Geographic Information Retrieval From Disparate Data Sources

Geographic Information Retrieval from Disparate Data Sources Ian Turton, Anuj Jaiswal, Mark Gahegan GeoVISTA Center, School of Geography, Pennsylvania State University ijt1,arj135,mng1@psu.edu

Summary

Information Retrieval?

Geographic?

Disparate Data Sources?

Does it work?

Semantics and Ontologies, do they help?

Further work?

Conclusions

Information Retrieval

Information retrieval (IR) is the science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within databases, whether relational stand-alone databases or hypertextually-networked databases such as the World Wide Web.

Wikipedia

OR more simply

Is there some way I can avoid reading all 19,000 of those articles about measles and still sound like I know what I’m talking about at the next conference?

Geography

Well we all know that geography is important.

Depending on who you ask more than 80% of all information contains a geographic element.

Explicit:

Has a map coordinate

Implicit:

Has a place name

Disparate Data Sources

Large collections of text containing implicit geographic references about Avian Flu and Measles:

PubMed abstracts

News Feeds (RSS)

WHO incident reports

Building the System

Acquire data

Extract geographic information

Extract semantic and ontological information

Present in a form that allows easy exploration by users.

Acquire Data

First extract abstracts from PubMed

http://eutils.ncbi.nlm.nih.gov/entrez/eutils/

((avian OR bird) AND (influenza OR flu)) OR H5N1

Returns a structured XML file with citation data and abstract for selected papers.

Process XML into PostGIS database

Extract Geographic Entities

Use FactXtractor (http://julian.mine.nu/snedemo.html)

Uses GATE to detect and extract Named Entities and Entity Relationships

Usually finds People , Places and Organizations

Returned as an OWL encoded ontology

In this case we just make use of places

<rdf:RDF xml:base="http://ist.psu.edu/sna/ontology#">

<owl:Class rdf:ID="Location"/>

<owl:Class rdf:ID="Organization"/>

<owl:Class rdf:ID="Person"/>

<owl:DatatypeProperty rdf:ID="counts"/>

<Location rdf:ID="Africa">

<counts>1</counts>

<mentioned_in>

<_Article rdf:ID="InputString0">

</_Article>

</mentioned_in>

</Location>

<Location rdf:ID="Asia">

<counts>1</counts>

<mentioned_in rdf:resource="#InputString0"/>

</Location>

<Location rdf:ID="Vietnam"/>

<Location rdf:ID="South_East"/>

<Location rdf:ID="Europe">

<counts>1</counts>

<mentioned_in rdf:resource="#InputString0"/>

</Location>

</rdf:RDF>

GeoLocation

Converting a place name into a location

State College, PA -> (40.7934, -77.86)

Call the GeoNames web service to carry out a gazetteer lookup on the name.

Disambiguation

Which London did you mean?

Types of Ambiguity

Geo/Geo

London, UK vs London, Ontario

South Wales, UK vs New South Wales, Au

Paris, France vs Paris, Texas

Geo/Non Geo

Washington, DC vs George Washington

Van, Turkey vs delivery van

West Nile, Egypt vs West Nile Virus

Sort of Ambiguous

avian A/Mallard/Pennsylvania/10218/84 (H5N2) influenza virus strains

Disambiguating Multiple Places

Choose A if A is a Political Entity and B is not,

Choose B if B is a Political Entity and A is not,

Choose A if A is a Region and B is not,

Choose B if B is a Region and A is not,

Choose A if A is an Ocean and B is not,

Choose B if B is an Ocean and A is not,

Choose A if A is a Populated Place and B is not,

Choose B if B is a Populated Place and A is not,

Choose A if A's population is greater than B's,

Choose B if B's population is greater than A's,

Choose A if A is an Administrative Area and B is not,

Choose B if B is an Administrative Area and A is not,

Choose A if A is a Water Feature and B is not,

Choose B if B is a Water Feature and A is not,

Choose A.

Solving Geo/Non Geo Ambiguity

Stop word lists – hand crafted by experience

Province, valley, way, hill, Children, Children's, new, cross, red, clinic, general, côte, ii, iii, bas, pays, chem, northern region, eastern region, central region, southern region, region, off, square, census, islands, city, district, park, USA, State, Virology, Microbiology, Immunology, Medical, Science, Employee, Surveillance, Disease, Biochemistry, Prevention, for, and, mail, natl, dept, dev, agr, Rural, inst, mil, med, coll, Internal, Publ, Bur, Hosp, Jude, Childrens, Chai, yan, Virol, Dis, Div, Enter, Cent, lab, Univ, res, ist, prevent, roc, prod, Roche, vet, castle, peak, stat, garden, Atl, Anim, mar, queen, central, Director, LAT, AC-EIA, register, north, east, south, west, northern, southern, eastern, western

Concept Extraction