semantic similarity measurement and geographic applications introduction

3 Apr 2008 geog 288MR, spring 08 1

Semantic Similarity Measurement and Geographic Applications

Introduction

Dr. Martin Raubal

Department of Geography, UCSB

[email protected]


Overview

• Instructor

• Organization

• Goals

• Schedule

• Introduction to the topic


Instructor

• Instructor: Dr. Martin Raubal, [email protected]://www.geog.ucsb.edu/~raubal/

• Office (hours): Wednesday 2-4pm, EH 5713

• Phone: 893-4839


Class meets

• Thursday 1-3pm, EH 5824


Textbook & literature

• No textbook

• Literature online: http://www.geog.ucsb.edu/~raubal/Courses/288MR_Spring08_Papers/


Organization

• Course Website: http://www.geog.ucsb.edu/~raubal/Courses/288MR_SemSim_Spring08_schedule.pdf

• Weekly readings

• Class participation is mandatory!


Assignments

• Weekly brief critical commentaries (not longer than 1 page each) plus 3 questions connected to the readings (possibly related to your own field of study) to be discussed in class.

• Please hand in a printed version at the beginning of each session. Late assignments will not be accepted and count as failed.

• Lead one session including presentation of the weekly material (and possibly additional material) and discussion.


Grading

• Your presentation & discussion lead (35%)

• Written assignments (35%)

• General class participation (30%)

Registration 2-4 hours!?


Goals

• Identify and recognize the importance of semantic similarity measurement for GI Science & Systems (data retrieval, integration, etc.).

• Explore theoretical models and practical applications.

• Get to know relevant literature.

• Discuss, write, and present.


Schedule

http://www.geog.ucsb.edu/~raubal/Courses/288MR_SemSim_Spring08_schedule.pdf

Take the lead for one session.


You

• Who are you and what is your background?

• What is your motivation to participate in this class?

• Semantic similarity measurement? Any experience?

• How does it relate to your own research?


Applications!?

• Think of applications where semantic similarity is of relevance for GI Sciences.


Motivating example (1)

• Customer of OS wants to set up flood warning system.

• Need for existing flooding areas to analyze current flood defense situation in U.K.


• OS Master Map:– geographic & topographic– information on areas used for flooding but not

designated as such

• ‘Watermeadow', 'carse‘, 'haugh' identified as flooding areas by their semantic description only (properties in ontology).



User conceptualization of roads & residential areas

System model of roads & residential areas

Roads overlap residential areas?

Intersect to find roads going through residential areas



Semantic interoperability

• “Capacity of (geographic) information systems and services to work together without the need for human intervention” (Harvey, Kuhn et al. 1999)

• Achieving sufficient degree of semantic interoperability => necessary to determine semantic similarity between concepts.


Similarity (psychology)“Similarity is fundamental for

learning, knowledge and thought, for only our sense of similarity allows us to order things into kinds so that these can function as stimulus meanings. Reasonable expectation depends on the similarity of circumstances and on our tendency to expect that similar causes will have similar effects" [Quine 1969, p. 114].


Computer science

• Similarity plays major role to enable machine-based solutions: decision support systems, data mining, pattern recognition.

• Semantic information retrieval: similarity indicates relevance of results with regard to being similar to the query.


Concept

A concept is "a mental representation of a class or individual and deals with what is being represented and how that information is typically used during the categorization" [Smith 1989, p. 502].

Concept vs. Category?


Concepts in knowledge representation

• Conceptual knowledge can be represented in ontologies that consist of specifications of concepts, relations and axioms.

• Relations link concepts together and enable reasoning and measurement within an ontology.

• Taxonomical (hierarchical) relations are the most important for reasoning and structuring knowledge.


dist (Bus, Ferry) < dist (Bus, Bike)


Similarity measurements

• Approaches from different research areas (psychology, computer science, artificial intelligence) => apply to ontology-based semantic similarity measurement.

• Application areas:– Information retrieval & integration– Data mining & maintenance– Categorization– Natural-language processing– Pattern recognition


Measure and representation

Representational model used to describe concepts determines semantic similarity measure (based on one notion of similarity).

Representation => similarity measure


Semantic similarity measurement

• How close are two entities to each other conceptually?

• Value between 0 and 1:– ‘0’ => no similarity– ‘1’ => both entities are equal

• Different measurement theories.


Schwering (2008)

relations not explicitly represented


Topics tackled in this seminar

• Psychological models to explain human similarity judgment

• Approaches to similarity in different areas / disciplines

• What is representation?• Representation of geographic semantics• Semantic interoperability



• Theories for measuring semantic similarity– Feature-based models– Geometric models

• Similarity measurement in context• Semantic similarity vs. semantic

relatedness• Measurements using a semantic similarity

serverhttp://128.176.133.142:8080/SimInterface/


• Use of similarity measurement in geographic applications:– Geographic information retrieval– Hydrography– Land use / land cover classification– Wayfinding and landmarks– World knowledge in Wikipedia– …



Reading for next session

• R. Goldstone and J. Son (2005) Similarity. in: K. Holyoak and R. Morrison (Eds.), Cambridge Handbook of Thinking and Reasoning. Cambridge University Press, Cambridge.

• Schwering, A., Approaches to Semantic Similarity Measurement for Geo-Spatial Data: A Survey. Transactions in GIS, 2008. 12(1): p. 5-29.

• A. Rissland (2006) AI and Similarity. IEEE Intelligent Systems (May-June 06): 39-49.

semantic similarity measurement and geographic applications introduction

Documents

semantic description

motivating example

class meetsthursday

existing flooding areas

general class participation

practical applications

gi sciences

readingsclass participation