Talk at UMBC
2005-03-07
Yoshio Fukushige
W3C Fellow / Matsushita Electric Industrial Co., Ltd.(Panasonic)
Today's talk consists of:
Name | Yoshio Fukushige 福重 貴雄 |
---|---|
Affiliate | Network Systems Development Center, Matsushita Electric Industrial Co., Ltd. fukushige.yoshio@jp.panasonic.com |
W3C fellow, based at Keio Univ. fuku@w3.org |
|
Research domain | Semantic Web (cf. RDF Data Access Working Group), Text classification/clustering, Application of Bayesian Network |
The aim of my ongoing work is to provide
and to provide them as a strawman for standard / best practice in processing uncertain information in RDF.
(Provide as a Web Service?)
|
|
|
||||||||||||||||||||||||||||||||||
|
|
A node in BN is represented as a node of type prob:ProbabilisticStatements.
The targets of description are probabilistic relations between events. An event is represented as a prob:Clause node. (prob:Event, prob:State or prob:Proposition are better namings?) A prob:Clause node has a predicate node and role nodes (all are instances).
e.g. Clause node with * represents a High Serum Calcium event
SerumCalcium | |||
---|---|---|---|
TRUE | FALSE | ||
Metastatic Cancer |
TRUE | 0.80 | 0.20 |
FALSE | 0.20 | 0.80 |
e.g. Mary has a headache with probability of 70%.
e.g. Mary has a metastatic cancer with probability of 9.7%.
I have examined the vocabulary needed for representing probabilistic relations in RDF, and now am writing code for transformation to BN. Query language and protocol are yet to be examined.
Remaining issues are among others,
My approach | BayeseOWL | ||
---|---|---|---|
main target | Causal relationship (RDF) | Ontological relationship (OWL) | |
basic proposition | prob:Clause | Variable (?) | |
conditions are attached to | prob:ProbabilisticStatements (distribution) | CondProb (case in distribution) | |
probability | prob:Probability | literal |
How can these approaches help each other?
In order not to make it a ground statement (= which is said to be real). The graph below says that Mary really has high serum calcium
Suppose Hans may have dropped an ax. Clause 1 claims that it is a golden ax, and clause 2 claims that it is a silver ax. Both agree that he may have dropped something.