Spire News

1. Spire News Joel Sachs jsachs@cs.umbc.edu

2. Semantic Web Tools UMD MIND SWAP Semantic CAIN Ontology Development Dissemination Spire Semantic Prototypes In Ecoinformaics UMBC Ebiquity Infrastructure UC Davis ICE Agents Information Retrieval NBII Ontology of Ecological Interaction Prototype applications RMBL Peace NASA GSFC Invasive Species Forecasting System Remote Sensing Data Food Webs

5. Overview of Talk • What (and why) is the semantic web? • History • The tragic legacy of ontologies • Hope for the future • Some Spire achievements • Elvis, Ethan, Swoogle, Tripleshop, RDF123 • Semantic Eco-blogging • Spotter, Splickr, Fieldmarking • Bioblitzes • Linked Data • Why? How? • A tiny data browsing demo

6. Semantic Web? • The Semantic Web arose out of a confluence of 3 communities. • Hypertext; AI; Electronic publishing • The AI component achieved early dominance. • Knowledge representation; Ontologies; First order logic, etc. • This was exciting for some, and confounding for others.

7. The next 3 slides are from “The Suggested Upper Merged Ontology (SUMO) at Age 7: Progress and Promise”, by Adam Pease

8. High Level Distinctions The first fundamental distinction is that between ‘Physical’ (things which have a position in space/time) and ‘Abstract’ (things which don’t)‏ Entity Physical Abstract

9. High Level Distinctions Partition of ‘Physical’ into ‘Objects’ and ‘Processes’ Physical Object Process

10. IntentionalProcess IntentionalPsychologicalProcess RecreationOrExercise OrganizationalProcess Guiding Keeping Maintaining Repairing Poking ContentDevelopment Making Searching SocialInteraction Maneuver Motion BodyMotion DirectionChange Transfer Transportation Radiating DualObjectProcess Substituting Transaction Comparing Attaching Detaching Combining Separating InternalChange BiologicalProcess QuantityChange Damaging ChemicalProcess SurfaceChange Creation StateChange ShapeChange Processes

11. Interoperability through Simplicity

12. Spire So far: Ontologies • “The Big Experiment” • A collection of linked ontologies enabling highly detailed descriptions of ecological interaction. • Supports WoW - Webs on the Web • SpireEcoConcepts • Medium size. Used for expressing trophic links and related information, including bibliographic info on studies. • ETHAN • Evolutionary trees and natural history. • Huge. • Observation ontology • For semantic eco-blogging. • Tiny. • Invasives ontology • Lightweight and extensible in the most trivial of manners.

13. ETHAN Engineering • The semantics behind an arbitrary relation can often be expressed using the rdfs:subClassOf relation, as opposed to rdf:property. Doing so has a number of benefits: • It seems to be more computationally efficient. (We have no hard evidence for this, yet.) • It makes it easy to introduce a new concept, especially in a distributed manner. (See our discussion of conservation information below.) • It leads to fewer disagreements among scientists and, therefore, greater chance of ontology adoption (We have anecdotal evidence for this.)

14. A Brief Tour of Some Relevant Ontologies • http://spire.umbc.edu/ontologies/InvasivesOntology.owl • http://spire.umbc.edu/ontologies/lists/ • http://spire.umbc.edu/ontologies/lists/USFWSInjuriousAnimals.owl • http://spire.umbc.edu/ontologies/lists/Cal-IPC.owl

15. Spire So far … • ELVIS • A suite of tools motivated by the belief that food web structure plays a role in determining the success or failure of potential species invasions. • Species List Constructor. • Give a location, get a species list. • Food Web Constructor. • Give a species list, get a food web. • Evidence Provider. • Drill down on a predicted trophic link, and see evidence for and against the existence of that link. • This illustrates our general attitude of moving away from “answer providers” to “evidence providers”.

16. ELVIS: Ecosystem Localization, Visualization, and Information System Oreochromis niloticus Nile tilapia Bacteria Microprotozoa Amphithoe longimana Caprella penantis Cymadusa compta Lembos rectangularis Batea catharinensis Ostracoda Melanitta Tadorna tadorna Food web constructor Species list constructor ? . . .

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

18. Food Web Constructor generates possible links

19. Evidence provider gives details

20. So far: Integration • Swoogle • Google for the semantic web. • Crawls and indexes RDF documents. • Computes metadata, including “ontoRank”. • Tripleshop • A SPARQL query engine. • Leave out the FROM clause. • Data comes from Swoogle • Semi-automatic dataset constructor • Our main platform for integration

21. Google has made us smarter

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

24. 80 ontologies were found that had these three terms By default, ontologies are ordered by their ‘popularity’, but they can also be ordered by recency or size. Let’s look at this one

25. Basic Metadata hasDateDiscovered:  2005-01-17 hasDatePing:  2006-03-21 hasPingState:  PingModified type:  SemanticWebDocument isEmbedded:  false hasGrammar:  RDFXML hasParseState:  ParseSuccess hasDateLastmodified:  2005-04-29 hasDateCache:  2006-03-21 hasEncoding:  ISO-8859-1 hasLength:  18K hasCntTriple:  311.00 hasOntoRatio:  0.98 hasCntSwt:  94.00 hasCntSwtDef:  72.00 hasCntInstance:  8.00

28. These are the namespaces this ontology uses. Clicking on one shows all of the documents using the namespace. All of this is available in RDF form for the agents among us.

29. Here’s what the agent sees. Note the swoogle and wob (web of belief) ontologies.

30. 10K terms associatged with “person”! Ordered by use. Let’s look at foaf:Person’s metadata

31. UMBC Triple Shop • http://sparql.cs.umbc.edu/tripleshop2 • Online SPARQL RDF query processing basedon HP’s Jena and Joseki with several interesting features • Selectable level of inference over model • Automatically finds SWDs for give queries using Swoogle backend database • Provide dataset creation wizard • Dataset can be stored on our server or downloaded • Tag, share and search over saved datasets

32. Who knows Anupam Joshi? Show me their names, email address and pictures

33. The UMBC ebiquity site publishes lots of RDF data, including FOAF profiles

34. No FROM clause! Constraints on wherethe data comes from

36. Swoogle found 292 RDF data files that appear relevant to answering our query

37. Let’s save the dataset before we use it

38. And tag it so we and others can find it more easily.

39. He has many friends!

40. Semantic Eco-Blogging: Some Background 1/3 of all new web content is user generated • Scientific data is increasingly a part of Web 2.0/3.0 • How easy can we make semantic annotation? Climate change drives ecological change • Alters species distribution Wuethrich, B. How Climate Change Alters Rhythms of the Wild Bernice Wuethrich (4 February 2000) Science 287 (5454), 793. • Drives evolution Bradshaw, W. E., and Holzapfel, C. M. 2001. Genetic shift in photoperiodic response correlated with global warming. Proc. Nat. Acad Sci. USA. 98:14509-14511

41. Semantic Eco-blogging. • Eco-blogs are popping up all over the place. • Bloggers are both amateur nature-lovers, and working biologists. • “On April 24 in Washington DC, I saw a leopard slug. Here’s a picture.” • These observations are, potentially, an important part of the ecological record. • “What was the earliest sighting of a robin hatching?” • “What was the Northernmost sighting of the Asian Longhorn Beetle?” • Etc. • System concept: global human sensor net. • SPOTTER • A firefox plugin for creating OWL from field observations. • Spotter map lets you see all “spots” • Being tested at http://ebiquity.umbc.edu/fieldmarking/ and other blogs near you.

46. You can download spotter at http://spire.umbc.edu/spotter Try it out, and then view your observations on the Spotter map: http://spire.umbc.edu/spotter/spotterMap.php