Abstract
Knowledge graphs are networks with annotated nodes and edges, representing different relations between the network nodes. Learning from such graphs is becoming increasingly important as numerous real-life systems can be represented as knowledge graphs, where properties of selected types of nodes or edges are learned. This paper presents a fully autonomous approach to targeted knowledge graph decomposition, advancing the state-of-the-art HINMINE network decomposition methodology. In this methodology, weighted edges between the nodes of a selected node type are constructed via different typed triplets, each connecting two nodes of the same type through an intermediary node of a different type. The final product of such a decomposition is a weighted homogeneous network of the selected node type. HINMINE is advanced by reformulating the supervised network decomposition problem as a combinatorial optimization problem, and by solving it by a differential evolution approach. The proposed approach is tested on node classification tasks on two real-life knowledge graphs. The experimental results demonstrate that the proposed end-to-end learning approach is much faster and as accurate as the exhaustive search approach.
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Notes
- 1.
The machine used for evaluation was an of-the-shelf Lenovo y510p laptop with an i7 Intel processor (8 cores) and 4 GB of RAM.
- 2.
The feature matrix is not memory efficient, as it uses \(\mathcal {O}(N^{2})\) space, yet optimization of this part of the procedure is out of the scope of this study.
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Škrlj, B., Kralj, J., Lavrač, N. (2018). Targeted End-to-End Knowledge Graph Decomposition. In: Riguzzi, F., Bellodi, E., Zese, R. (eds) Inductive Logic Programming. ILP 2018. Lecture Notes in Computer Science(), vol 11105. Springer, Cham. https://doi.org/10.1007/978-3-319-99960-9_10
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