Approximating Numeric Role Fillers via Predictive Clustering Trees for Knowledge Base Enrichment in the Web of Data

In the context of the Web of Data, plenty of properties may be used for linking resources to other resources but also to literals that specify their attributes. However the scale and inherent nature of the setting is also characterized by a large amount of missing and incorrect information. To tackle these problems, learning models and rules for predicting unknown values of numeric features can be used for approximating the values and enriching the schema of a knowledge base yielding an increase of the expressiveness, e.g. by eliciting SWRL rules. In this work, we tackle the problem of predicting unknown values and deriving rules concerning numeric features expressed as datatype properties. The task can be cast as a regression problem for which suitable solutions have been devised, for instance, in the related context of RDBs. To this purpose, we adapted learning predictive clustering trees for solving multi-target regression problems in the context of knowledge bases of the Web of Data. The approach has been experimentally evaluated showing interesting results.