Automatic Acquisition of Fuzzy Footprints Steven Schockaert, Martine De Cock, Etienne E. Kerre

Workshop on SEmantic Based Geographic Information Systems 1.Introduction 2.Constructing fuzzy footprints 3.Experimental results

Workshop on SEmantic Based Geographic Information Systems Geographical Question Answering WWW Give a list of Italian Restaurants in the neighborhood of Agia Napa. La Strada Italian Restaurant, Bosko’s ristorante, …

Workshop on SEmantic Based Geographic Information Systems Geographic Question Answering Resources –Linguistic resources for question analysis, answer extraction, … –A traditional search engine to locate relevant documents –Geographic background knowledge Footprints provided by gazetteers are often inadequate –We need a more fine-grained representation than a bounding box –Questions may involve vague regions such as the Alpes, the Highlands, … Our solution: construct footprints automatically –Use the web the collect relevant information –Use a digital gazetteer to map location names to co- ordinates –Use fuzzy sets to represent footprints

Workshop on SEmantic Based Geographic Information Systems Fuzzy Sets A fuzzy set A in a universe U is a mapping from U to [0,1] (Zadeh, 1965) –u belongs to A  A(u)=1 –u doesn’t belong to A  A(u)=0 –u more or less belongs to A  0 < A(u) < 1 Old

Workshop on SEmantic Based Geographic Information Systems We represent footprints as fuzzy sets in the universe of co-ordinates Fuzzy Footprints “South of France”

Workshop on SEmantic Based Geographic Information Systems 1.Introduction 2.Constructing fuzzy footprints 3.Experimental results

Workshop on SEmantic Based Geographic Information Systems Obtaining relevant locations the Ardeche region - Located in the north of the Ardeche region, - (,)* and other cities in the Ardeche region - is situated in the heart of the Ardeche region - … St-Félicien, Lamastre, St-Agrève,… ADL gazetteer

Workshop on SEmantic Based Geographic Information Systems Disambiguation of location names based on –the country the region is located in –the distance to the other locations Obtaining relevant locations

Workshop on SEmantic Based Geographic Information Systems Existing approaches –Use the convex hull of the locations  web data is too noisy  not suitable for vague regions –Use the density of the locations (Purves et al., 2005)  reflects popularity rather than the extent of a region Our solution: search for additional constraints to filter out noise Constructing a footprint

Workshop on SEmantic Based Geographic Information Systems Constructing a footprint x is in the north of the Ardeche region

Workshop on SEmantic Based Geographic Information Systems Constructing a footprint x is in the north of the Ardeche region inconsistent consistent ???

Workshop on SEmantic Based Geographic Information Systems Modelling constraints x is located in the north of the Ardeche Gradual transition Consistent Inconsistent

Workshop on SEmantic Based Geographic Information Systems Modelling constraints x is located in the north of the Ardeche Gradual transition Consistent Inconsistent Based on the average difference in y co- ordinates

Workshop on SEmantic Based Geographic Information Systems In a similar way: –x is located in the south of the Ardeche –x is located in the west of the Ardeche –x is located in the east of the Ardeche –x is located in the north-west of the Ardeche  x is located in the north of the Ardeche  x is located in the west of the Ardeche –x is located in the heart of the Ardeche Modelling constraints

Workshop on SEmantic Based Geographic Information Systems Modelling constraints the Ardeche is located in the south of France Gradual transition Consistent Inconsistent

Workshop on SEmantic Based Geographic Information Systems Modelling constraints the Ardeche is located in the south of France Gradual transition Consistent Inconsistent Based on the minimal bounding box for France (ADL gazetteer)

Workshop on SEmantic Based Geographic Information Systems In a similar way: –R is located in the north of France –R is located in the east of France –R is located in the west of France –R is located in the north-west of France  R is located in the north of France  R is located in the west of France –R is located in the heart of France Modelling constraints

Workshop on SEmantic Based Geographic Information Systems Modelling constraints Heuristic: points that are too far from the median are likely to be noise Inconsistent Gradual transition Consistent

Workshop on SEmantic Based Geographic Information Systems Modelling constraints Heuristic: points that are too far from the median are likely to be noise Inconsistent Gradual transition Consistent Based on the average distance to the median

Workshop on SEmantic Based Geographic Information Systems Example Constraints satisfied to degree 1 Constraints satisfied to degree 0.6 Constraints satisfied to degree 0.4 Constraints satisfied to degree 0

Workshop on SEmantic Based Geographic Information Systems Example Constraints satisfied to degree 1

Workshop on SEmantic Based Geographic Information Systems Example Constraints satisfied to degree 0.6

Workshop on SEmantic Based Geographic Information Systems Example Constraints satisfied to degree 0.4

Workshop on SEmantic Based Geographic Information Systems If the set of constraints is inconsistent (i.e. no point satisfies all constraints), we remove a minimal set of constraints such that: –As many constraints as possible are preserved –The area of the fuzzy footprint is as high as possible Imposing constraints is used to improve precision, not recall Some remarks

Workshop on SEmantic Based Geographic Information Systems Bordering regions Footprint can be constructed using the ADL gazetteer

Workshop on SEmantic Based Geographic Information Systems 1.Introduction 2.Constructing fuzzy footprints 3.Experimental results

Workshop on SEmantic Based Geographic Information Systems Evaluation metric Precision: degree to which the fuzzy footprint F is included in the correct footprint G Recall: degree to which the correct footprint G is included in the fuzzy footprint F

Workshop on SEmantic Based Geographic Information Systems 81 political subregions of France, Italy, Canada, Australia and China Divided into three groups: –Regions for which we found more than 30 candidate cities –Regions for which we found less than 10 candidate cities –Regions for which we found between 10 and 30 candidate cities Gold standard: convex hull of the locations that are known to lie in the region according to the ADL gazetteer Test data

Workshop on SEmantic Based Geographic Information Systems Precision Without bordering regions With bordering regions

Workshop on SEmantic Based Geographic Information Systems Without bordering regions With bordering regions Recall

Workshop on SEmantic Based Geographic Information Systems New approach to approximate the footprint of an unknown region Also suitable for vague regions Search for constraints on the web to improve precision Search for bordering regions on the web to improve recall Experimental results confirm this hypothesis Conclusions Thank you for your attention!