AIRFrame: Astrobiology Integrative Research Framework

Lisa Miller and Rich GazanDepartment of Information and Computer SciencesUH-NASA Astrobiology Institutelisa.miller@hawaii.edu, gazan@hawaii.edu

August 5, 2010

Overview● AIRFrame project rationale● AIRFrame project goals● Textpresso system

– Ontology

– Database

– Adaptation to astrobiology

● Current and Future work– Database and Ontology building

– Clustering/Classification

– Browsable visual interface

Nature of Astrobiology● System-level science

– Concerned with complex, multidisciplinary, multi-phenomena behaviors of large physical and biological systems

– Information technology needed to consolidate and represent knowledge and data across many disciplines

● New field– No centralized repositories of knowledge

– No established, standardized vocabulary

Interdisciplinary Collaboration● Proven to be difficult

– Different disciplines have different:● Vocabularies/terminologies● Methods and formats for sharing and

presenting research● Assumed levels of precision

– Institutional and cultural boundaries

● Concern about duplication of research– Lack of access to existing knowledge

– Lack of discipline-specific knowledge to efficiently access what is available

AIRFrame project goals

● Discover and relate diverse research concepts as a high level activity

● Eliminate the need to search for data using combinations of specific keywords

● Show users conceptual and functional relationships between diverse research documents

Astrobiology Integrative Research Framework

Keyword Search Inadequate without a well-defined vocabulary● Keyword searches on Elsevier's ScienceDirect using

some astrobiologically relavant synonyms:

Keywords Number of results An article found only with this search

“prebiotic synthesis” 566 The origin of life – a review of facts and speculations

“prebiotic chemistry” 611 A possible circular RNA at the origin of life

“abiotic synthesis” 312 Recent advances in the chemical evolution and the origins of life

“organic synthesis” 53,434 Prebiotic organic synthesis in early Earth and Mars atmospheres

Keyword Search inadequate for interdiscipinary work

Two searches on Thomson Reuters' ISI Web of Science:

1. astrobio*

- 791 results

- astron* - 23,000+

2. amino acid* Earth

- 940 results

● Only 28 occur in both

● No results from Journal of Theoretical Biology or Origins of Life and Evolution of Biospheres in astrobio* search.

● Some articles with keyword astrobiology assigned by author appear in 2 but not returned with 1

Search: astrobio*

Search: amino acid* Earth

Textpresso-based system● Open-source information retrieval and

extraction system– Developed at CalTech for biological

research

– Currently deployed in 17 different, tightly focused literatures

● Two major components– Database of full-text scholarly documents

– Ontology cataloging types of objects, abstract concepts, and relationships

Ontology● Textpresso uses a shallow ontology to

catalog terms

ConceptsConcepts DescriptionsDescriptions RelationshipsRelationships

Purpose

●Fulfill●Make●Govern●Produce● ...

NucleicAcid

●Adenine●Cytosine●DNA●Guanine● ...

Comparison

●Dissimilar●Equal sized●Like●Related● ...

Database of full-text articles

All Documents

Single Document

Abstract

Body Text

Authors

Title

Individual sentencesWe report the synthesis of glycine on interstellar ice­analog films composed of water,

 methylamine (MA), and carbon dioxide ... 

XML MarkupWe report the <biologicalprocess>synthesis</biologicalprocess> of <aminoacid>glycine</aminoacid>

 on interstellar ice­analog films <action>composed</action> of <volatile><water>water</water></volatile>, methylamine ( MA ), and 

<volatile><gases><chemicalelement>carbon</chemicalelement> dioxide</gases></volatile>

Ontology

System queryAllows search by meaning rather than specific keyword

Search by:● keyword,

●keyword + synonyms, ●and/or whole categories

System Query - Results

Results ranked by number of sentenceswith matching terms

Category 2 Keyword Category 1

Category 2Synonym

Adaptation challenges● Previous implementations of Textpresso

have been based on pre-existing ontologies such as the Gene Ontology

● Previous implementations have been much more narrowly focused such as genetics for a single organism

● Most biological journals have open full-text access through a single source, PubMed

Current Work: Ontology Standards Adoption

● To allow more breadth to the ontology – Build a new ontology using SKOS

standard developed by Vocabulary Explorer at IVOA

– Adapt Textpresso to directly read SKOS ontology

● Standards based ontology can– Ease porting existing ontologies to ours

– Allow easy sharing of our data with other systems

Why SKOS?● Allows fast addition of terms from existing sources such

as International Astronomical Union Thesaurus and IVOA.

● http://explicator.dcs.gla.ac.uk/WebVocabularyExplorer/

● Created to allow conversion from other formats

● SKOS allows the kind of relationships we want to leverage with AIRFrame

Current Work:Database Building

● “Proof-of-concept” AIRFrame/Textpresso is currently up and functioning

– www.ifa.hawaii.edu/airframe/textpresso● Workflow

New articles AIRFrameontology

Mark updocuments

&index

Mine for newTerms

Outsideontologies

AIRFramesearchabledatabase

Current Work:Initial Steps in Document

Classification● Want to use machine learning methods to

discover connections in the data– Possibly use several to give users many

views.

● Some work done on phrase-based clustering

● My research area is in information theoretic clustering

● Open to other ideas

Information Bottleneck Method● Developed by Tishby, Pereira, Bialek in

1999● Based on Shannon Information

– Measures the reduction in uncertainty or the distortion between an original signal and its compressed representation

– Where uncertainty is the entropy

I [ x , y ]=H [ x ]−H [ x∣y ]

H [ x ]=∑x

N

p x log 1 / p x

IB Method 2● Data is compressed so that information

about a quantity of interest (in this case words) is kept maximally.

●

● Relative entropy emerges as the distortion function (Kullback-Leibler divergence)

●

● With the optimal assignment rule●

max p c∣x[ I y , c −TI x , c]

DKL[ p y∣x ∣∣p y∣c]=∑y

p y∣x log2 [ p y∣x p y∣c ]

pc∣x =p c Z x ,

exp −1TDKL[ p y∣x∣∣p y∣c]

IB Method 3● Has been shown to be one of the most

accurate unsupervised clustering algorithms

● Downside: slow & computationally expensive

● I am working to establish the optimal number of clusters automatically within the same process using annealing.

Future Plans● Visualizations!

Future plans● Create a cleaner, browsable interface which

displays results and links in an easily understandable way

●Current Interface

- Ugly!- Not intuitive

- Results spread across pages --Hard to see overall picture

Future Plans● Allow users to input a document and get

back information such as

– Related articles– Connections to other researchers– Relavant NASA/NAI goals

Some Ideas● Use document classification and clustering

assisted by the semantic markup● Create clusterings unsupervised and then

use a Support Vector Machine to classify a document input by the user

● Display connections with topic maps by making Textpresso output in ISO Topic Map form

Personas● We envision AIRFrame being able to

present information in dynamic ways such as this:

● http://personas.media.mit.edu/personasWeb.html

● Would an interface like this be valuable?– Notice cannot click through to sites – will

come back to this

LDA● Used by Personas● Returns document as a distribution across

topics– Topics are clusters of single word terms

– Manually labeled for Personas

● Personas uses an SVM trained with the LDA groupings for on-the-fly classification

Snappy Words● WordNet visualizer

http://www.snappywords.com/?lookup=nasa

Open Questions● How best to use our semantic markup to

present our data? ● How to visualize our information to allow

discovery and understanding of connections?

● How best to search for and represent● Category links between terms?● Authorship linkages between projects?● NASA/NAI goal links to research?

● Ontology building– Can it be at least partially automated?

Issues● Speed

– Users need to have feedback if they need to wait.

– Slow unsupervised methods can be run offline during database building

● Users need to be able to do novel searches, not just a few predefined ones.

● Complexity and size of ontology

Thank you!

For updates visit our site at:www.ifa.hawaii.edu/airframe

To test out our textpresso version:www.ifa.hawaii.edu/airframe/textpresso

References and Resources● R.M. Keller, “A Survey of Knowledge Management Research &

Development at NASA Ames Research Center,” 2002.

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● The Web and SKOS,http://www.iskouk.org/presentations/miles_web_and_skos_2 00807.pdf

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● .

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