Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Concept similarity in Formal Concept Analysis-An information content approach Advisor: Dr. Hsu Presenter: Hsin-Yi Huang Authors: Anna Formica KBS.8

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 2008/4/22 2 Outline Motivation Objective Methodology  Formal Concept Analysis  Information content similarity  Concept Similarity Evaluation Conclusion Comments

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 2008/4/22 3 Motivation Assessing concept similarity requires human interaction and is time-consuming and error- prone. The prerequisite of the previous method of the author is the existence of a predefined domain ontology containing similarity degrees. Similarity degrees are established by a panel of experts in the given domain, according to a consensus system.

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 2008/4/22 4 Objective The author propose a method allows user to automatically obtain attribute similarity scores without relying on human domain expertise. tires a motor seats bus truck wagon van RV attributes: objects: related to O is called the concept’s extension, A is called the concept’s intension O: a set of objects A: a set of attributes

N.Y.U.S.T. I. M. Intelligent Database Systems Lab Formal Concept Analysis 2008/4/22 5 1) E={Paris} 3) Derive (E’)’=(Met, Eur, Str)’ = (Paris, Rome) 2) Derive the attributes E’=(Met, Eur, Str) 4) (E’’, E) = ((Paris, Rome),(Met, Eur, Str)) O: a set of objects A: a set of attributes An incidence relation R between O and A Formal context (O, A, R) ((P, Ro),(Met, Eur, Str)) ((A,P, Ro),(Met, Eur, Str)) Arc=Archeological_Site Bea=Beach Met= Metropolis Eur=Euro Str=Stream Ski=Skiing_Area European Cities Concept Lattice

N.Y.U.S.T. I. M. Intelligent Database Systems Lab Information content similarity (ics) 2008/4/22 6 Water Lake Stream Metropolis … WordNet The information content of Water: the probability of a concept : the information content of a concept: The information content of Lake:

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 2008/4/22 7 Concept Similarity =2 (E 1,I 1 )(E 2,I 2 ) EX: (E 1,I 1 )=((A,Ro), (Arc, Bea,Met,Eur)) (E 2,I 2 )=((P,Ro), (Met,Eur, Str)).

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 2008/4/22 8 Evaluation The similarity degree with the information content similarity (ics) scores can be computed without relying on human domain expertise. Evaluating the similarity of attribute names in FCA according to a proposal which provides similarity scores closer to ideal values than other methods defined in the literature. Comparing the intensional components of FCA concepts according to a method that overcomes the limitations of Dice’s function.

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 2008/4/22 9 Conclusion The similarity of the concept intents has been addressed according to the information content approach. This approach by making use of any lexical database for the English language available on the Internet.

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 2008/4/22 10 Comments Advantage  There are many understandable examples Drawback  The proposal lacks benchmarks and experimental results. Application  Formal Concept Analysis  Semantic web