Data enrichment is a critical task in the data preparation process of many data science projects where a data set has to be extended with additional information from different sources in order to perform insightful analyses. The most crucial pipeline step is the table reconciliation, where values in cells are mapped to objects described in the external data sources. State-of-the-art approaches for table reconciliation perform well, but they do not scale to huge datasets and they are mostly focused on a single external source (e.g., a specific Knowledge Graph). Thus, the investigation of the problem of scalable table enrichment has recently gained attention. The focus of this talk will be on an experimental approach for reconciling values in tables, which relies on the neural-symbolic reasoning paradigm and that is potentially able to both scale and adapt itself to new sources of information. Preliminary results will be discussed in the last part of the talk.