Medina Andreşel

Medina Andresel, Trung-Kien Tran, Csaba Domokos et al.

Current methods for embedding-based query answering over incomplete Knowledge Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by learning patterns from the data, and lack the complementary ability to do deductive reasoning, which requires the application of domain knowledge to infer further information. To address this shortcoming, we investigate the problem of incorporating ontologies into embedding-based query answering models by defining the task of embedding-based ontology-mediated query answering. We propose various integration strategies into prominent representatives of embedding models that involve (1) different ontology-driven data augmentation techniques and (2) adaptation of the loss function to enforce the ontology axioms. We design novel benchmarks for the considered task based on the LUBM and the NELL KGs and evaluate our methods on them. The achieved improvements in the setting that requires both inductive and deductive reasoning are from 20% to 55% in HITS@3.

Medina Andreşel, Yazmin Ibáñez-García, Magdalena Ortiz et al.

In ontology-based data access (OBDA), ontologies have been successfully employed for querying possibly unstructured and incomplete data. In this paper, we advocate using ontologies not only to formulate queries and compute their answers, but also for modifying queries by relaxing or restraining them, so that they can retrieve either more or less answers over a given dataset. Towards this goal, we first illustrate that some domain knowledge that could be naturally leveraged in OBDA can be expressed using complex role inclusions (CRI). Queries over ontologies with CRI are not first-order (FO) rewritable in general. We propose an extension of DL-Lite with CRI, and show that conjunctive queries over ontologies in this extension are FO rewritable. Our main contribution is a set of rules to relax and restrain conjunctive queries (CQs). Firstly, we define rules that use the ontology to produce CQs that are relaxations/restrictions over any dataset. Secondly, we introduce a set of data-driven rules, that leverage patterns in the current dataset, to obtain more fine-grained relaxations and restrictions.