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Web3.0 Presentation

This is the presentation of chapter 7 of Web 3.0 Text Book

pramod koneru

on 19 February 2013

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Transcript of Web3.0 Presentation

Semantics Empowered Web 3.0
Semantics of Social Data
CS 7900 Outline Evolution of Social Web Nature of Social Data Conclusion Evolution of Social Data
Differences between Web 2.0 and Web 3.0
Challenges that arise because of this evolution i.e., Challenges of Social Data
Nature of Social Media
Semantics in Social Media
Apps built upon SSocW presented by
Kurtis Glendenning
Pramod C Koneru Web 2.0 blogging, book marking, sharing......

Made it very easy for people to consume, produce and share information.

Content (UGC) Web 3.0 Semantics on Content

People, connections between people and social, cultural and behavioral richness of humans that participate in these Challenges Ubiquitous The twitter Problem The teen Problem of Informal and mediated.
Off-topic discussions
I am very poor at English.. :(
Slang combined with creativity.
BP oil spill, oil #spill
BPee, spill baby spill etc Twitters limitation to use only 140 Characters make the posts more terse, leaving minimal cue to automatically identify the theme. If it is so tricky
& Messy Real-time social perception
of an event in a region Citizen Sensing What over 1 billion web users can do? What is it good for? Consensus data can be extracted What Where When Theme Spatial Temporal "People act as the sensors in the world by reporting their collected data onto networks." Situation Awareness Vision of Semantic Web Wisdom of Crowd UGC Rich Domain Knowledge
(Ontologies, Knowledge Bases) Better to Organize &
analyze Social Media
Content Web 3.0 document-level metadata "Making Web Based Documents and data machine-understandable as well as easier to integrate and analyze" First step toward SSocW Marking-up or annotating User Generated Content (UGC). But is annotating Traditional Content vs Social Media one and the same ? Role of Semantics in Social Media and background knowledge
can act as common reference models and play a significant role in inferring semantics behind UGC, supplementing well-known statistical and natural language processing (NLP) techniques." Disambiguation Identifying Entities " Ontologies "Lily I loved your cheryl tweedy do..... heart Amy" Entities Found using NLP
"Lily", "Cheryl tweedy" and "Amy" But does Amy --> Amy Winehouse (Popular Artist)
Amy --> Amy (Just another name). MusicBrainz Disambiguated So we have identified
Lily Allen - artist
Cheryl Tweedy - music track
Need to disambiguate between
Amy vs Amy Winehouse (artist) Amy is not Amy Winehouse. "Lils smile so rocks" Syntactic parse gives us the result that it is a verb (VBP) which is not if you look into the context..... But if we check the knowledge base, it tells us that "Smile" is a track by "Lily Allen" Similarly, "Steve says: All Zunes and OneCares must go, at prices permanently slashed!" Here Steve refers to "Steve Ballmer, Microsoft's CEO". I NEED HELP WITH !! Ugh and i have a video project due tomorrow for merrill lynch :( all i need to do is simple: Extract several scenes from a clip, insert captions, transitions and thats it. really. omgg i can’t figure out anything!! help!! and i got food poisoning from eggs. its not fun. Pleassssse, help? :( SONY VEGAS PRO 8 But a knowledge base tells us clearly that none of the off-topic keywords are relevant to "SONY VEGAS PRO 8" Off-Topic Noise The presence of off-topic especially affects the content-analysis applications when a strong monetary value is associated with the content like say the advertisement beside... ANALYZING USER COMMENTS "Smile by Lily Allen Rocks.. :)" Sentiment annotator
(Syntactic parser) Smile (Track)
Lily Allen (Artist) Rocks (Positive) By observing popularity trends over time and
the patterns that stand out in the user activity of such online communities it was possible to forecast what was going to be popular tomorrow. TWARQL Apps based of Semantic Social Web (SSocW) ZEMANTA Zemanta brings useful content
to bloggers, connects authors to their peers
and publishers to marketers. Zemanta API resolves text into strongly identified entities, and then query Freebase for detailed information about the mentioned people, places, movies, etc. BBC SOUNDINDEX (Transforming Social Intelligence to Business Intelligence) "A near real-time analytics of music popularity using data from a variety of Social networks" "Twitris is a tool that extracts social signals for sense making (that is, understanding connections between people, places, and events), which uses the spatio-temporal-thematic metadata extracted from the observations to summarize the semantic content, to create a mashup for visualizing the spatio-temporal-thematic aspects, and to provide a foundation for exploring other news and information sources " A 360 degree Social Media Analytics Platform It captures
Semantics along spatial, temporal, thematic directions
user intentions and sentiments
networking behavior Architecture of Twitris+ Twitris: Search & Explore Tab Example of How, using a back ground knowledge, the system is able to answer complex queries like Who are all the dead people mentioned in the context of OWS?
What are the different professions of the people being mentioned in the OWS movement? Walk-through of How this is being done: Tweet (Entity Spotter using dbpedia) Entity1
Entity2 (person/place/thing) (person/place/thing) (Converting them into RDF Triples) <Tweet> <maot#taggedWith> <Entity1>.
<Tweet> <moat#taggedWith> <Entity2>. Background Knowledge <http://dbpedia.org/resource/Aristotle> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/ontology/Person> .
<http://dbpedia.org/resource/Abraham_Lincoln> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/ontology/Person> .
<http://dbpedia.org/resource/Alain_Connes> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/ontology/Person> .
<http://dbpedia.org/resource/Allan_Dwan> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/ontology/Person> . <http://dbpedia.org/resource/Aristotle> <http://dbpedia.org/ontology/deathPlace> <http://dbpedia.org/resource/Chalcis> .
<http://dbpedia.org/resource/Aristotle> <http://dbpedia.org/ontology/birthPlace> <http://dbpedia.org/resource/Stageira> .
<http://dbpedia.org/resource/Aristotle> <http://purl.org/dc/elements/1.1/description> "Greek philosopher"@en .
<http://dbpedia.org/resource/Aristotle> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://xmlns.com/foaf/0.1/Person> .
<http://dbpedia.org/resource/Aristotle> <http://xmlns.com/foaf/0.1/name> "Aristotle"@en . Combining both of these and using SPARQL to query Sample SPARQL Query SELECT ?person COUNT(?person) as ?count WHERE {
?tweet <http://moat-project.org/ns#taggedWith> ?person .
?person a <http://dbpedia.org/ontology/Person> .
?person <http://dbpedia.org/ontology/deathPlace>
<http://dbpedia.org/resource/United_States> .
} GROUP BY ?person ORDER BY DESC(?count) "TWARQL is an infrastructure translating microblog posts from Twitter as Linked Open Data in real-time." This includes going the following steps 1) extract content (entity mentions, hashtags, and URLs) from microblogs

2) encode content in a structured format (RDF) using shared vocabularies (FOAF, SIOC, MOAT, etc.) 3) enable structured querying of microblogs (using SPARQL) This includes setting up SPARQL Endpoint
to query using Concept feeds 4) enable subscription to a stream of microblogs that match a given query (Concept Feeds) 5) enable scalable real-time delivery of streaming data (SparqlPuSH) -- http://www.huffingtonpost.com/steve-harmon/romney-finds-the-volume_b_1674993.html http://wiki.knoesis.org/index.php/Citizen_Sensing http://wiki.knoesis.org/index.php/Citizen_Sensing -- http://www.musicbrainz.org -- http://www.musicbrainz.org A. Sheth and M. Nagarajan. Semantics-empowered social computing. IEEE Internet Comput., 13 A. Sheth and M. Nagarajan. Semantics-empowered social computing. IEEE Internet Comput., 13 -- http://knoesis. wright.edu/library/resource.php?id=00176. -- http://www.zemanta.com -- http://twitris.knoesis.org -- http://twitris.knoesis.org -- http://twitris.knoesis.org -- http://twitris.knoesis.org/ows/explore/ -- http://twitris.knoesis.org/ows/explore/ -- http://wiki.knoesis.org/index.php/Twarql perception of environmental elements w.r.t time space and theme. http://www.slideshare.net/Cloud/the-social-semantic-web http://www.slideshare.net/Cloud/the-social-semantic-web "There is a great potential in the integration of Social Web and Semantic Web, where objects are treated as first-class citizens, making it easier to search, integrate, and exploit the information surrounding them."
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