Retrofitting Soft Rules for Knowledge Representation Learning

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Learning Soft Inference Rules in Large and Uncertain Knowledge Bases

Recent progress in information extraction has enabled us to create large semantic knowledge bases with millions of RDF facts extracted from the Web. Nevertheless, the resulting knowledge bases are still incomplete or might contain inconsistencies, either because of the heuristic nature of the extraction process, or due to the varying reliability of the Web sources from which they were collected...

متن کامل

Learning-based Knowledge Representation

This paper presents a learning-based representation of knowledge which is at the basis of the family of Disciple learning agents. It introduces a representation for concepts, generalization and specialization rules, different types of generalizations and specializations, and the representation of the main elements of a knowledge base, including partially learned concepts, problems, and rules. F...

متن کامل

Knowledge Representation in Description Logic with Rules

Description logic SHOIQ is widely used for defining ontologies. Recently, rules are added to DL to increase its expressive power. We present a reasoner for DL with rules that translates the DL reasoning task into first order logic, performs reasoning in FOL, and interprets the result in terms of DL with rules. We compare the performance of our reasoner with tableax-based reasoners for DL (which...

متن کامل

Knowledge Representation Representation Formalisms in Machine Learning

We begin with what seems a paradox. The world of experience of any normal man is composed of a tremendous array of discriminably different objects, events, people, impressions...But were we to utilize fully our capacity for registering the differences in things and to respond to each event encountered as unique, we would soon be overwhelmed by the complexity of our environment... The resolution...

متن کامل

Evolutionary Generation of Implicative Fuzzy Rules for Design Knowledge Representation

In knowledge representation by fuzzy rule based systems two reasoning mechanisms can be distinguished: conjunction-based and implication-based inference. Both approaches have complementary advantages and drawbacks depending on the structure of the knowledge that should be represented. Implicative rule bases are less sensitive to incompleteness of knowledge. However, implication-based inference ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Big Data Research

سال: 2021

ISSN: 2214-5796

DOI: 10.1016/j.bdr.2020.100156