  Knowledge graphs have been shown to play an important role in recent
knowledge mining and discovery, for example in the field of life sciences or
bioinformatics. Although a lot of research has been done on the field of query
optimization, query transformation and of course in storing and retrieving
large scale knowledge graphs the field of algorithmic optimization is still a
major challenge and a vital factor in using graph databases. Few researchers
have addressed the problem of optimizing algorithms on large scale labeled
property graphs. Here, we present two optimization approaches and compare them
with a naive approach of directly querying the graph database. The aim of our
work is to determine limiting factors of graph databases like Neo4j and we
describe a novel solution to tackle these challenges. For this, we suggest a
classification schema to differ between the complexity of a problem on a graph
database. We evaluate our optimization approaches on a test system containing a
knowledge graph derived biomedical publication data enriched with text mining
data. This dense graph has more than 71M nodes and 850M relationships. The
results are very encouraging and - depending on the problem - we were able to
show a speedup of a factor between 44 and 3839.
