edu/smith/IntroOntology_Course.html
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Linked data for Enterprise Data IntegrationSören Auer
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
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?
Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.
Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of traffic related incident reports from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner.
We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.
Towards digitizing scholarly communicationSören Auer
Slides of the VIVO 2016 Conference keynote: Despite the availability of ubiquitous connectivity and information technology, scholarly communication has not changed much in the last hundred years: research findings are still encoded in and decoded from linear, static articles and the possibilities of digitization are rarely used. In this talk, we will discuss strategies for digitizing scholarly communication. This comprises in particular: the use of machine-readable, dynamic content; the description and interlinking of research artifacts using Linked Data; the crowd-sourcing of multilingual
educational and learning content. We discuss the relation of these developments to research information systems and how they could become part of an open ecosystem for scholarly communication.
This invited keynote at the Social Computing Track at WI-IAT21 gives an introduction to Knowledge Graphs and how they are built collaboratively by us. It gives also presents a brief analysis of the links in Wikidata.
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...Khirulnizam Abd Rahman
Application of Ontology in Semantic Information Retrieval
by Prof Shahrul Azman from FSTM, UKM
Presentation for MyREN Seminar 2014
Berjaya Hotel, Kuala Lumpur
27 November 2014
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment Paris Sud University
Today, we are experiencing an unprecedented production of resources, published as Linked Open Data (LOD, for short). This is leading to the creation of knowledge graphs (KGs) containing billions of RDF (Resource Description Framework) triples, such as DBpedia, YAGO and Wikidata on the academic side, and the Google Knowledge Graph or Microsoft’s Satori graph on the commercial side. These KGs contain millions of entities (such as people, proteins, or books), and millions of facts about them. This knowledge is typically expressed in RDF (Resource Description Framework), i.e., as triples of the form ⟨Macron, presidentOf, France⟩. Some KGs provide an ontology expressed in OWL2 (Web Ontology Language), which describes the vocabulary (the classes and properties) for the RDF facts. However, to exploit and take benefits from the richness of this available data and knowledge, several problems have to be faced, namely, data linking, data fusion and knowledge discovery, when data is of big volume, heterogeneous and evolving. In this tutorial we will first give an overview of exiting data linking and key discovery approaches. Then, we will discuss the problem of identity crisis caused by the misuse of owl:sameAs predicate and give some possible solutions. We will finish by highlighting some current challenges in this research area.
Forms part of a training course in ontology given in Buffalo in 2009. For details and accompanying video see http://ontology.buffalo.edu/smith/IntroOntology_Course.html
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Linked data for Enterprise Data IntegrationSören Auer
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
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?
Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.
Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of traffic related incident reports from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner.
We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.
Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data analysis comprising data collected from real patients highlighting the challenges in deploying such an application. The results show strong promise to derive actionable information using a combination of physiological indicators from active and passive sensors that can help doctors determine more precisely the cause, severity, and control level of asthma. Information synthesized from kHealth can be used to alert patients and caregivers for seeking timely clinical assistance to better manage asthma and improve their quality of life.
Paper: http://www.knoesis.org/library/resource.php?id=2153
Citation:
Pramod Anantharam, Tanvi Banerjee, Amit Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan, Shalini G. Forbis, Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children , IEEE 4th International Conference on Mobile Services, June 27 - July 2, 2015, New York, USA.
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
Technologies and Systems (CTS 2015), June 2015
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCS
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
A review of the state of the art in Machine Learning on the Semantic WebSimon Price
Paper presentation at UK Computation Intelligence workshop 2003, Bristol. This paper reviews the current state of the art of machine learning applied to the Semantic Web. It looks at the Semantic Web and its languages, including RDF and OWL, from a machine learning perspective. Trends in the Semantic Web are mentioned throughout and the relationship with Web Services is examined. Applications are discussed with recent examples and pointers to data sets. Finally, the emerging field of Semantic Web Mining is introduced.
Tutorial presented at 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012), January 28-30, 2012. http://sites.google.com/site/web2011ihi/participants/tutorials
This tutorial weaves together three themes and the associated topics:
[1] The role of biomedical ontologies
[2] Key Semantic Web technologies with focus on Semantic provenance and integration
[3] In-practice tools and real world use cases built to serve the needs of sleep medicine researchers, cardiologists involved in clinical practice, and work on vaccine development for human pathogens.
Talk about Exploring the Semantic Web, and particularly Linked Data, and the Rhizomer approach. Presented August 14th 2012 at the SRI AIC Seminar Series, Menlo Park, CA
These slides were presented as part of a W3C tutorial at the CSHALS 2010 conference (http://www.iscb.org/cshals2010). The slides are adapted from a longer introduction to the Semantic Web available at http://www.slideshare.net/LeeFeigenbaum/semantic-web-landscape-2009 .
A PDF version of the slides is available at http://thefigtrees.net/lee/sw/cshals/cshals-w3c-semantic-web-tutorial.pdf .
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
The Semantic Web is a vision of information that is understandable by computers. Although there is great exploitable potential, we are still in "Generation Zero'' of the Semantic Web, since there are few real-world compelling applications. The heterogeneity, the volume of data and the lack of standards are problems that could be addressed through some nature inspired methods. The paper presents the most important aspects of the Semantic Web, as well as its biggest issues; it then describes some methods inspired from nature - genetic algorithms, artificial neural networks, swarm intelligence, and the way these techniques can be used to deal with Semantic Web problems.
Given at the annual Open Universiteit Informatics faculty research meeting on March 6, 2012. Video is at http://video.intranet.ou.nl/mediadienst/_website/php/external_video.php?Q=1056|videoID
morning session talk at the second Keystone Training School "Keyword search in Big Linked Data" held in Santiago de Compostela.
https://eventos.citius.usc.es/keystone.school/
Integrating a Domain Ontology Development Environment and an Ontology Search ...Takeshi Morita
In order to reduce the cost of building domain ontologies manually, in this paper, we propose a method and a tool named DODDLE-OWL for domain ontology construction reusing texts and existing ontologies extracted by an ontology search engine: Swoogle. In the experimental evaluation, we applied the method to a particular field of law and evaluated the acquired ontologies.
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.
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.
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.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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.
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
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
Semantic Web: introduction & overview
1. 1
Semantic Web: intro & overview
A conversation with students – 1 Sept 2015
Amit Sheth http://knoesis.org/amit
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, OH, USA
2. What are the most important
recent software/Internet
success stories?
5. Semantic technologies in the mainstream
• Microsoft purchased Powerset in 2008
• Apple purchased Siri [Apr 2010]
– “Once Again The Back Story Is About Semantic Web”
• Google buys Metaweb [June 2010]...” Google Snaps
Up Metaweb in Semantic Web Play” and releases Semantic search in 2013
– Now see: “Google Knowledge Graph Could Change Search Forever”
• Facebook OpenGraph, Twitter annotation
…”another example of semantic web going mainstream”
“Google, Twitter and Facebook build the semantic web”
5
6. • RDFa adoption ….Search engines (esp Bing)
started using domain models and (all) use of
background knowledge/structured databases
with large entity bases (these are part of
Knowledge Graph and equivalent)
• Bing, Yahoo! and Google are using schema.org
in a big way
7. A bit of history
• Semantics with metadata and ontologies for heterogeneous
documents and multiple repositories of data including the
Web was discussed in 1990s (semantic information brokering,
faceted search, InfoHarness, SIMS, Ariadne, OBSERVER, SHOE,
MREF, InfoQuilt, …). Also DAML and OIL.
• Tim Berners-Lee used “Semantic Web” in his 1999 book
• I had founded a company Taalee in 1999, gave a keynote on
Semantic Web & commercialization in 2000 and filed for a
patent in 2000 (awarded 2001).
• Well known TBL, Hendler, Lassila paper in Scientific American
took AI-ish approach (agents,…) to Semantic Web
• First 5 years saw too much of AI/DL, but more
practical/applied work has dominated recently
8. Different foci
• TBL – focus on data: Data Web (“In a way, the Semantic
Web is a bit like having all the databases out there as one
big database.”)
• Others focus on reasoning and intelligent processing
• But the biggest current use seems to be about Search:
– 15 years of Semantic Search and Ontology-enabled Semantic
Applications
10. 1
• Ontology: Agreement with a common
vocabulary/nomenclature, conceptual models
and domain Knowledge
• Schema + Knowledge base
• Agreement is what enables interoperability
• Formal description - Machine processability is
what leads to automation
11. 2
• Semantic Annotation (Metadata Extraction):
Associating meaning with data, or labeling
data so it is more meaningful to the system
and people.
• Can be manual, semi-automatic (automatic
with human verification), automatic.
12. From Syntax to Semantics
Shallow semantics
Deep semantics
Changing Focus on Interoperability in Information Systems: From System, Syntax, Structure to Semantics
13. SSN
Ontology
2 Interpreted data
(deductive)
[in OWL]
e.g., threshold
1 Annotated Data
[in RDF]
e.g., label
0 Raw Data
[in TEXT]
e.g., number
3 Interpreted data
(abductive)
[in OWL]
e.g., diagnosis
Intellego
“150”
Systolic blood pressure of 150 mmHg
Elevated
Blood
Pressure
Hyperthyroidism
……
13
Levels of Abstraction
15. Semantic Web Stack
• Web of Linked Data
• Introduced by Berners Lee
et. al as next step for
Web of Documents
• Allow “machine
understanding” of data,
• Create “common” models
of domains using formal
language - ontologies
Layer cake image source: http://www.w3.org; see W3C SW publications
Semantic Web Layer Cake
16. Characteristics of Semantic Web
16
Self
Describing
Machine &
Human
Readable
Issued by
a Trusted
Authority
Easy to
Understand
Convertible
Can be
Secured
The Semantic Web:
XML, RDF & Ontology
Adapted from William Ruh (CISCO)
17. • Resource Description Framework – Recommended by
W3C for metadata modeling [RDF]
• A standard common modeling framework – usable by
humans and machine understandable
Resource Description Framework
IBM
Armonk, New York,
United States
Zurich, Switzerland
Location
Company
RDF/OWL slides From: Semantic Web in Health Informatics (thanks: Satya)
18. • RDF Triple
o Subject: The resource that the triple is about
o Predicate: The property of the subject that is described by the triple
o Object: The value of the property
• Web Addressable Resource: Uniform Resource Locator (URL),
Uniform Resource Identifier (URI), Internationalized Resource Identifier (IRI)
• Qualified Namespace: http://www.w3.org/2001/XMLSchema# as
xsd:
o xsd: string instead of
http://www.w3.org/2001/XMLSchema#string
RDF: Triple Structure, IRI, Namespace
IBM Armonk, New York,
United States
Headquarters located in
19. • Two types of property values in a triple
o Web resource
o Typed literal
RDF Representation
IBM Armonk, New York,
United States
Headquarters located in
IBM
Has total employees
“430,000” ^^xsd:integer
• The graph model of RDF: node-arc-node is the
primary representation model
• Secondary notations: Triple notation
o companyExample:IBM companyExample:has-Total-
Employee “430,000”^^xsd:integer .
20. • RDF Schema: Vocabulary for describing groups of
resources [RDFS]
RDF Schema
IBM Armonk, New
York, United States
Headquarters located in
Oracle Redwood Shores,
California, United States
Headquarters located in
Company Geographical Location
Headquarters located in
21. • Property domain (rdfs:domain) and range
(rdfs:range)
RDF Schema
Headquarters located in
Company
Domain Range
Geographical Location
• Class Hierarchy/Taxonomy: rdfs:subClassOf
rdfs:subClassOf
Computer Technology
Company
SubClass (Parent) Class
Company
Banking Company
Insurance Company
22. Ontology: A Working Definition
• Ontologies are shared conceptualizations of a
domain represented in a formal language*
• Ontologies:
o Common representation model - facilitate
interoperability, integration across different
projects, and enforce consistent use of
terminology
o Closely reflect domain-specific details (domain
semantics) essential to answer end user
o Support reasoning to discover implicit knowledge
* Paraphrased from Gruber, 1993
23. Expressiveness Range:Knowledge Representation
and Ontologies
Catalog/ID
General
Logical
constraints
Terms/
glossary
Thesauri
“narrower
term”
relation
Formal
is-a
Frames
(properties)
Informal
is-a
Formal
instance
Value
Restriction
Disjointness,
Inverse,
part of…
Ontology Dimensions After McGuinness and Finin
Simple
Taxonomies
Expressive
Ontologies
Wordnet
CYCRDF DAML
OO
DB Schema RDFS
IEEE SUOOWL
UMLS
GO
KEGG TAMBIS
EcoCyc
BioPAX
GlycOSWETO
Pharma
24. • A language for modeling ontologies [OWL]
• OWL2 is declarative
• An OWL2 ontology (schema) consists of:
o Entities: Company, Person
o Axioms: Company employs Person
o Expressions: A Person Employed by a Company =
CompanyEmployee
• Reasoning: Draw a conclusion given certain
constraints are satisfied
o RDF(S) Entailment
o OWL2 Entailment
OWL2 Web Ontology Language
25. • Class Disjointness: Instance of class A cannot be
instance of class B
• Complex Classes: Combining multiple classes
with set theory operators:
o Union: Parent = ObjectUnionOf (:Mother :Father)
o Logical negation: UnemployedPerson =
ObjectIntersectionOf (:EmployedPerson)
o Intersection: Mother = ObjectIntersectionOf (:Parent
:Woman)
OWL2 Constructs
26. • Property restrictions: defined over property
• Existential Quantification:
o Parent = ObjectSomeValuesFrom (:hasChild :Person)
o To capture incomplete knowledge
• Universal Quantification:
o US President = objectAllValuesFrom (:hasBirthPlace
United States)
• Cardinality Restriction
OWL2 Constructs
27. SPARQL: Querying Semantic Web Data
• A SPARQL query pattern composed of triples
• Triples correspond to RDF triple structure, but
have variable at:
o Subject: ?company ex:hasHeadquaterLocation ex:NewYork.
o Predicate: ex:IBM ?whatislocatedin ex:NewYork.
o Object: ex:IBM ex:hasHeadquaterLocation
?location.
• Result of SPARQL query is list of values – values
can replace variable in query pattern
28. SPARQL: Query Patterns
• An example query pattern
PREFIX ex:<http://www.eecs600.case.edu/>
SELECT ?company ?location WHERE
{?company ex:hasHeadquaterLocation ?location.}
• Query Result
company location
IBM NewYork
Oracle RedwoodCity
MicorosoftCorporation Bellevue
Multiple
Matches
29. SPARQL: Query Forms
• SELECT: Returns the values bound to the variables
• CONSTRUCT: Returns an RDF graph
• DESCRIBE: Returns a description (RDF graph) of a
resource (e.g. IBM)
o The contents of RDF graph is determined by SPARQL
query processor
• ASK: Returns a Boolean
o True
o False
40. Sample applications
• Early Semantic Search, use baby steps of
today’s engines
• Enterprise applications – healthcare & life
sciences, financial, security
• Driving the innovation with new types of data:
sensor (Semantic Sensor Web), social
(Semantic Social Web), semantic IoT/WoT
41. 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
Taalee Semantic/Faceted Search & Browsing (1999-2001)
42. 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 Search/Browsing/Directory (2001-….)
43. System recognizes ENTITY & CATEGORY
Relevant portion
of the Directory is
automatically
presented.
Semantic Search/Browsing/Directory (2001-….)
46. Semagix Freedom for building
ontology-driven information system
Extracting Semantic Metadata from
Semistructured and Structured Sources (1999 – 2002)
Managing Semantic Content on the Web
48. 2004 SEMAGIX
48
Watch list Organization
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 a banned
organization’
Semantic Associations - Connecting the Dots
49. Global Investment Bank
Fraud Prevention application used in
financial services – Related KYC
application is deployed at Majority
of Global Banks
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
51. Semantic Web + Clinical Practice Informatics =
Active Semantic Electronic Medical Record (ASEMR)
Operationally deployed in January 2006, in use (as of 2012)
52. ASEMR: SW application in use
In daily use at Athens Heart Center
– 28 person staff
• Interventional Cardiologists
• Electrophysiology Cardiologists
– Deployed since January 2006
– 40-60 patients seen daily
– 3000+ active patients
– Serves a population of 250,000 people
53. Information Overload in Clinical
Practice
• New drugs added to market
– Adds interactions with current drugs
– Changes possible procedures to treat an illness
• Insurance Coverage's Change
– Insurance may pay for drug X but not drug Y even
though drug X and Y are equivalent
– Patient may need a certain diagnosis before some
expensive test are run
• Physicians need a system to keep track of ever
changing landscape
54. Active Semantic Document (ASD)
A document (typically in XML) with the following features:
• Semantic annotations
– Linking entities found in a document to ontology
– Linking terms to a specialized lexicon [TR]
• Actionable information
– Rules over semantic annotations
– Violated rules can modify the appearance of the document (Show an
alert)
55. Active Semantic Patient Record
• An application of ASD
• Three Ontologies
– Practice
Information about practice such as patient/physician data
– Drug
Information about drugs, interaction, formularies, etc.
– ICD/CPT
Describes the relationships between CPT and ICD codes
• Medical Records in XML created from database
56. Active Semantic Electronic Medical Record App
In Use Today at Athens Heart Center For Clinical Decision Support since January 2006
Amit P. Sheth, S. Agrawal,Jonathan Lathem, Nicole Oldham, H. Wingate, P. Yadav, and K. Gallagher, Active Semantic
Electronic Medical Record, Proc. of the 5th International Semantic Web Conference, 2006
57. Demo of ASEMR and other
applications
http://knoesis.org/showcase
http://archive.knoesis.org/library/demos/
58. Benefits of ASEMR
• Error prevention (drug interactions, allergy)
– Patient care
– insurance
• Decision Support (formulary, billing)
– Patient satisfaction
– Reimbursement
• Efficiency/time
– Real-time chart completion
– “semantic” and automated linking with billing
59. Using large data sets for Structured
Data on the web:
Linked Open Data – samples from
2005 to 2010
60. Linked Open Data
Publish Open Data Sets in RDF
By 2010, 203 data data sets
25 billion Triples
Image: http://richard.cyganiak.de/2007/10/lod/
61. You publish the raw data…
Semantic Web Adoption and Application
62. … and others can use it
Semantic Web Adoption and Application
63. Using the LOD to build Web site: BBC
Semantic Web Adoption and Application
64. Using the LOD to build Web site:
BBC
Semantic Web Adoption and Application
70. Twitris: Semantic Social Web Mash-up
Select topicSelect date
Topic tree
Spatial Marker
N-gram summaries
Wikipedia articles
Reference newsRelated tweets
Images & Videos
Tweet traffic
Sentiment
Analysis
TWITRIS
71. Web (and associated computing) is
evolving
Web of pages
- text, manually created links
- extensive navigation
2007
1997
Web of databases
- dynamically generated pages
- web query interfaces
Web of resources
- data, service, data, mashups
- 4 billion mobile computing
Web of people, Sensor Web
- social networks, user-created casual content
- 40 billion sensors, 500M+ FB users, 1B tweets/wk
Web as an oracle / assistant / partner
- “ask the Web”: using semantics to leverage text
+ data + services
- Powerset
Computing for Human Experience
Keywords
Patterns
Objects
Situations,
Events
Enhanced Experience,
Tech assimilated in life
72. Structured text
(Scientific
publications /
white papers)
Experimental
Results Clinical Trial Data
Public domain
knowledge
(PubMed)
Metadata Extraction/Semantic Annotations
Ontologies/Dom
ain Models/
Knowledge
Meta data /
Semantic
Annotations
Semantic Search/
Browsing/Personalization/
Analysis, Knowledge
Discovery,
Visualization,
Situational Awareness
Big data
Search and
browsing
Patterns / Inference / Reasoning
2D-3D & Immersive
Visualization, Human
Computer Interfaces
Impacting
bottom line
Knowledge
discovery
Migraine
Stress
Patient
affects
isa
Magnesium
Calcium Channel
Blockers
inhibit
SEMANTICS, MEANING PROCESSING
72
74. Take Home Message (Cont.)
Semantics play a key role in refering
"meaning" behind the data. Requires
progress from keywords -> entities ->
relationships -> events, from raw data to
human-centric abstractions.
75. Take Home Message (Cont.)
Wide variety of semantic models and KBs
(vocabularies, social dictionaries, community created semi-structured
knowledge, domain-specific datasets, ontologies) empower
semantic solutions. This can lead to Semantic
Scalability – scalability that is meaningful to
human activities and decision making.
76. Interested in more?
Kno.e.sis Wiki for the following and more:
• Computing for Human Experience
• Continuous Semantics to Analyze Real-Time Data
• Semantic Modeling for Cloud Computing
• Citizen Sensing, Social Signals, and Enriching Human Experience
• Semantics-Empowered Social Computing
• Semantic Sensor Web
• Traveling the Semantic Web through Space, Theme and Time
• Relationship Web: Blazing Semantic Trails between Web Resources
• SA-REST: Semantically Interoperable and Easier-to-Use Services and Mashups
• Semantically Annotating a Web Service
Tutorials: Semantic Web:Technologies and Applications for the Real-World (WWW2007)
Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications (WWW2011)
Partial Funding: NSF (Semantic Discovery: IIS: 071441, Spatio Temporal Thematic: IIS-0842129), AFRL and
DAGSI (Semantic Sensor Web), Microsoft Research (Semantic Search) and IBM Research (Analysis of Social
Media Content),and HP Researh (Knowledge Extraction from Community-Generated Content).
77. 77
http://knoesis.org
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio, USA
Vision Paper: Computing for Human
Experience:http://wiki.knoesis.org/index.php/Computing_For_Human_Experience
Future: Computing for Human Experience
Editor's Notes
RDF: Triple structure
Review types of heterogeneity. Why we need to reconcile data heterogeneityUniform Resource Locator: A network location and used as an identifier for resources on the Web. URL is a specific type of URI. URI can be used to refer to anythingIRI: In addition to ASCII character set, contains Universal Character Set (from RFC 3987)
RDF uses XML Schema datatypes
Allows creation of an abstract representation of domain
Allows creation of an abstract representation of domain
Review types of heterogeneity. Why we need to reconcile data heterogeneity
Review types of heterogeneity. Why we need to reconcile data heterogeneity
Review types of heterogeneity. Why we need to reconcile data heterogeneity
Taalee (subsequently Voquette and Semagix) was founded in 1999 as an Audio/Video Web Search Company (focus on A/V mainly for scalability and market focus reasons, servicename: MediaAnywhere). Domain models/ontologies were created in major areas (many more than what you can find on Bing in 2011) and automatically populated to build knowledge bases (populated ontologies or WorldModel) from a variety of structured and semistructured sources, and periodically kept up to date. This was than used for semantic annotation/metadata extraction to drive semantic search, browsing, etc applications over data crawled from Web sites.
The important thing is that the system knew that Robert Duval is a movie actor, is a different person that David Duval who is a golfer and a sportsperson, and had understanding of a variety of relationships Robert Duval participates in – such as
Obtained from Ivan’s slide
Obtained from Ivan’s slide
Obtained from Ivan’s slide
Obtained from Ivan’s slide
Obtained from Ivan’s slide
Let me give a technological introduction to what our center is about: we all face a fire hose of data-- Pubmed adds 2000 to 4000 citations per day, it is usual to add about 5 gig from a single run of a scientific experiment -- and just imagine how much data created by all the cameras and 40 billion mobile sensors in the world! But even with all the search and browsing tools we have, we face huge information glut. How do we make sense from the data? Just as humans apply their knowledge and experience to understand what they see– we apply domain model or knowledge to attach meaningful labels to these data. Then we can apply computational techniques to visualize, provide situational awareness, discovery nuggets of knowledge of information and insight. For example, from all that biomedical data, what a scientist may be looking for is– how can we treat Migraine? What has Magnesium to do with Migraine? Why does Magnesium deficiency cause Migraine? What is the process by which Magnesium affects Migraine?
Kno.e.sis has 15 faculty in Computer Science, life sciences and health care, cognitive science and business. It has about 50 PhD students and post docs– about 2/3 of these in Computer Science. Its faculty members have 40 labs, and occupies a majority of 50K sqft Joshi Research Center. Its students are highly successful– eg tenure track faculty @ Case Western Reserve Univ or Researcher at IBM Almaden. It has received recent funding from funding from Microsoft Research. IBM Research, HP Labs, Google, and small companies (Janya, EZdi,…) and collaborates with many more (Yahoo! Labs, NLM, …).