The BioMoby Semantic Annotation Experiment

Open Semantic Annotation an experiment with BioMoby Web Services

Benjamin Good, Paul Lu, Edward Kawas, Mark Wilkinson

University of British ColumbiaHeart + Lung Research Institute

St. Paul’s Hospital

The Web contains lots of things

But the Web doesn’t know what they ARE

text/html video/mpeg image/jpg audio/aiff

The Semantic Web

It’s A Duck

Semantic Web Reasoning

Logically… It’s A DuckWalks Like a Duck

Quacks Like a Duck

Looks Like 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

Asserted vs. ReasonedSemantic Web

Catalog/ID

SelectedLogicalConstraints(disjointness, inverse, …)

Terms/glossary

Thesauri“narrowerterm”relation

Formalis-a

Frames(Properties)

Informalis-a

Formalinstance

Value Restrs.

GeneralLogical

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

Who assigns these properties?

Works ~well

…but doesn’t scale

When we say “Web” we mean “Scale”

Natural Language Processing

Scales Well…

Works!!

…Sometimes…

…Sort of….

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

Web 2.0 Approach

OPEN to all Web users (Scale!)

Parallel, Distributed,

“Human Computation”

Human Computation

Getting lots of people to solve problems

that are difficult for computers.

(term introduced by Luis Von Ahn, Carnegie Mellon University)

Example: Image Annotation

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)

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

BUSTED!

I just pulled a bunch of Semantics out of my Seioubo!

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

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!)

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?

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

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?

Experiment

1. Implemented The BioMoby Annotator• Web interface for annotation• myGrid ontology + Freebase as the grounding

2. Recruited volunteers

3. Volunteers annotated BioMoby Web Services

4. Measured• Inter-annotator agreement• Agreement with manually constructed standard

• Individuals, aggregates

BioMoby AnnotatorInformation extracted from

Moby Central Web Service Registry

Tagging areas

Tagging

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

Tagging

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

“Gold-Standard” Dataset

• 27 BioMoby services were hand-annotated by us

• Typical bioinformatics functions– Retrieve database record– Perform sequence alignment– Identifier-to-Identifier mapping

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

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

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 Ba = A without Ib = B without I

PSA(A, B) =

Gold-standard Agreement Metrics

• Precision, Recall, F measure

True tags by T

All tags by TPrecision (T) =

True tags by T

All true tagsRecall (T) =

(F = PSA if one set considered “true”)

F = harmonic mean of P and R (2PR/P+R)

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

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

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 ServiceOperations

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

Consensus & Correctness: Datatypes

0.00

0.20

0.40

0.60

0.80

1.00

1 2 3 4 5 6 7 8 9

Min votes per tag

PSA

Precision

Recall

coverage

Consensus and Correctness: Operations

0.00

0.20

0.40

0.60

0.80

1.00

1 2 3 4 5 6 7 8 9

Min votes per tag

PSA

Precision

Recall

coverage

Open Annotations are Different

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 !

IT’S ALL ABOUT CONTEXT!!

We can get REALLY good semantic annotations IF we provide context!!

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

Watch for

• Forthcoming issue in the International Journal of Knowledge Engineering and Data Mining on

“Incentives for Semantic Content Creation”

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