Disambiguating Queries for Geographic Information Retrieval Carolyn Hafernik Thesis Proposal May 10, 2006 Computer Science Advisor: Lisa Ballesteros

Information Retrieval (IR) What are the goals of an IR system? What are the goals of an IR system? What is a relevant document? What is a relevant document? How does one determine which documents are relevant? How does one determine which documents are relevant? How are IR systems evaluated? How are IR systems evaluated?

Geographic Information Retrieval (GIR) GIR is an extension of IR GIR is an extension of IR It aims to use geospatial information to help improve retrieval effectiveness It aims to use geospatial information to help improve retrieval effectiveness What makes GIR challenging? What makes GIR challenging? Poor query specification Poor query specification Ambiguity of language Ambiguity of language No central repository for geospatial information No central repository for geospatial information

Geospatial Information Map from Map from Locations Locations Population statistics Population statistics Name variations Name variations Nearby landmarks Nearby landmarks How can geospatial information be used to increase retrieval effectiveness given a query? How can geospatial information be used to increase retrieval effectiveness given a query? Example query: “Hiking near the Bay Area” Example query: “Hiking near the Bay Area”

Sample GeoCLEF 2005 Topics <top> GC001 GC001 C084 C084 Shark Attacks off Australia and California Shark Attacks off Australia and California Documents will report any information relating to shark attacks on humans. Documents will report any information relating to shark attacks on humans. Identify instances where a human was attacked by a shark, including where the attack took place and the circumstances surrounding the attack. Only documents concerning specific attacks are relevant; unconfirmed shark attacks or suspected bites are not relevant. Identify instances where a human was attacked by a shark, including where the attack took place and the circumstances surrounding the attack. Only documents concerning specific attacks are relevant; unconfirmed shark attacks or suspected bites are not relevant. Shark Attacks Shark Attacks near near Australia Australia California California </top><top> GC004 GC004 C126 - C126 - Actions against the fur industry in Europe and the U.S.A. Actions against the fur industry in Europe and the U.S.A. Find information on protests or violent acts against the fur industry. Find information on protests or violent acts against the fur industry. Relevant documents describe measures taken by animal right activists against fur farming and/or fur commerce, e.g. shops selling items in fur. Articles reporting actions taken against people wearing furs are also of importance. Relevant documents describe measures taken by animal right activists against fur farming and/or fur commerce, e.g. shops selling items in fur. Articles reporting actions taken against people wearing furs are also of importance. Animal Rights Actions against the fur industry Animal Rights Actions against the fur industry in in Europe Europe United States United States </top>

Previous Work GeoCLEF 2005 GeoCLEF 2005 Common approaches Common approaches Places to store information Places to store information Named Entity Recognition Named Entity Recognition Query Expansion Query Expansion Traditional IR approaches Traditional IR approaches

Hypothesis My hypothesis is that using geospatial information for query expansion and to re-weight geospatial components for each query will improve retrieval effectiveness. My hypothesis is that using geospatial information for query expansion and to re-weight geospatial components for each query will improve retrieval effectiveness. Improvement will occur because the expanded query will provide the system with more specific information than that contained in the original query. Improvement will occur because the expanded query will provide the system with more specific information than that contained in the original query.

Timeline Timeline Timeline Fall Semester Fall Semester Build the Gazetteer Build the Gazetteer Modify Query Analyzer Modify Query Analyzer Design Experiments Design Experiments Do More Background Reading Do More Background Reading Start writing thesis Start writing thesis January Term January Term Run experiments Run experiments Continue writing thesis Continue writing thesis Spring Semester Spring Semester Analyze results Analyze results Run more experiments (If necessary) Run more experiments (If necessary) Finish thesis Finish thesis

References [1] Davide Buscaldi, Paolo Rosso, Emilio Sanchia Arnal. A WordNet-based Query Expansion method for Geographical Information Retrieval [1] Davide Buscaldi, Paolo Rosso, Emilio Sanchia Arnal. A WordNet-based Query Expansion method for Geographical Information Retrieval [2] Nuno Cardoso, Bruno Martins, Marcirio Silveira Chaves, Leonardo Andrade, Mario J. Silva. The XLDB Group at GeoCLEF [2] Nuno Cardoso, Bruno Martins, Marcirio Silveira Chaves, Leonardo Andrade, Mario J. Silva. The XLDB Group at GeoCLEF [3] O. Ferrandez, Z. Kozareve, A. Toral, E. Noguera, A. Montoyo, R. Munoz, Fernando Llopis. Univeristy of Alicante at GeoCLEF [3] O. Ferrandez, Z. Kozareve, A. Toral, E. Noguera, A. Montoyo, R. Munoz, Fernando Llopis. Univeristy of Alicante at GeoCLEF [4] Daniel Ferres, Alicia Ageno, Horacio Rodriguez. The GeoTALP-IR System at GeoCLEF-2005: Experiments Using a QA-based IR System, Linguistic Analysis, and a Geographical Thesaurus [4] Daniel Ferres, Alicia Ageno, Horacio Rodriguez. The GeoTALP-IR System at GeoCLEF-2005: Experiments Using a QA-based IR System, Linguistic Analysis, and a Geographical Thesaurus [5] Fredric Gey, Ray Larson, Mark Sanderson, Hideo Joho, Paul Chlough. GeoCLEF: the CLEF 2005 Cross-Language Geographic Information Retrieval Track Overview [5] Fredric Gey, Ray Larson, Mark Sanderson, Hideo Joho, Paul Chlough. GeoCLEF: the CLEF 2005 Cross-Language Geographic Information Retrieval Track Overview [6] Fredric Gey, Vivien Petras. Berkeley2 at GeoCLEF: Cross-Language Geographic Information Retrieval of German and English Documents [6] Fredric Gey, Vivien Petras. Berkeley2 at GeoCLEF: Cross-Language Geographic Information Retrieval of German and English Documents [7] Rocio Guillen. CSUSM Experiments in GeoCLEF2005: Monolingual and Bilingual Tasks [7] Rocio Guillen. CSUSM Experiments in GeoCLEF2005: Monolingual and Bilingual Tasks [8] Baden Hughes. NICTA i2d2 at GeoCLEF [8] Baden Hughes. NICTA i2d2 at GeoCLEF [9] Andras Kornai. MetaCarta at GeoCLEF [9] Andras Kornai. MetaCarta at GeoCLEF [10] Sara Lana-Serrano, Jose M. Goni-Menoyo, Jose C. Gonzalez-Cristobal. Miracle’s 2005 Approach to Geographical Information Retrieval [10] Sara Lana-Serrano, Jose M. Goni-Menoyo, Jose C. Gonzalez-Cristobal. Miracle’s 2005 Approach to Geographical Information Retrieval [11] Ray R. Larson. Chesire II at GeoCLEF: Fusion and Query Expansion for GIR [11] Ray R. Larson. Chesire II at GeoCLEF: Fusion and Query Expansion for GIR [12] Jochen L. Leidner. Preliminary Experiments with Geo-Filtering Predicates for Geographic IR [12] Jochen L. Leidner. Preliminary Experiments with Geo-Filtering Predicates for Geographic IR [13] Johannes Leveling, Sven Hartrumpf, Dirk Veiel. University of Hagen at GeoCLEF 2005: Using Semantic Networks for Interpreting Geographical Queries [13] Johannes Leveling, Sven Hartrumpf, Dirk Veiel. University of Hagen at GeoCLEF 2005: Using Semantic Networks for Interpreting Geographical Queries

Thank you! Questions? Comments?