Efficient Symbolic Learning over Knowledge Graphs

Abstract

Knowledge Graphs (KG) are repositories of structured information. Inductive Logic Programming (ILP) can be used over these KGs to mine logical rules which can then be used to deduce new information and learn new facts from these KGs. Over the years, many algorithms have been developed for this purpose, almost all requiring the complete KG to be present in the main memory at some point of their execution. With increasing sizes of the KGs, owing to the improvement in the knowledge extraction mechanisms, the application of these algorithms is being renderedless and less feasible locally. Due to the sheer size of these KGs, many of them don’t even fit in the memory of normal computing devices. These KGs can, however, also be represented in RDF making them structured and queriable using the SPARQL endpoints. And thanks to software like Openlink’s Virtuoso, these queriable KGs can be hosted on a server as SPARQL endpoints. In light of this fact, an effort was undertaken to develop an algorithm that overcomes the memory bottleneck of the current logical rule mining procedures by using SPARQL endpoints. To that end, one of the state-of-the-art algorithms AMIE was taken as a reference to create a new algorithm that mines logical rules over these KGs by querying the SPARQL endpoints on which they are hosted, effectively overcoming the aforementioned memory bottleneck, allowing us to mine rules (and eventually deduce new information) locally.