  Sparse Knowledge Graphs (KGs), frequently encountered in real-world
applications, contain fewer facts in the form of (head entity, relation, tail
entity) compared to more populated KGs. The sparse KG completion task, which
reasons answers for given queries in the form of (head entity, relation, ?) for
sparse KGs, is particularly challenging due to the necessity of reasoning
missing facts based on limited facts. Path-based models, known for excellent
explainability, are often employed for this task. However, existing path-based
models typically rely on external models to fill in missing facts and
subsequently perform path reasoning. This approach introduces unexplainable
factors or necessitates meticulous rule design. In light of this, this paper
proposes an alternative approach by looking inward instead of seeking external
assistance. We introduce a two-stage path reasoning model called LoGRe (Look
Globally and Reason) over sparse KGs. LoGRe constructs a relation-path
reasoning schema by globally analyzing the training data to alleviate the
sparseness problem. Based on this schema, LoGRe then aggregates paths to reason
out answers. Experimental results on five benchmark sparse KG datasets
demonstrate the effectiveness of the proposed LoGRe model.
