The Progress on Sagace and

Data Integration

Maori Ito1

Main two topics

• Sagace –Cross Search

• RDF –Data Integration

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Sagace

• Search for Biomedical Data & Resources in Japan

Features

• Focus on biomedical database• Manual Semi-automated Ranking • Refining search results with

facets• More informative search results

with metadata

Mechanism of Search Engine

1. Crawling2. Indexing3. Query Processing4. Scoring

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Crawling

Databases

Crawling Program

Indexing

• Split data convenient size and store own server

Internal Server

Indexing Data

Query Processing and Scoring

NIBIO

MEDALS

JCGGDB

NBDC / DBCLS

AgriTogo

Collaborate by using P2P architecture

Search System

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What is the most Important

thing in cross search ?

! Speed and Accuracy !

Features

• Focus on biomedical database

• Semi-automated Ranking

Log Analysis and reflect search results

• The members of top 8 databases are almost the same.– Patents– KEGG MEDICUS– Medicine and pharmaceutical

proceedings– Drug emergency call– Ingredients information of health food– Merck Manual– Medical Information Network Distribution

Service– The Encyclopedia of Psychoactive Drugs

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Comparison of databases

• Popular databases are Medical or Pharmaceutical “literal rich” databases.

• Top databases run away with the winnings!

• More than half databases have never clicked!

Log data has been reflected in ranking.

• Original score -> A:12,000,B:8,000• Gather clicked data• Eliminate duplicating database in the same

day and pick up lowest denotative rank.– If the database score is lower than 12,400, add

200.– The other databases are added 100 basically. But

if the database denotative rank is lower than 10, add 200.

• Patents score is fixed 8,100.• Maximum score is 30,000.

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Unpopular databases

• Sagace has started the service in March 2012.

• Some databases have never clicked since then.

• Eliminate these databases.• Databases

– 272 DB -> 122 DB

Results

• Accuracy for users must have improved.

• Reducing databases also caused speed up.

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Specific databases in life science

• Some databases in life science is lacked “literal information” .

• Cross search engine is suitable to show literal information.

• Metadata will help these database.

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Metadata

• If the developers mark up data with metadata…

Metadata

• Literal information can add into search results!

Results Image

How to mark up and reflect the results?

【 HTML 】

<div itemscope itemtype="http://schema.org/BiologicalDatabaseEntry"> <span itemprop="dateModified">2012-10-24</span></div>

【 Result 】

Declare scope itemtype with normal html tag

Select property Content

Win Win Win!

• Database developers can appeal rich database information.

• Users can find valuable information easily.

• Crawler program can find these metadata properly.

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What is schema.org?

• "Schema.org is a set of extensible schemas that enables webmasters to embed structured data on their web pages for use by search engines and other applications.”

• "Search engines including Bing, Google, Yahoo! and Yandex rely on this markup to improve the display of search results, making it easier for people to find the right web pages.”

(http://schema.org/)

Microdata

“You use the schema.org vocabulary, along with the microdata format, to add information to your HTML content.”

(http://schema.org/docs/gs.html)• Finalizing the proposal of schema.org

extension is a requirement to show “rich” results for major search engines.

Current Situation

• Define original "property" (entryID, isEntryOf, taxon, seeAlso, reference).

• Please refer to– http://sagace.nibio.go.jp/press/metadata/markup/

6 DBs, 1 catalog and 1 DB archive applied microdata!

• DoBISCUIT(Database Of BIoSynthesis clusters CUrated and InTegrated)

• JCRB Cell Bank • Functional Glycomics with KO mice database • Glyco-Disease Genes Database• JCGGDB Report• MEDALS• Integbio Database Catalog• Life Science Database Archive

To add biological database vocabularies into schema.org,

• “Need more people who think it is a good idea.” (by organizers @ schema.org)– public-vocabs@w3.org (<- ML Let’s join !)

• We need more databases and web pages that are marked up with microdata.

• I want your opinion on microdata.• Let's talk!

http://www.mkbergman.com/968/a-new-best-friend-gephi-for-large-scale-networks/

Data Integration with RDF

http://www.cytoscape.org/what_is_cytoscape.html

What is RDF?

• Resource Description Framework

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@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .@prefix drugbank: <http://bio2rdf.org/drugbank:> .drugbank:DB00316 rdfs:label "Acetaminophen”.

RDF

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@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .@prefix drugbank: <http://bio2rdf.org/drugbank:> .@prefix drugbank_vocab: <http://bio2rdf.org/drugbank_vocabulary:> .@prefix drugbank_target: <http://bio2rdf.org/drugbank_target:> .

drugbank:DB00316 rdfs:label "Acetaminophen" ; drugbank_vocab:target drugbank_target:290 .drugbank_target:290 rdfs:label "Prostaglandin G/H synthase 2".

RDF

subject predicate object

Drugbank:DB00316

Drugbank_target:290

Acetaminophen

Prostaglandin G/H synthase2

rdfs:label

rdfs:label

drugbank_vocab:target

Subject

ObjectPredicate

Predicate

Predicate ObjectObject / Subject

SPARQL(SPARQL Protocol and RDF Query Language)

• “SPARQL (pronounced "sparkle", a recursive acronym for SPARQL Protocol and RDF Query Language) is an RDF query language, that is, a query language for databases, able to retrieve and manipulate data stored in Resource Description Framework format.”

(http://en.wikipedia.org/wiki/SPARQL) 30

How to use?

"Prostaglandin G/H synthase 2”

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@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .@prefix drugbank: <http://bio2rdf.org/drugbank:> .@prefix drugbank_vocab: <http://bio2rdf.org/drugbank_vocabulary:> .@prefix drugbank_target: <http://bio2rdf.org/drugbank_target:> .drugbank:DB00316 rdfs:label "Acetaminophen" ; drugbank_vocab:target drugbank_target:290 .drugbank_target:290 rdfs:label "Prostaglandin G/H synthase 2".

RDF

PREFIX drugbank:<http://bio2rdf.org/drugbank_vocabulary:>select distinct ?v where {#distinct means exclude duplicate?s rdfs:label "Acetaminophen” ; drugbank:target ?t .?t rdfs:label ?v.}

SPARQL

Results!

What is the target of “Acetaminophen”

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SPARQL Endpoint

What is the target of “Acetaminophen”

e.g:http://drugbank.bio2rdf.org/sparql

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Results

• You can get results from the endpoint.

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RDFization in life science

• Many data has been rdfized already.

• Affymetrix,Drugbank, GO, OMIM, KEGG, PDB, UniProt, PubMed...

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Let’s try!

• Bio2RDF– http://bio2rdf.org/

• EBI RDF Platform – http://www.ebi.ac.uk/rdf/

• SPARQL endpoint– e.g:http://drugbank.bio2rdf.org/

sparql• How to learn?

– Learning SPARQL

Pros of RDF

• Excellent with life science data

• Comparison to RDB– Easily be expanded– RDB RDF

• Excellent with No SQL too– key value

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Cons of RDF

• A bit hard to make RDF• A bit hard to create developing

environments

• Speed of SPARQL

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Currant situation in NIBIO

• Toxygates– Johan-san and Igarashi-san have

been developing .

• Orphan Drug Data

Toxygates

• RDFization Open TG-Gates data.– microarray data, pathological data

(kidney, liver, grade ,... )• Linked to other database by

using RDF– KEGG pathway– GO terms– CHEMBL– DrugBank

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http://toxygates.nibio.go.jp/

Orphan Drug

• RDFize orphan drug information in NIBIO.

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<http://www.nibio.go.jp/orphanDrugTarget#80> drgn:designationFiscalYear "1996"; drgn:designationDate "1996/4/1"; drgn:number "(8yaku A) No. 81";drgb:name "Imiglucerase"; dc:description "Improvement of symptoms of anaemia, thrombocytopenia, hepatosplenomegaly, bone symptoms, etc. in patients with Gaucher's disease"; drgn:designationApplicant "Genzyme Japan K.K."; drgb:pharmacology "Improvement of symptoms of anaemia, thrombocytopenia, hepatosplenomegaly, bone symptoms, etc. in patients with Gaucher's disease"; drgb:manufacturer "Genzyme Japan K.K."; eob:approvalDate "1998/3/6"; drgb:product "Cerezyme injection 200U";drgb:brand "CEREZYME_ injection"; drgn:approvedName "Imiglucerase (Genetical Recombination)";drgn:status "Approved".

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Let’s try and give me your idea!

• RDF data will enlarge many kinds of data in Life science.

• NBDC encouraged this movement.

Future Perspective

• RDFize other databases in NIBIO– E.g. bioresource

• Examine the benefit• Spread RDF to many scientists • Make useful environments for

who are not familiar with computers

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