Towards Log-Linear Logics with Concrete Domains
This work addresses a specific challenge in knowledge representation and reasoning for AI, focusing on enhancing ontology learning with concrete data types, which is incremental as it builds upon existing log-linear description logics and Markov logic networks.
The paper tackles the problem of extending log-linear description logics with concrete domains, nominals, and instances to find the most probable, classified, and coherent ontology from a knowledge base, resulting in a novel method for handling concrete domains by extending the cutting plane inference algorithm in Markov logic networks.
We present $\mathcal{MEL}^{++}$ (M denotes Markov logic networks) an extension of the log-linear description logics $\mathcal{EL}^{++}$-LL with concrete domains, nominals, and instances. We use Markov logic networks (MLNs) in order to find the most probable, classified and coherent $\mathcal{EL}^{++}$ ontology from an $\mathcal{MEL}^{++}$ knowledge base. In particular, we develop a novel way to deal with concrete domains (also known as datatypes) by extending MLN's cutting plane inference (CPI) algorithm.