Probabilistic Inference

Dongjoo Lee

IDS Lab.

School of Computer Science and Engineering

Seoul National University

Copyright 2008 by CEBT

Probabilistic Inference

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Query

Conceptual Knowledge

Physical Data

Rule Database

SQL

not a procedural querygraphical expression can be usedjust describes what has to be retrievedwhat can mean ‘type of entity’, ‘conditional properties of entity’, and so on…

query have to be converted into SQL in order to access RDB

rule database includes the informationabout which table stores entities and relationsand attributes

Copyright 2008 by CEBT

Issues

Which objects does a user want?

semantic, personalization, context awareness

Query Model

graphical query expression

aided by visualization tools

SQL Conversion

How to map entities and relations at the conceptual level to the tables in target physical storages.

query optimization

virtual views? technical dictionaries?

– Ontoms

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Copyright 2008 by CEBT

To Do

Select target domain

Make a feasible scenario

Make a model

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Copyright 2008 by CEBT

Reference

Tao Cheng et al., VLDB2007, Entity Rank: Searching Entities Directly and Holistically 웹 문서에서 Entity 를 판별하고 , Retrieve

Dan Roth, Wen-tau Yih, 2002, Probabilistic Reasoning for Entity & Relation Recognition NLP

R. Haenni, et al., Probabilistic Argumentation Systems

Rolf Haenni, LNCS 2005, Towards a Unifying Theory of Logical and Probabilistic Reason-ing

Simona Colucci et al., SEBD 2005, Semantic-Based Resource Retrieval using Non-Standard Inference Services in Description Logics Request 에 대해서 Object 들이 얼마나 만족하는지를 DL 의 일부 표현 방법들을 빌어 사용

저자들이 다양한 논문을 발표했고 , Abduction, Contraction 이라는 개념을 통해 Semantic 을 적용하려는 시도를 많이 하고 있음 . Set 을 활용한 간단한 수준인 것 같음 .

e-Catalog 같은 현업에서의 이슈임

Michael I. Jordan, Probabilistic inference in graphical models

Peter A. Flach et al., 2002, Probabilistic reasoning with terms term 을 이용하여 확률적으로 first order logic 의 lists, trees, tuples 그리고 sets, multisets 를

표현하여 logic 에 확률을 접목

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