This document discusses an experiment with the BioMoby web services aimed at improving semantic annotation through human computation and social tagging. It examines the challenges of semantic web service discovery and tests the efficacy of volunteer-based annotation compared to a gold-standard dataset. The results suggest that while open semantic annotation can work effectively with enough context and volunteer involvement, further research is needed to understand factors influencing participant contributions.

1. Open Semantic Annotation an experiment with BioMoby Web Services Benjamin Good, Paul Lu, Edward Kawas, Mark Wilkinson University of British Columbia Heart + Lung Research Institute St. Paul’s Hospital

2. The Web contains lots of things

3. But the Web doesn’t know what they ARE text/html video/mpeg image/jpg audio/aiff

4. The Semantic Web It’s A Duck

5. Semantic Web Reasoning Logically… It’s A Duck Defining the world by its properties helps me find the KINDS of things I am looking for Add properties to the things we are describing Walks Like a Duck Quacks Like a Duck Looks Like a Duck

6. Asserted vs. Reasoned Semantic Web Catalog/ ID Selected Logical Constraints (disjointness, inverse, …) Terms/ glossary Thesauri “ narrower term” relation Formal is-a Frames (Properties) Informal is-a Formal instance Value Restrs. General Logical constraints Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness. Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html

7. Who assigns these properties? Works ~well … but doesn’t scale

8. When we say “Web” we mean “Scale”

9. Natural Language Processing Scales Well… Works!! … Sometimes… … Sort of….

10. Natural Language Processing Problem #1 Requires text to get the process started Problem #2 Low accuracy means it can only support, not replace, manual annotation

11. Web 2.0 Approach OPEN to all Web users (Scale!) Parallel, Distributed, “ Human Computation”

12. Human Computation Getting lots of people to solve problems that are difficult for computers. (term introduced by Luis Von Ahn, Carnegie Mellon University)

13. Example: Image Annotation

14. ESP Game results >4 million images labeled >23,000 players Given 5,000 players online simultaneously, could label all of the images accessible to Google in a month See the “Google image labeling game”… Luis Von Ahn and Laura Dabbish (2004) “Labeling images with a computer game” ACM Conference on Human Factors in Computing Systems (CHI)

15. Social Tagging Accepted Widely applied Passive volunteer annotation. Del.icio.us 2006 surpassed 1 million users Connotea, CiteUlike, etc. See also our ED2Connotea extension This is a picture of Japanese traditional wagashi sweets called “seioubo” which is modeled after a peach

16. BUSTED! I just pulled a bunch of Semantics out of my Seioubo!

17. BUSTED! This is a picture of Japanese traditional wagashi sweets called “seioubo” which is modeled after a peach This is a totally sweet picture of peaches grown in the city of Seioubo, in the Wagashi region of Japan

18. So tagging isn’t enough… We need properties, but the properties need to be semantically-grounded in order to enable reasoning (and this ain’t gonna happen through NLP because there is even less context in tags!)

19. Social Semantic Tagging Q1: Can we design interfaces that assist “the masses” to derive their tags from controlled vocabularies (ontologies)? Q2: How well do “the masses” do when faced with such an interface? Can this data be used “rigorously” for e.g. logical reasoning? Q3: “The masses” seem to be good at tagging things like pictures… no brainer! How do they do at tagging more complex things like bioinformatics Web Services?

20. Context: BioMoby Web Services BioMoby is a Semantic Web Services framework in which the data-objects consumed/produced by BioMoby service providers are explicitly grounded (semantically and syntactically) in an ontology A second ontology describes the analytical functions that a Web Service can perform

21. Context: BioMoby Web Services BioMoby ontologies suffer from being semantically VERY shallow… thus it is VERY difficult to discover the Web Service that you REALLY want at any given moment… Can we improve discovery by improving the semantic annotation of the services?

22. Experiment Implemented The BioMoby Annotator Web interface for annotation myGrid ontology + Freebase as the grounding Recruited volunteers Volunteers annotated BioMoby Web Services Measured Inter-annotator agreement Agreement with manually constructed standard Individuals, aggregates

23. BioMoby Annotator Information extracted from Moby Central Web Service Registry Tagging areas

24. Tagging Type-ahead tag suggestions drawn from myGrid Web Service Ontology & from Freebase

25. Tagging New simple tags can also be created, as per normal tagging

26. “ Gold-Standard” Dataset 27 BioMoby services were hand-annotated by us Typical bioinformatics functions Retrieve database record Perform sequence alignment Identifier-to-Identifier mapping

27. Volunteers Recruited friends and posted on mailing lists. Offered small reward for completing the experiment ($20 Amazon) 19 participants Mix of BioMoby developers, bioinformaticians, statisticians, students. Majority had some experience with Web Services 13 completed annotating all of the selected services

28. Measurements Inter-annotator agreement Standard approach for estimating annotation quality. Usually measured for small groups of professional annotators (typically 2-4**) Agreement with the “gold standard” Measured in the same way but one “annotator” is considered the standard

29. Inter-annotator Agreement Metric Positive Specific Agreement Amount of overlap between all annotations elicited for a particular item comparing annotators pairwise 2*I (2*I + a + b) I = intersection of sets A and B a = A without I b = B without I PSA(A, B) =

30. Gold-standard Agreement Metrics Precision, Recall, F measure True tags by T All tags by T Precision (T) = True tags by T All true tags Recall (T) = (F = PSA if one set considered “true”) F = harmonic mean of P and R (2PR/P+R)

31. Metrics Average pairwise agreements reported Across all pairs of annotators By Service Operation (e.g. retrieval) and Objects (e.g. DNA sequence) By semantically-grounded tags By free-text tags

32. Inter-Annotator Agreement Type N pairs mean median min max stand. dev. coefficient of variation Free, Object 1658 0.09 0.00 0.00 1.00 0.25 2.79 Semantic, Object 3482 0.44 0.40 0.00 1.00 0.43 0.98 Free, Operation 210 0.13 0.00 0.00 1.00 0.33 2.49 Semantic, Operation 2599 0.54 0.67 0.00 1.00 0.32 0.58

33. Agreement to “Gold” Standard Subject Type measure mean median min max stand. dev. coefficient of variation Data-types (input & output) PSA 0.52 0.51 0.32 0.71 0.11 0.22 Precision 0.54 0.53 0.33 0.74 0.13 0.24 Recall 0.54 0.54 0.30 0.71 0.12 0.21 Web Service Operations PSA 0.59 0.60 0.36 0.75 0.10 0.18 Precision 0.81 0.79 0.52 1.0 0.13 0.16 Recall 0.53 0.50 0.26 0.77 0.15 0.28

34. Consensus & Correctness: Datatypes

35. Consensus and Correctness: Operations

36. Open Annotations are Different

37. Trust must be earned Can be decided at runtime By consensus agreement (as described here) By annotator reputation By recency By your favorite algorithm By you !

38. IT’S ALL ABOUT CONTEXT!! We can get REALLY good semantic annotations IF we provide context!!

39. Open Semantic Annotation Works IF we provide CONTEXT IF enough volunteers contribute BUT we do not understand why people do or do not contribute without $$$ incentive SO further research is needed to understand Social Psychology on the Web

40. Watch for Forthcoming issue in the International Journal of Knowledge Engineering and Data Mining on “ Incentives for Semantic Content Creation”

41. Ack’s Benjamin Good Edward Kawas Paul Lu MSFHR/CIHR Bioinformatics Training Programme @ UBC iCAPTURE Centre @ St. Paul’s Hospital NSERC Genome Canada/Genome Alberta