Mashups and Google Maps from a Geospatial Semantic Web Perspective

1. Mashups and Google Mapsfrom a Geospatial Semantic Web Perspective

2. Outline • Introduction • Google Maps & Google Earth • Shortcomings in the current mashups • How Semantics Can Help • Semantic Web vs. semantic web • Semantic Web Mashup Example • From triples to Google Maps • Concluding Remarks

3. Visiting the White House The White House in Google Maps The White House in Google Earth

4. Google Maps vs. Google Earth

5. Special Features in Google Earth 3D buildings and terrian Measure Distances

6. An Explosion of Mashups A mashup is a website or web application that uses content from more than one source to create a completely new service. Source: Wikipedia -- http://en.wikipedia.org/wiki/Mashup_(web_application_hybrid) Source: New Scientist (2006-05-12) Check Real Estate Value Track Ski Conditions Track Storms

7. Questions • Why is there a sudden explosion of “mashups”? Is it the “holy grail” in building the next generation Web? • What’s the use of semantic technology in building mashups? • Do we have the right semantic technology?

8. Mashups are Growing Fast • Ubiquitous web service API • Google Maps, Yahoo! Maps, Amazon, Flickr, del.icio.us, etc. • People can create new applications by reusing the existing parts • The whole is more than the sum of its parts • Maps are intuitive UI interface.

9. Mashup Issues (1 of 3) • The present Web is built for human users. Information is meant for humans to consume and not for computer programs. • A map image is a map to the humans, but is a image to the machines. Map! GIF!

10. Mashup Issues (2 of 3) • It’s difficult to discover and integrate legacy data into new mashup applications. Where can I find real estate data? Data format? Permission to use it? Real Estate Value Mashup Where can I find weather data? Data format? Permission to use it? National Ski Condition Mashup

11. Mashup Issues (3 of 3) • Too many wrongly think that mashups must be Google Maps on “steroid”. • Web 2.0 Mashup Matrix • Records 104 Web 2.0 API • 104 x 104 possibilities • Google Maps 1 of 104 http://www.programmableweb.com/matrix

12. II. How Semantic Web Can Help

13. How the Semantic Web Can Help • Shared Semantic Web ontologies will enable mashups to share data and interoperate • Expressively defined knowledge on the Web will enable mashups to better discover and access existing information • Non-geographical semantic knowledge will encourage the innovation of non-map-based mashups

14. Semantic Technology on the Web • Semantic Web vs. semantic web • Publishing geospatial data on the Web • Exporting legacy data onto the Web • Searching semantic data on the Web RDFS Structured Blogging hCard XML GML RDF/A KML RSS GeoRSS RDF rel-tag XNF OWL Geo Microformat

15. Semantic Web vs. semantic web

16. Key aspects of the SW • Size (= Huge) • Sem. markup (eventually to reach) the same order of magnitude as the web • Conceptual Heterogeneity (= Big) • Sem. markup based on many different ontologies • Rate of change (= Very High) • Data generated all the time from human and artificial agents… • Provenance (= Very Heterogeneous) • ….Hence provenance itself is extremely heterogeneous • Trust (= very variable and subjective) • A side-effect of heterogeneous provenance • Data Quality (= very variable) • No guarantee of correctness • Intelligence (= by-product of size and heterogeneity) • Rather than a by-product of sophisticated problem solving

17. Compare with traditional KBS • Size (= Small or Medium) • KBS normally small to medium size • Conceptual Heterogeneity (= Not an issue) • KBS normally based on a single conceptual model • Rate of change (= Very Low) • Change rate under developers' control (hence, low) • Provenance (= Not an issue) • KBS are normally created ad hoc for an application by a centralised team of developers • Trust (= not a major issue) • Centralisation of devpt. process implies no significant trust issues • Data Quality (= not a major issue) • Again, centralisation guarantees data quality across the board • Intelligence (= by-product of complex, task-centric reasoning) • E.g., sophisticated diagnostic, planning systems…

18. Trader Publish Find Client Server(s) Bind Provision Order Broker Use cases and roles for semantic Web processing • Cross-domain resource discovery • Heterogeneous resource query • Resource translation

19. Publishing Geospatial Data • Describing Geo coordinates • W3C RDF Geo Vocabulary (WGS 84) • Geo of Microformat (WGS 84) • GeoRSS – encoding GML geometry in RSS • Describing geographical locations • CIA Fact Book • http://www-ksl-svc.stanford.edu:5915/doc/wfb/index.html • Open Cyc Spatial Ontology • http://www.cyc.com/cyc-2-1/cyc-vocab.daml

20. Using W3C Geo <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns="http://xmlns.com/foaf/0.1/"> <Person> <name>Dan Brickley</name> <homepage dc:title="Dan's home page“ rdf:resource="http://danbri.org/"/> <based_near geo:lat="51.47026" geo:long="-2.59466"/> <rdfs:seeAlso rdf:resource="http:/danbri.org/foaf.rdf"/> </Person> </rdf:RDF> Source: http://www.w3.org/2003/01/geo/

21. Exporting Legacy Data • Much data is hidden in our legacy systems. We must find ways to export this data onto the Web • Web pages are designed for people. For the Semantic Web we need to look at existing databases and the data in them. • Tim Berners-Lee, March 2006. • http://www.bcs.org/server.php?show=ConWebDoc.3337

22. Getting Data onto the Web • Approach 1: Consolidate everything into a single database

23. Getting Data onto the Web • Approach 2: Dynamically integrate data into a uniformed representation

24. Data Integration Systems • Oracle RDF database (Oracle) • Supports full RDF and RDFS • Support SQL query over RDF graph model • Built-in subsumption support: subClassOf and subPropertyOf • D2RQ (Freie Universität Berlin): • Declarative language for describing mappings between relational DB schemas and RDFS/OWL ontologies • Support SQL • D2RQ Server allows accesses to SQL using SPARQL queries over HTTP • KnowledgeSmarts (Image Matters LLC) • A middle-ware system for knowledge integration over heterogeneous datastores • Supports SQL, Shapefiles, XML, WFS and more. • Optimized for applications that require spatial and temporal computation support.

25. Searching Semantic Data • Swoogle: a Semantic Web search engine • The Ebiquity Research Group at UMBC • Indexes 1.5 million SW documents (as of 2006/06) • Performs sophisticated statistic analysis on triples, OWL classes, OWL properties, and documents (similar to Page Rank) • How to search “geo” ontology using Swoolge • http://geospatialsemanticweb.com/2006/06/06/searching-geospatial-ontologies-in-swoogle http://swoogle.umbc.edu

26. III. Semantic Web Mashup Example

27. Semantic Mashup: Piggy Bank • Piggy Bank is a Firefox extension that uses JavaScript to scrape RDF triples from the Web. • Part of MIT’s SIMILE project • http://simile.mit.edu/piggy-bank

28. Movies at Toronto.Com Typical movies listing Piggy Bank this information

29. Semantic Data in a Piggy Bank Location information Movies!!!!

30. IV. Concluding Remarks

31. Mashups are HOT • An explosion of “mashups” is fueled by • (1) ubiquitous Web Service API (esp. Google Maps API) • (2) the idea that “everyone can create new applications by reusing the existing parts” • (3) the rediscovery of the power of “maps”

32. Semantics is the Key • Developing more sophisticated mashups will require the use of Semantic Web technology • For publishing data on the Web • For exporting legacy data onto the Web • For search semantic data on the Web • We should embrace both “Semantic Web” and “semantic web” technology

33. You Mashup? By Cathy Wilcox, the Sydney Morning Herald