Machine Learning Techniques for Automatic Ontology Extraction 机器学习的本体自动提取技术.ppt

Semantic Class Labeling of Concepts Given: semantic classes T ={T1, ..., Tk } and concepts C = { C1, ..., Cn} Find: a labeling L: C -- T, namely, L(c) identifies the semantic class of concept c for each c in C. For example, C = {voter, poll worker, voting machine} and T = {person, location, artifacts} SCL Na?ve Bayes Learning for SCL Four attributes are used to describe any concept The last 2 characters of the concept The head word of the concept The pronoun following the concept The preposition proceeding the concept Na?ve Bayes Learning for SCL Na?ve Bayes Classifier: Given an instance x = a1, ..., an, and a set of classes Y = {y1, ..., yk} NB(x) = Evaluations On E-voting domain: 622 instances, 6-fold cross-validation: 93.6% prediction accuracy Larger experiment: from WordNet 2326 in the person category 447 in the artifacts category 196 in the location category 223 in the action category 2624 instances from the Reuters data, 6-fold cross-val. produced 91.0% accuracy Reuters data: 21578 Reuters news wire articles in 1987 Attribute Analysis for SCL Non-taxonomical relation learning We focus on learning non-hierarchical relations of form Ci, R, Cj Here R is a non-hierarchical relation, and Ci, Cj are concepts Example relations: voter, cast, ballot official, tell, voter machine, record, ballot Related Works Non-hierarchical relation learning is relatively less tackled Several works on this problem make restrictive assumptions: Define a fixed set of concepts, then look for relations among these concepts Define a fixed set of non-hierarchical relations, then look for concept pairs satisfying these relations Syntactical structure of the form (subject, verb, object) is often used Ciaramita et al(2005): Use a pre-defined set of relations Extract concept pairs satisfying such a relation Use chi-square test to verify the statistical significance Experimented with the Mole