The subgraph matching problem is a fundamental problem in graph theory that is known to be NP-complete. In this study, performers were asked to develop algorithms to search for semantic property graphs that were subgraphs of a large knowledge graph. The templates provided contained structural information about the subgraphs and some attributes for each node and edge. There also exists a similarity measure between a set of attribute values that occurs on every node and edge. Algorithms performed well in the case where an exact match existed, but performers were also provided templates that had noise added such that there existed no match in the knowledge graph. Performers were asked to find the closest matches to those noisy subgraphs. To evaluate performance on this task, we developed a version of the graph edit distance algorithm to measure the cost of editing the template graph so that it is isomorphic in structure and attributes to the performer submission.

In the real world, data does not come neatly packaged. Instead, it typically comes as small updates from many sources with different conventions. Building a single, cohesive knowledge-base to work from requires merging small updates from many different sources. This paper outlines methods we have investigated for scoring merging routines. Given a challenge problem consisting of a large knowledge-base and a set of smaller documents, algorithms are asked to identify alignment points between the smaller document and the knowledge base. This paper surveys options for evaluating such algorithms, providing notes on strengths, weaknesses and considerations for interpretation.