Improving the Generation of Infoboxes from Data Silos through Machine Learning and the Use of Semantic Repositories

Abstract

Nowadays, both public and private organizations own large private text-based data repositories with critical information. The information stored in these data silos is usually queried through information retrieval systems based on indexes, which yield hundreds or thousands of results when interrogated using keywords. In order to improve data accessibility when searching for specific information, the use of infoboxes can be very useful. The generation such infoboxes is by itself a complex problem, but in this type of isolated environments, it becomes even harder as the selection of the entities and their attributes can be conditioned by local and very specific parameters. In this work, we propose a methodology to tackle this special problem, combining classical approaches with machine learning, and leveraging the resources provided by the Semantic Web. The working methodology has been applied to two well-known datasets, and also it has been tested on a real environment scenario, showing the feasibility of our approach.

Organizational Silos: A Scoping Review Informed by a Behavioral Perspective on Systems and Networks

“…Lastly, functional silos [93] are particularly interesting for the aims of this study, insofar as they are defined as a hierarchical management style [94], rather than the structure of exchanges underlying the management or leadership practices. For example, this view is in contrast with other views of organizational silos: data silos [95,96] and knowledge silos [97,98], according to which the boundaries lie in the content and are not just formal or arbitrary ones.…”