Alexandria Digital Library Project University of California, Santa Barbara

Textual- Geospatial Integration Project NSF National Science Digital Library Project Aerial photos Maps Data

Project Goals Extend NSDL infrastructure by enabling o geographic queries  for text and non-text items across heterogeneous digital libraries o geographic referencing  of arbitrary texts without explicit geographic cataloging

Participants University of California, Santa Barbara o James Frew, PI o Terence Smith o Michael Bueno o Linda Hill Information Retrieval Lab, Illinois Institute of Technology o Ophir Frieder o David Grossman o Eric Jensen The American Geological Institute (AGI) has permitted us to use a set of their GeoRef records for system training.

Geospatially- o What’s here?  Find library objects associated with a given location: –Place name(s) –“Footprint” (geographic extent) o Where’s this?  Find the location(s) associated with a given library object

Augmented Search Examples Queries from TREC-9 o Find documents that contain residential real estate listings within New Jersey. o Find reports on automobile traffic in the Washington, DC metropolitan area. o 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 footprints spatial analysis identify best footprint

The evaluation Test Settings geoparser: IIT, version 2 geoparser: give partial matches a value of 0.25 geofact rule: include geological terms gazetteer: ADL Gazetteer, protocol interface, 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 Analysis Geofacts Auburn IN New York Cayuga County IN New York IN Auburn Auburn New York United States Gaz entries adlgaz (Auburn, New York) adlgaz d (Cayuga County, New York) adlgaz (New York) adlgaz (United States)

Geoparsing Geoparsing performance o parser recall = 4.25/6 = 0.71 o 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 o valid fact = 1 o partially valid fact = 0.25 o invalid fact = 0 blue = valid fact green = partially valid fact red = invalid fact

Gazlookup o 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 o lookup recall = 3/4 = 0.75 o 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: o Geoparsing  Recall ………………………  Precision …………………… o Gazlookup  Recall ………………………  Precision …………………… o TBI bounding box  Recall ………………………  Precision ……………….…  Similarity 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 GeoparserGazlookupClustering