The talk titled "Realizing Semantic Web - Light Weight semantics and beyond" given by prof. T.K. Prasad at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013. The talk emphasized on annotation and search framework.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
A talk given in 2000 at IBM when it identified Taalee (then merged with Voquette), the Semantic Web company I founded, as one of the five exciting start ups.
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
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.
The talk given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013. The talk showcased demos of successful applications that use semantic web technologies in several research problems.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
The talk titled "Realizing Semantic Web - Light Weight semantics and beyond" given by prof. T.K. Prasad at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013. The talk emphasized on annotation and search framework.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
A talk given in 2000 at IBM when it identified Taalee (then merged with Voquette), the Semantic Web company I founded, as one of the five exciting start ups.
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
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.
The talk given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013. The talk showcased demos of successful applications that use semantic web technologies in several research problems.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
Amit Sheth with TK Prasad, "Semantic Technologies for Big Science and Astrophysics", Invited Plenary Presentation, at Earthcube Solar-Terrestrial End-User Workshop, NJIT, Newark, NJ, August 13, 2014.
Like many other fields of Big Science, Astrophysics and Solar Physics deal with the challenges of Big Data, including Volume, Variety, Velocity, and Veracity. There is already significant work on handling volume related challenges, including the use of high performance computing. In this talk, we will mainly focus on other challenges from the perspective of collaborative sharing and reuse of broad variety of data created by multiple stakeholders, large and small, along with tools that offer semantic variants of search, browsing, integration and discovery capabilities. We will borrow examples of tools and capabilities from state of the art work in supporting physicists (including astrophysicists) [1], life sciences [2], material sciences [3], and describe the role of semantics and semantic technologies that make these capabilities possible or easier to realize. This applied and practice oriented talk will complement more vision oriented counterparts [4].
[1] Science Web-based Interactive Semantic Environment: http://sciencewise.info/
[2] NCBO Bioportal: http://bioportal.bioontology.org/ , Kno.e.sis’s work on Semantic Web for Healthcare and Life Sciences: http://knoesis.org/amit/hcls
[3] MaterialWays (a Materials Genome Initiative related project): http://wiki.knoesis.org/index.php/MaterialWays
[4] From Big Data to Smart Data: http://wiki.knoesis.org/index.php/Smart_Data
The talk titled "Federated Architecture with Provenance and Access Control to realize Open Digital Data for MGI" given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013. The talk emphasized important issues that material scientists encounter in publishing data - Provenance and Access Control.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
Cory Henson and Amit Sheth, Active Perception Over Machine and Citizen Sensing, SemTech 2011, June 2011.
http://semtech2011.semanticweb.com/sessionPop.cfm?confid=62&proposalid=3825
http://semantic-sensor-web.com
"Computing for Human Experience: Semantics empowered Cyber-Physical, Social and Ubiquitous Computing beyond the Web" Keynote at On the Move Federated Conferences, Crete, Greece, October 18, 2011.
http://www.onthemove-conferences.org/
Details: http://wiki.knoesis.org/index.php/Computi
Meenakshi Nagarajan,Amit Sheth,Selvam Velmurugan, "Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications," Tutorial at WWW2011, Hyderabad, India, March 28, 2011.
More info at:
http://knoesis.org/library/resource.php?id=1030
http://www2011india.com/tutorialstr27.html
Ajith Ranabahu, Priti Parikh, Maryam Panahiazar, Amit Sheth and Flora Logan-Klumpler: Kino : Making Semantic Annotations Easier, Presented at 5th Intl Conf on Semantic Computing (ICSC2011), Palo Alto, CA, September 2011.
Ignite talk at ICCM-2013 at United Nations (UN) Nairobi by NSF SoCS project researcher, Hemant Purohit - 'How to Leverage Social Media Communities for Crisis Response Coordination' using Human+Machine computing
Key-message: We need to extract smart actionable data out of big crisis data to assist response coordination, by focusing on demand-supply centric technology.
More at Kno.e.sis' SOCS project page: http://knoesis.org/research/semsoc/projects/socs
Also, Crisis Informatics at Kno.e.sis: http://j.mp/CrisisRes
Description - Ajith defended his thesis on application and data portability in cloud
computing. More details on Ajith's research and publications can be
found at http://knoesis.wright.edu/researchers/ajith/
Video can be found at : http://www.youtube.com/watch?v=oDBeBIIFmHc&list=UUORqXk1ZV44MOwpCorAROyQ&index=1&feature=plpp_video
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
This webinar focuses on how small cultural organizations, teachers, and students might share and promote their cultural stories on platforms such as Twitter, Facebook, etc. The webinar covers based tips for social media use, creating content, hashtags, local publicity, and more. This is the fourth of four webinars created for the "Be Here: Main Street" project in conjunction with the Smithsonian's Museum on Main Street program. The four webinars in this series specific address teachers who are working with student storytellers.
Slides accompanying the VLDB 2010 Journal paper -
Daniel Gruhl, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Amit Sheth, Multimodal Social Intelligence in a Real-Time Dashboard System to appear in a special issue of the VLDB Journal on "Data Management and Mining for Social Networks and Social Media"
Searching Twitter: Separating the Tweet from the ChaffASOS.com
This presentation was given at ICWSM 2011. In this presentation, we report on a qualitative investigation into the different factors that make tweets ‘useful’ and ‘not useful’ for a set of common search tasks. The investigation found 16 features that help make a tweet useful, noting that useful tweets often showed 2 or 3 of these features. ‘Not useful’ tweets, however, typically had only one of 17 clear and striking features.
Our results contribute a novel framework for extracting useful information from real-time streams of social-media content
This is the group presentation (MIC - Made in China) for the client Headway UK, which is a national and local charity looking after people with head injuries.
Gov 2.0 for Texas Certified Public Manager (CPM ) ProgramGovLoop
Workshop delivered for the Texas Certified Public Manager (CPM ) Program, June 2010.
For more information on Gov 2.0, please visit http://topics.govloop.com/gov20
Invited talk at Session on Semantic Knowledge for Commodity Computing, at Microsoft Research Faculty Summit 2011, July 19-20, 2011, Redmond, WA. http://research.microsoft.com/en-us/events/fs2011/default.aspx
Associated video at: https://youtu.be/HKqpuLiMXRs
Amit Sheth with TK Prasad, "Semantic Technologies for Big Science and Astrophysics", Invited Plenary Presentation, at Earthcube Solar-Terrestrial End-User Workshop, NJIT, Newark, NJ, August 13, 2014.
Like many other fields of Big Science, Astrophysics and Solar Physics deal with the challenges of Big Data, including Volume, Variety, Velocity, and Veracity. There is already significant work on handling volume related challenges, including the use of high performance computing. In this talk, we will mainly focus on other challenges from the perspective of collaborative sharing and reuse of broad variety of data created by multiple stakeholders, large and small, along with tools that offer semantic variants of search, browsing, integration and discovery capabilities. We will borrow examples of tools and capabilities from state of the art work in supporting physicists (including astrophysicists) [1], life sciences [2], material sciences [3], and describe the role of semantics and semantic technologies that make these capabilities possible or easier to realize. This applied and practice oriented talk will complement more vision oriented counterparts [4].
[1] Science Web-based Interactive Semantic Environment: http://sciencewise.info/
[2] NCBO Bioportal: http://bioportal.bioontology.org/ , Kno.e.sis’s work on Semantic Web for Healthcare and Life Sciences: http://knoesis.org/amit/hcls
[3] MaterialWays (a Materials Genome Initiative related project): http://wiki.knoesis.org/index.php/MaterialWays
[4] From Big Data to Smart Data: http://wiki.knoesis.org/index.php/Smart_Data
The talk titled "Federated Architecture with Provenance and Access Control to realize Open Digital Data for MGI" given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013. The talk emphasized important issues that material scientists encounter in publishing data - Provenance and Access Control.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
Cory Henson and Amit Sheth, Active Perception Over Machine and Citizen Sensing, SemTech 2011, June 2011.
http://semtech2011.semanticweb.com/sessionPop.cfm?confid=62&proposalid=3825
http://semantic-sensor-web.com
"Computing for Human Experience: Semantics empowered Cyber-Physical, Social and Ubiquitous Computing beyond the Web" Keynote at On the Move Federated Conferences, Crete, Greece, October 18, 2011.
http://www.onthemove-conferences.org/
Details: http://wiki.knoesis.org/index.php/Computi
Meenakshi Nagarajan,Amit Sheth,Selvam Velmurugan, "Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications," Tutorial at WWW2011, Hyderabad, India, March 28, 2011.
More info at:
http://knoesis.org/library/resource.php?id=1030
http://www2011india.com/tutorialstr27.html
Ajith Ranabahu, Priti Parikh, Maryam Panahiazar, Amit Sheth and Flora Logan-Klumpler: Kino : Making Semantic Annotations Easier, Presented at 5th Intl Conf on Semantic Computing (ICSC2011), Palo Alto, CA, September 2011.
Ignite talk at ICCM-2013 at United Nations (UN) Nairobi by NSF SoCS project researcher, Hemant Purohit - 'How to Leverage Social Media Communities for Crisis Response Coordination' using Human+Machine computing
Key-message: We need to extract smart actionable data out of big crisis data to assist response coordination, by focusing on demand-supply centric technology.
More at Kno.e.sis' SOCS project page: http://knoesis.org/research/semsoc/projects/socs
Also, Crisis Informatics at Kno.e.sis: http://j.mp/CrisisRes
Description - Ajith defended his thesis on application and data portability in cloud
computing. More details on Ajith's research and publications can be
found at http://knoesis.wright.edu/researchers/ajith/
Video can be found at : http://www.youtube.com/watch?v=oDBeBIIFmHc&list=UUORqXk1ZV44MOwpCorAROyQ&index=1&feature=plpp_video
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
This webinar focuses on how small cultural organizations, teachers, and students might share and promote their cultural stories on platforms such as Twitter, Facebook, etc. The webinar covers based tips for social media use, creating content, hashtags, local publicity, and more. This is the fourth of four webinars created for the "Be Here: Main Street" project in conjunction with the Smithsonian's Museum on Main Street program. The four webinars in this series specific address teachers who are working with student storytellers.
Slides accompanying the VLDB 2010 Journal paper -
Daniel Gruhl, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Amit Sheth, Multimodal Social Intelligence in a Real-Time Dashboard System to appear in a special issue of the VLDB Journal on "Data Management and Mining for Social Networks and Social Media"
Searching Twitter: Separating the Tweet from the ChaffASOS.com
This presentation was given at ICWSM 2011. In this presentation, we report on a qualitative investigation into the different factors that make tweets ‘useful’ and ‘not useful’ for a set of common search tasks. The investigation found 16 features that help make a tweet useful, noting that useful tweets often showed 2 or 3 of these features. ‘Not useful’ tweets, however, typically had only one of 17 clear and striking features.
Our results contribute a novel framework for extracting useful information from real-time streams of social-media content
This is the group presentation (MIC - Made in China) for the client Headway UK, which is a national and local charity looking after people with head injuries.
Gov 2.0 for Texas Certified Public Manager (CPM ) ProgramGovLoop
Workshop delivered for the Texas Certified Public Manager (CPM ) Program, June 2010.
For more information on Gov 2.0, please visit http://topics.govloop.com/gov20
Invited talk at Session on Semantic Knowledge for Commodity Computing, at Microsoft Research Faculty Summit 2011, July 19-20, 2011, Redmond, WA. http://research.microsoft.com/en-us/events/fs2011/default.aspx
Associated video at: https://youtu.be/HKqpuLiMXRs
An overview of current Augmented Reality (AR) technology and potential future applications in libraries. Researched and presented to 9410: Emerging Technologies in Fall 2012 at the University of Missouri School of Information Science and Learning Technologies (SISLT).
Lego Beowulf and the Web of Hands and Hearts, for the Danish national museum ...Michael Edson
This talk was delivered at the awards ceremony for the 2012 Bikuben Foundation Danish Museum Prize in Copenhagen, Denmark.
Ideas about what museums are, who they serve, and the role they play in society are changing with dramatic speed, driven largely by social media and the participatory culture of global networks.
Denmark supports world-class museums, with remarkable collections, expert staff, and beautiful architecture. But how can museum leaders balance the traditional concepts of organizational mission and outcomes with the disruptive possibilities being demonstrated by those who love and use museums in new ways?
A text version of this presentation, with hyperlinks and footnotes, is available at http://www.slideshare.net/edsonm/michael-edson-lego-beowulf-and-the-web-of-hands-and-hearts-for-the-danish-national-museum-awards-13444266
Presentation for School of Visual Arts' Introduction to Information Architecture & Design - Presented by Robert Stribley, Senior Information Architect, Razorfish, NY
SXSW 2012 Panel: Rise of Co-created Shared World CommunitiesScott Walker
Audio-enabled presentation of this SXSW 2012 panel. Presenters: Esther Lim, Scott Walker, & J. Craig Williams. Visit http://cocreatedswc.tumblr.com/ for more (tweetstream, contact info, etc.).
Similar to Detecting Signals from Real-time Social Web (20)
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
1. Detecting Signals from Real-time Social Web
Semantic Social Networking Panel @ STC 2010
June 24, 2010
Amit Sheth
Kno.e.sis, Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, OH
Thanks - Meena Nagarajan, Kno.e.sis
2. Our Approach
• Semantics of ‘Semantic Social Networking’
• Bottom-up and top-down
• Statistical semantics powered by domain model
semantics
• Social Networks of Interest
• Not the friend/peer/co-author network
• Event/topic oriented dynamic networks
3. Dynamic Social Networks: Citizen
Journalism, Online Communities..
http://www.telegraph.co.uk/news/worldnews/asia/india/3530640/Mumbai-attacks-Twitter-and-Flickr-used-to-break-news-Bombay-
India.html
5. Other Areas of Focus
WHAT “I decided to check out Wanted demo today even though
I really did not like the movie”
“It was THE HANGOVER of the year..lasted forever.. so I
went to the movies..bad choice picking “GI Jane” worse
now”
WHAT: Named entity recognition, topics..
6. Other Areas of Focus
“Looking for a cheap body shop mechanic in Dayton
WHAT WHY OH” - Transactional
“Check out these links..” - Information Sharing
“Where can I find a good psp cam” - Information
Seeking
WHAT: Named entity recognition, topics..
WHY: User intent identification ...
7. Other Areas of Focus
Male: “I graduated in '04 from USC... now working in
Austin... I like stuff, and i like doing stuff. What stuff do
you like to do?”
WHAT WHY
Female: “Well Im a pretty easy going person. Love the
outdoors and going camping, boating, fishing, short
weekend trips,the horseraces, drag races, hanging out at
HOW home, doing yard work,or just watching movies or having
BBQ's with friends.”
WHAT: Named entity recognition, topics..
WHY: User intent identification ...
HOW: Word usages and an active population..
8. Other Areas of Focus
WHAT (NER): “Context and Domain Knowledge Enhanced
Entity Spotting in Informal Text”, The 8th International
Semantic Web Conference, 2009
“A Measure of Extraction Complexity: a Novel Prior for
Improving Recognition of Cultural Entities”, Manuscript in
preparation
WHAT WHY
WHY (Intents): “Monetizing User Activity on Social Networks -
HOW Challenges and Experiences”, International Conference on
Web Intelligence, 2009
HOW: “An Examination of Language Use in Online Dating
Personals”, 3rd Int'l AAAI Conference on Weblogs and
Social Media, 2009
9. Sample showcases
Social Computing @ Kno.e.sis
• Social perceptions behind events : Twitris
http://twitris.knoesis.org
• Online popularity of music artists: BBC Sound
Index (IBM Almaden)
http://www.almaden.ibm.com/cs/projects/iis/sound/
10. http://twitris.knoesis.org/
TWITRIS
online pulse of a populace around news-worthy
events..
Mumbai terror attack, Health care debate ..
11. Chatter around news-worthy
events..
Hundreds of tweets, facebook posts, blogs about a single event
multiple narratives, strong opinions, breaking news..
12. TWITRIS : Twitter+Tetris
• WHAT are people saying, WHEN and from
WHERE
• Browse citizen reports using social perceptions
as the fulcrum
• Citizen reports in context by overlaying it
with Web articles!
13. What, When and Where:
The Power of Spatio-Temporal-
Thematic slices
18. Summaries of Citizen Reports
RT @WestWingReport: Obama reminds the faith-based
groups "we're neglecting 2 live up 2 the call" of being R
brother's keeper on #healthcare
19. Find resources related to
Find resources related to
social perceptions
2. Social Media in Context
social perceptions
SOYLENT GREEN and the HEALTH CARE REFORMand News and
News
Wikipedia articles
Information right where you need it ! Wikipedia articles
toto put extracted
put extracted
descriptors in
descriptors in
context
context
ws and
kipedia articles
put extracted
scriptors in
ntext
Cull
well
blog
!Exploit spatio, temporal semantics for thematic aggregation
Exploit spatio, temporal semantics for thematic aggregation
20. Quick Show & Tell: http://twitris.knoesis.org
• See short video at the above link or
search “Twitris”
21. Spatial Aggregation
Assisted by a model of a domain/event...
!"#$%&''()*+,(-*&./01&23&/45670,(8)&9&0:&;6*)(-5/0
&776*)6<0/50!"#$%&'()037(./5160;=3+>>/*?4<>@ABCD0
E6F3&5<G0H/7&56'61I(50
!"#$%"&'()*+%,-"-./#,0012+*3/%,04.*05#,*6#+(7+80%,,*90#:0
8*3%;;+%,.-0#:0:#+<-+0=>?0%!60@#$60A-9*,3#,0#,0!"#$%&#'()*B0
?+%,02C;(DD/,EF+"G.#<DEHI6!880
!"#$%&'()*+%*+,'%*'!"#!$'-./011234/15%6787'9:;<='9:;<=>?>@AB=
9(C4<=D:E-FG'
!"#$%&'()*+,-.(&/&.*0#"(123&'04&2($#(
%1))&"(-"(!"#$%((51$*'216(78(91'(
:;'1"<,&.0#"((=4161%.""(
22. Twitris - A Village Effort!
We are very excited for what is to come!
Stay Tuned!
http://twitris.knoesis.org/
23. Things we are working on..
• Factual vs. Opinionated tweets
• Polarized opinions: what is breaking up a
community
• Joe Wilson: “You lie!”
• Personalized Tweets: what do people like me
think about X.
• Customizing it to events you want to track!
• Trust in Social Media & Content ...... and much more!
24. http://www.almaden.ibm.com/cs/projects/iis/sound/
http://www.almaden.ibm.com/cs/projects/iis/sound/
BBC SoundIndex (IBM Almaden)
Pulse of the Online Music Populace
Daniel Gruhl, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Amit Sheth:
Multimodal Social Intelligence in a Real-Time Dashboard System to appear in a special issue of the VLDB Journal on "Data
Management and Mining for Social Networks and Social Media", 2010
25. The Vision ! Netizens do not always
buy their music, let alone
buy in a CD store.
http://www.almaden.ibm.com/cs/projects/iis/sound/
! Traditional sales figures
are a poor indicator of
music popularity.
• What is ‘really’ hot? • BBC SoundIndex - “A
pioneering project to tap into
• BBC: Are online music the online buzz surrounding
communities good artists and songs, by
leveraging several popular
proxies for popular
online sources”
music listings?!
26. “Multimodal Social Intelligence in a Real-Time
Dashboard System”, VLDB Journal 2010 Special Issue:
Data Management and Mining for Social Networks
and Social Media.
User metadata, unstructured,
Artist/Track structured attention
Metadata metadata
27. “Multimodal Social Intelligence in a Real-Time
Dashboard System”, VLDB Journal 2010 Special Issue:
Data Management and Mining for Social Networks
and Social Media.
Album/Track identification
Sentiment Identification
Spam and off-topic comments
UIMA Analytics Environment
28. “Multimodal Social Intelligence in a Real-Time
Dashboard System”, VLDB Journal 2010 Special Issue:
Data Management and Mining for Social Networks
and Social Media.
Exracted concepts into
explorable datastructures
29. “Multimodal Social Intelligence in a Real-Time
Dashboard System”, VLDB Journal 2010 Special Issue:
Data Management and Mining for Social Networks
and Social Media.
What are 18 year olds in London
listening to?
30. “Multimodal Social Intelligence in a Real-Time
Dashboard System”, VLDB Journal 2010 Special Issue:
Data Management and Mining for Social Networks
and Social Media.
What are 18 year olds in London
listening to?
Crowd-sourced preferences
31. The Word on the Street
Billboards Top 50 Singles chart during the week of Sept 22-28 ’07
vs. MySpace popularity charts
comments were spam Billboard.com MySpace Analysis
comments had positive sentiments
comments had negative sentiments Soulja Boy T.I.
comments had no identifiable sentiments Kanye West Soulja Boy
on Statistics Timbaland Fall Out Boy
Fergie Rihanna
J. Holiday Keyshia Cole
50 Cent Avril Lavigne
in Section 8, the structured metadata Keyshia Cole Timbaland
mestamp, etc.) and annotation results Nickelback Pink
m, sentiment, etc.) were loaded in the Pink 50 Cent
Colbie Caillat Alicia Keys
resented by each cell of the cube is the Table 8 Billboard’s Top Artists vs. our generated list
ents for a given artist. The dimension- Showing Top 10
e is dependent on what variables we
1 was comprised of respondents between ages 8
32. The Word on the Street
Billboards Top 50 Singles chart during the week of Sept 22-28 ’07
vs. MySpace popularity charts
comments were spam Billboard.com MySpace Analysis
comments had positive sentiments both
* Top artists appear in lists,
comments had Overlaps
Several negative sentiments Soulja Boy T.I.
comments had no identifiable sentiments Kanye West Soulja Boy
on Statistics Timbaland Fall Out Boy
* Predictive power of MySpace - Fergie Rihanna
Billboard next week looked a lot like J. Holiday Keyshia Cole
50 Cent Avril Lavigne
in MySpace this week.. metadata
Section 8, the structured Keyshia Cole Timbaland
mestamp, etc.) and annotation results Nickelback Pink
m, sentiment, etc.) were loaded in the Pink 50 Cent
Teenagers are big music influencers Colbie Caillat Alicia Keys
[MediaMark2004]
resented by each cell of the cube is the Table 8 Billboard’s Top Artists vs. our generated list
ents for a given artist. The dimension- Showing Top 10
e is dependent on what variables we
1 was comprised of respondents between ages 8
33. Powerful Proxies for
Popularity
• “Which list more accurately reflects the artists
that were more popular last week?”
• 75 participants
• Overall 2:1 preference for MySpace list
38% of total comments were spam Billboard.com MySpace Analysis
61% of total comments had positive sentiments
4% of total comments had negative sentiments
• Younger age groups: 6:1 (8-15 yrs)
35% of total comments
Table 7 Annotation Statistics
had no identifiable sentiments
Soulja Boy
Kanye West
Timbaland
T.I.
Soulja Boy
Fall Out Boy
Fergie Rihanna
J. Holiday Keyshia Cole
50 Cent Avril Lavigne
As described in Section 8, the structured metadata
Challenging traditional polling methods!
Keyshia Cole Timbaland
(artist name, timestamp, etc.) and annotation results Nickelback Pink
(spam/non-spam, sentiment, etc.) were loaded in the Pink 50 Cent
Colbie Caillat Alicia Keys
hypercube.
The data represented by each cell of the cube is the Table 8 Billboard’s Top Artists vs. our generated list
34. Details here..
Social Computing research at Kno.e.sis
http://knoesis.wright.edu/research/semweb/
projects/socialmedia/
Meena Nagarajan’s research on understanding user-
generated content
http://knoesis.wright.edu/researchers/meena/
35. Semantic Social Networking Panel @ STC 2010
• How can we use the Social Web to detect and observe signals from
real time social data?
• How to study diversity and change, identify patterns of interactions,
and extract insights
• What can we learn about social perceptions of real time events?
• Tools for visualization and analysis in space, time and theme
• Can social network analysis be trusted?
• Capturing social network content to track and analyze buyer
preferences, shopping experience, demographics, and other
characteristics that influence purchasing behavior
Editor's Notes
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
my research has focused on three different understanding challenges associated with ugc
all
with goals of adding structured to unstructured content
in each of these areas I have contributed specific algorithms and techniques, several of which are published efforts..
mention names of techniques
collaborations
the first work that i want to tell u about has been a joint collab with res at IBM over the last 2 years
It is a deployed social web application aimed at real-time analytics of music popularity using data from social networks - basically using crowd sourced social intelligence for business intel
BBC - a platform for ingesting content from popular online sources for music discussion to generate billboard like popularity .. except from user chatter
differs from traditional polling
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
there are two kinds of data that go into soundindex
one structured - here u r seeing the structured metadata artists
but this also includes - structured attention metadata - user listens, plays
second type - unstructured text
significant volume -> user attention to this space
Ingesting into a common format - fetch and process is separate
point polling along with ongoing verification with subject matter experts DJs
Top 45 - showing 10
however for SI we were interested in one dimensional lists
talk about ordering overlaps
Top 45 - showing 10
however for SI we were interested in one dimensional lists
talk about ordering overlaps
Top 45 - showing 10
however for SI we were interested in one dimensional lists
talk about ordering overlaps
We conclude that new opportunities for self expression on the web provide a more accurate place to gather data on what people are really interested in than tra- ditional methods. The even stronger results from the younger audience suggests that this trend is, if any- thing, accelerating.