UMBC an Honors University in Maryland 1 Adding Semantics to Social Websites for Citizen Science Pranam Kolari University of Maryland, Baltimore County Joint work with Andriy Parafiynyk, Tim Finin, Cynthia Parr, Joel Sachs, and Lushan Han  This work was partially supported by DARPA contract F , NSF grants CCR and IIS

2 This talk Motivation Swoogle Semantic Web search engine Social Semantic Web Conclusions

3 Social media describes the online technologies and practices that people use to share opinions, insights, experiences, and perspectives and engage with each other. Wikipedia 07 SOCIAL MEDIA

4 Social Media for agents Today social media supports information sharing among communities of people - enables Citizen Journalism An infrastructure based on pings, feeds, content aggregators, and filters (e.g. pipes) aids scalability Social media now accounts for ~1/3 of new Web content! We need to explore how networks of agents can use the same strategies to share data and knowledge

5 This talk Motivation Swoogle Semantic Web search engine Social Semantic Web Conclusions

6 Google has made us smarter

7 But what about our agents? tell register Agents still have a very minimal understanding of text and images.

8 But what about our agents? A Google for knowledge on the Semantic Web is needed by software agents and programs Swoogle tell register

9 Running since summer M RDF docs, 434M triples, 10K ontologies, 15K namespaces, 1.5M classes, 185K properties, 49M instances, 800 registered users Running since summer M RDF docs, 434M triples, 10K ontologies, 15K namespaces, 1.5M classes, 185K properties, 49M instances, 800 registered users

10 Analysis Index Discovery IR Indexer Search Services Semantic Web metadata Web Service Web Server Candidate URLs Bounded Web Crawler Google Crawler SwoogleBot SWD Indexer Ranking document cache SWD classifier human machine htmlrdf/xml … the Web Semantic Web Information flowSwoogle‘s web interface Swoogle Architecture pings Archive

11 Applications and use cases Supporting Semantic Web developers –Ontology designers, vocabulary discovery, who’s using my ontologies or data?, use analysis, errors, statistics, etc. Searching specialized collections –Spire: aggregating observations and data from biologists –InferenceWeb: searching over and enhancing proofs –SemNews: Text Meaning of news stories Supporting SW tools –Triple shop: finding data for SPARQL queries 1 2 3

12 2 An NSF ITR collaborative project with University of Maryland, Baltimore County University of Maryland, College Park U. Of California, Davis Rocky Mountain Biological Laboratory An NSF ITR collaborative project with University of Maryland, Baltimore County University of Maryland, College Park U. Of California, Davis Rocky Mountain Biological Laboratory

13 An invasive species scenario Nile Tilapia fish have been found in a California lake. Can this invasive species thrive in this environment? If so, what will be the likely consequences for the ecology? So…we need to understand the effects of introducing this fish into the food web of a typical California lake

14 Food Webs A food web models the trophic (feeding) relationships between organisms in an ecology –Food web simulators explore consequences of ecological changes, i.e., species introduction or removal –Food web are constructed from studies of a location’s species inventory and the known trophic relations. Goal: automatically construct a food web for a new species using existing data and knowledge ELVIS: Ecosystem Location Visualization and Information System

15 East River Valley Trophic Web

16 The problem We have data on what species are known to be in the location and can further restrict and fill in with other ecological models => Maybe we can mine social media for species observations data? But we don’t know which of these the Nile Tilapia eats of who might eat it. We can reason from taxonomic data (similar species) and known natural history data (size, mass, habitat, etc.) to fill in the gaps.

17 Food Web Constructor Predict food web links using database and taxonomic reasoning. In an new estuary, Nile Tilapia could compete with ostracods (green) to eat algae. Predators (red) and prey (blue) of ostracods may be affected

18 Status ELVIS (Ecosystem Location Visualization and Information System) as an integrated set of web services for constructing food webs for a given location. Background ontologies –SpireEcoConcepts: concepts and properties to represent food webs, and ELVIS related tasks, inputs and outputs –ETHAN (Evolutionary Trees and Natural History) Concepts and properties for ‘natural history’ information on species derived from data in the Animal diversity web and other taxonomic sources. 250K classes on plants and animals

19 This talk Motivation Swoogle Semantic Web search engine Social Semantic Web Conclusions

20 Social media sites have become the biggest source of new content on the Web Blogs, Wikis, Photo sites, forums, etc. Accounting for ~1/3 of new Web content

21 Social media sites embrace new ways of letting users add semantic information Shows users the potential of semantics This graph shows the uptake of tags in blogs

22 Social Media and the Semantic Web Many are exploring how Semantic Web technology can work with social media Social media like blogs are typically temporally organized –valued for their timely and dynamic information! If static pages form the Web ’ s long term memory, then the Blogosphere is its stream of consciousness Maybe we can (1) help people publish data in RDF on their blogs, (2) mine social media sites for useful information, (3) exploit new infrastructure ideas for sharing Semantic Web data.

23 The OWL icon links to the data in RDF A BioBlitz involves going out to an area and recording every organism you see

24 Here’s the post’s RDF data

25 A good Semantic Web opportunity We want to make it easy for scientists to enter and collect information from social media –Professionals, students and amateurs! Some early examples –SPOTter – a tool to add Semantic Web data to blogs –Splickr – a system to mine Flickr for images of organisms –RDF123 – an application and Web service to render spreadsheets as RDF data

26 SPOTter: SPire Observation Tool We’ve developed some simple components to help people add RDF data to blogs and ping Swoogle to get it indexed. SPOTter is an initial prototype that uses the ETHAN ontology and is being used in some BioBlitz activities with students. We’re working toward a version that uses Twitter so that people can make the blog entries from the cell phones via SMS –The SPOTter agent will get the entries (via RSS) and index the data

27 SPOTter button Once entered, the data is embedded into the blog post and Swoogle is pinged to index it

28 Prototype SPOTter Search engine We can draw a bounding box on the map and find observations An RSS feed provided for each query

29 Flickr The Flickr “photo sharing” site has millions of photographs –Many of plants and animals Most of them have descriptions, timestamps, tags and even geo-tags –Flickr has even introduced “machine tags” that can be mapped into RDF Any Flickr users (humans or bots) can add comments and annotations There’s a good API It could be a good source of ecological information

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32 Results for people and machines

33 RDF123 An application and web service to generate RDF data from spreadsheets MAP DATA Graphically create & edit spreadsheet to RDF map map + spreadsheet => RDF data Some metadata can Be embedded in spreadsheet CSV or Google doc See

34 RDF123 The Bioblitz project needed a way to collect and share observational data from students Spreadsheets selected as a common data format and templates developed RDF123 application and web service developed to ease exporting the data as RDF for a Maryland BioBlitz group –Supports a web service to generate RDF given URLs for the sheet and map –Works on CSV files and also Google spreadsheets

UMBC an Honors University in Maryland 35 A map provides a template for an RDF subgraph for each row

36 The map is also represented in RDF

37 Here’s the RDF that’s produced from the spreadsheet

38 Metadata, including the URI of a map, can be embedded in the spreadsheet

39 Ping and Feed Design Pattern The Web uses a ping and feed design pattern that is a variant of publish and subscribe It accounts for the scalable, smooth function of the Blogosphere and related social media systems Pings push and feeds pull We can use the same approach to managing volumes of Semantic Web data

40 Pings and Feeds in the Blogosphere Content provider send pings to ping servers when they have a new item Ping servers aggregate pings and stream them to aggregators and indexers, like Google Indexing sites retrieve new items from content provider’s feed Ping Server C1 C2 C3 pings Search Engine

41 Pings and Feeds in the Semantic Web Content provider send pings to ping-the- semantic-web when they have new RDF data PTSW aggregates pings and streams them to SW aggregators and indexers, like Swoogle Indexing sites retrieve new RDF data from content provider’s feed PTSW C1 C2 C3 pings Swoogle

42 Semantic Web Feeds drive Mashups As in the regular web, sites and query engines use feeds to capture queries Accessing a feed runs the query and produces a list of the first N results (usually 10 ≤ N ≤ 20) Such query feeds can drive mashups Systems like Yahoo pipes make it easy to compose feeds

43 This talk Motivation Swoogle Semantic Web search engine Social Semantic Web Conclusions

44 Conclusion The web will contain the world’s knowledge in forms accessible to people and computers –We need better ways to discover, index, search and reason over SW knowledge SW search engines address different tasks than html search engines –So they require different techniques and APIs Swoogle like systems can help create consensus ontologies and foster best practices Social media provide new challenges and opportunities for the Semantic Web

45 Annotated in OWL For more information