A Probabilistic Approach to Knowledge Translation

TL;DR

This paper introduces a probabilistic method for knowledge translation that enables knowledge reuse across heterogeneous schemas without requiring data, using graphical models to represent and infer knowledge transfer.

Contribution

It presents a novel probabilistic framework for knowledge translation using Markov logic networks, allowing knowledge reuse without data and handling semantic heterogeneity.

Findings

Knowledge translation achieves comparable results to data-dependent methods.

The probabilistic approach effectively models uncertainty in knowledge transfer.

Method applies to both propositional and relational domains.

Abstract

In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema. We refer to this task as "knowledge translation" (KT). Unlike data translation and transfer learning, KT does not require any data from the source or target schema. We adopt a probabilistic approach to KT by representing the knowledge in the source schema, the mapping between the source and target schemas, and the resulting knowledge in the target schema all as probability distributions, specially using Markov random fields and Markov logic networks. Given the source knowledge and mappings, we use standard learning and inference algorithms for probabilistic graphical models to find an explicit probability distribution in the target schema that minimizes the Kullback-Leibler divergence from the implicit distribution. This gives us a compact probabilistic model that represents knowledge from the source schema as well as possible, respecting the uncertainty in both the source knowledge and the mapping. In experiments on both propositional and relational domains, we find that the knowledge obtained by KT is comparable to other approaches that require data, demonstrating that knowledge can be reused without data.