Previous studies have highlighted the necessity for entity linking systems to capture the local entity-mention similarities and the global topical coherence. We introduce a novel framework based on convolutional neural networks and recurrent neural networks to simultaneously model the local and global features for entity linking. The proposed model benefits from the capacity of convolutional neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. Our evaluation on multiple datasets demonstrates the effectiveness of the model and yields the state-of-the-art performance on such datasets. In addition, we examine the entity linking systems on the domain adaptation setting that further demonstrates the cross-domain robustness of the proposed model.

Nowadays, it is widely accepted that the data warehouse design task should be largely automated. Furthermore, the data warehouse conceptual schema must be structured according to the multidimensional model and as a consequence, the most common way to automatically look for subjects and dimensions of analysis is by discovering functional dependencies (as dimensions functionally depend on the fact) over the data sources. Most advanced methods for automating the design of the data warehouse carry out this process from relational OLTP systems, assuming that a RDBMS is the most common kind of data source we may find, and taking as starting point a relational schema. In contrast, in our approach we propose to rely instead on a conceptual representation of the domain of interest formalized through a domain ontology expressed in the DL-Lite Description Logic. We propose an algorithm to discover functional dependencies from the domain ontology that exploits the inference capabilities of DL-Lite, thus fully taking into account the semantics of the domain. We also provide an evaluation of our approach in a real-world scenario.