With the rapid proliferation of knowledge bases and their use in automated inference in a variety of applications, there is a growing need for effective approaches for (i) knowledge translation, i.e., the task of applying knowledge learned or developed in one domain to another semantically different domain, and (ii) knowledge integration, i.e., the task of building a unified knowledge base from disparate sources. While the problem of data translation and integration has received a great deal of attention in the literature, the problem of knowledge integration remains relatively under-explored. Existing work on this topic has focused on primarily logical frameworks which make it difficult to take into account uncertainty in knowledge and in semantic mappings used for knowledge translation and integration.
This project aims to use Markov Logic Networks to express both knowledge and semantic mappings to obtain a unified probabilistic model for jointly translating knowledge and refining semantic mappings. This provides a basis for addressing the challenges of integrating heterogeneous knowledge with uncertain mappings, assessing the correctness of translated knowledge, simplifying the resulting models to obtain more compact approximate translations, and evaluating the methods in realistic application scenarios.
The results of the proposed work are likely to be applicable in a number of application domains that require integration or translation of knowledge across disparate knowledge sources. The work strengthens and facilitates interdisciplinary collaborations, provides enhanced research-based training opportunities for graduate and undergraduate students. The knowledge translation and integration software, and the benchmark data will be made available to the broader community through the project web site: http://aimlab.cs.uoregon.edu/SKTI/