Alexandria Digital Library Project

http://www.alexandria.ucsb.edu/

University of California, Santa Barbara

Textual-Geospatial Integration Project

NSF National Science Digital Library Project2001-2003

Aerial photos

Maps

Data

Project GoalsExtend NSDL infrastructure

by enabling

geographic queries for text and non-text items

across heterogeneous digital libraries

geographic referencing of arbitrary texts without

explicit geographic cataloging

ParticipantsUniversity of California,

Santa Barbara James Frew, PI Terence Smith Michael Bueno Linda Hill

Information Retrieval Lab, Illinois Institute of Technology

Ophir Frieder David Grossman Eric Jensen

The American Geological Institute (AGI) has permitted us to use a set of their GeoRef records for system training.

Geospatially- What’s here?

Find library objects associated with a given location:– Place name(s)– “Footprint” (geographic

extent)

Where’s this? Find the location(s) associated

with a given library object

Augmented SearchExamples Queries from TREC-9

Find documents that contain residential real estate listings within New Jersey.

Find reports on automobile traffic in the Washington, DC metropolitan area.

What forms of entertainment are available in Newport Beach, California?

The stages

Oral histories

geo-parsing

georeferenced facts

•placenames• IN• ENVIRONS• PIECE OF

•feature types

lookup in gazetteer

gazetteer entries

•names•footprint

s

spatial analysis

identify best footprint

1

4

3

2

The evaluationTest Settings

• geoparser: IIT, version 2• geoparser: give partial matches a value of 0.25• geofact rule: include geological terms• gazetteer: ADL Gazetteer, protocol interface, 04-

2003• gaz lookup settings: operator = “equals”• clustering settings: basic clustering

Manual Analysis• word count in document• identify unique geofacts in document• identify geoparsing output as valid, partial, or invalid• identify valid matches in ADL gazetteer

Metrics• geoparser: recall and precision• gazlookup: recall and precision• clustering bounding box: recall and precision• clustering bounding box: spatial similarity to

reference bounding box

Example Text

title: Stress-induced borehole elongation; a comparison between the four-arm dipmeter and the borehole televiewer in the Auburn geothermal well

keys: applications | Auburn | borehole breakouts | boreholes | caliper logging | Cayuga County New York | deformation | dipmeter logging | elongation | field studies | fractures | geophysical surveys | instruments | New York | patterns | preferred orientation | rock mechanics | spallations | stress | structural analysis | surveys | televiewers | United States | well-logging

abstract: The nature and origin of borehole elongation recorded by the four-arm dipmeter calipers is studied utilizing information obtained from hydraulic fracturing stress measurements and borehole televiewer data taken in a well located in Auburn, New York. A preferred orientation N10 degrees W-S10 degrees E, + or -10 degrees and a less prominant E-W orientation of borehole elongation, was observed on two runs of the dipmeter. Comparisons of borehole geometry determined using the televiewer and the dipmeter show that both tools give the same orientation of borehole elongation provided that the zone of elongation is longer than 30 cm. Comparisons of dipmeter caliper data with orientation of in situ stress and natural fractures, obtained from hydrofracturing tests and televiewer data show that the N10 degrees W-S10 degrees E borehole elongations (1) are axisymmetric, (2) are aligned with the minimum horizontal stress S (sub h) and (3) are not associated with natural fractures intersecting the well. These elongations are interpreted as stress-induced well bore breakouts. The E-W elongation direction is characterized by an assymmetric borehole cross section in thinly bedded rocks and is not caused by breakouts. This assymmetric geometry can be discriminated from breakouts using the oriented electric measurements provided by the dipmeter. This study demonstrates that the dipmeter can be used to determine the orientation of S (sub h) confirming the results of earlier less detailed studies, and provides a firm basis for mapping regional stress patterns using existing dipmeter data.--Modified journal abstract

GeoRef bibliographic record from the TGI test set of 7523 records

Manual AnalysisGeofacts

• Auburn IN New York • Cayuga County IN New York • IN Auburn • Auburn • New York • United States

Gaz entries• adlgaz-1-6862604-02 (Auburn, New York) • adlgaz-1-2168-0d (Cayuga County, New York) • adlgaz-1-195-06 (New York) • adlgaz-1-156-69 (United States)

Geoparsing

Geoparsing performance parser recall = 4.25/6 = 0.71 parser precision = 4.25/8 = 0.53

Geoparsing output(Auburn, , , , 1, K) (Auburn, , , (in, ), 1, T) (New York, , , , 1, K) (United States, , , , 1, K) (Cayuga County, , , , 1, K) (Auburn New, , , (in,), 1, B)(County New, , , (in, York), 1, K) (York, , , , 1, B)

fact ::= (name?, type?, footprint?, related-fact?, certainty, importance)

Geoparsing scoring valid fact = 1 partially valid fact = 0.25 invalid fact = 0

blue = valid fact green = partially valid fact red = invalid fact

Gazlookup operator = “equals” (exact match)

“auburn” .. 37 entries

“new york” .. 18 entries

“united states” .. 1 entry

“cayuga county” .. 1 entry

“auburn new” .. 0

“county new” .. 0

“york” .. 50 entries

TOTAL = 105

Gazlookup performance lookup recall = 3/4 = 0.75 lookup precision = 3/105 = 0.03

Scatter of points

Scatter of 105 points from “equals” Gazlookup

Clustered points (67) in the US and Canada

Baseline clustering

Derived footprint

Footprint for “equals” lookup data and simple clustering, compared to GeoRef footprint

GeoRef footprint

Derived footprint from points

Very low spatial similarity between TGI box and reference box from GeoRef

Statistics redux

Based on comparison of automated processes to manual analysis and GeoRef box for one sample record:

GeoparsingRecall ……………………….. 0.71Precision ……………………. 0.53

GazlookupRecall ……………………….. 0.75Precision ……………………. 0.03

TBI bounding boxRecall ……………………….. 0.75Precision ……………….….. 0.05Similarity to reference ….. 0

•Set new conditions

•Find settings that give good results for 10 test records

•Run 7,524 GeoRef test records through TGI

•Calculate similarity of TGI boxes to GeoRef boxes

•Choose 10 new test records for manual analysis from best & worst results

•Reset conditions•Repeat

Next steps

Geoparser Gazlookup Clustering