Categorical and Numeric Relations Dataset

Collaboratively constructed knowledge bases play an important role in information systems, but are essentially always incomplete. Thus, a large number of models has been developed for Knowledge Base Completion, the task of predicting new attributes of entities given partial descriptions of these entities. Virtually all of these models either concentrate on numeric attributes (what is Italy’s GDP?) or they concentrate on categorical attributes (Tim Cook is the chairman of Apple). This dataset was created as a part of a research experiment to develop a model for the joint prediction of numeric and categorical attributes based on embeddings learned from textual occurrences of the entities in question. This dataset consists of numeric and categorical relation tuples spanning from 7 different domains such as 'animal', 'country', 'people', etc. The tuples presented in this dataset have been used to train and test a neural network framework to perform the above mentioned task. All data presented in this dataset has been scraped from FreeBase.****FORTHCOMING PUBLICATION: the paper corresponding to this dataset will be available soon****