ML Anthology

Xeggora: Exploiting Immune-to-Evidence Symmetries with Full Aggregation in Statistical Relational Models (Extended Abstract)

Mohammad Mahdi Amirian, Saeed Shiry Ghidary
IJCAI 2020 pp. 5010-5014
doi:10.24963/IJCAI.2020/697 /ijcai/2020/amirian2020ijcai-xeggora/

Abstract

We present improvements in maximum a-posteriori inference for Markov Logic, a widely used SRL formalism. Several approaches, including Cutting Plane Aggregation (CPA), perform inference through translation to Integer Linear Programs. Aggregation exploits context-specific symmetries independently of evidence and reduces the size of the program. We illustrate much more symmetries occurring in long ground clauses that are ignored by CPA and can be exploited by higher-order aggregations. We propose Full-Constraint-Aggregation, a superior algorithm to CPA which exploits the ignored symmetries via a lifted translation method and some constraint relaxations. RDBMS and heuristic techniques are involved to improve the overall performance. We introduce Xeggora as an evolutionary extension of RockIt, the query engine that uses CPA. Xeggora evaluation on real-world benchmarks shows progress in efficiency compared to RockIt especially for models with long formulas.

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Text

Amirian and Ghidary. "Xeggora: Exploiting Immune-to-Evidence Symmetries with Full Aggregation in Statistical Relational Models (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/697

Markdown

[Amirian and Ghidary. "Xeggora: Exploiting Immune-to-Evidence Symmetries with Full Aggregation in Statistical Relational Models (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/amirian2020ijcai-xeggora/) doi:10.24963/IJCAI.2020/697

BibTeX

@inproceedings{amirian2020ijcai-xeggora,
  title     = {{Xeggora: Exploiting Immune-to-Evidence Symmetries with Full Aggregation in Statistical Relational Models (Extended Abstract)}},
  author    = {Amirian, Mohammad Mahdi and Ghidary, Saeed Shiry},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {5010-5014},
  doi       = {10.24963/IJCAI.2020/697},
  url       = {https://mlanthology.org/ijcai/2020/amirian2020ijcai-xeggora/}
}