Inductive Logical Query Answering in Knowledge Graphs
Authors: Michael Galkin, Zhaocheng Zhu, Hongyu Ren, Jian Tang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we show that inductive models are able to perform logical reasoning at inference time over unseen nodes generalizing to graphs up to 500% larger than training ones. Exploring the efficiency effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes. Code is available at https://github.com/Deep Graph Learning/Inductive QE. |
| Researcher Affiliation | Academia | Mikhail Galkin Mila, Mc Gill University mikhail.galkin@mila.quebec Zhaocheng Zhu Mila, Université de Montréal zhuzhaoc@mila.quebec Hongyu Ren Stanford University hyren@stanford.edu Jian Tang Mila, HEC Montréal, CIFAR AI Chair jian.tang@hec.ca |
| Pseudocode | No | The paper describes algorithms and methods in prose but does not include any explicit pseudocode blocks or sections labeled 'Algorithm'. |
| Open Source Code | Yes | Code is available at https://github.com/Deep Graph Learning/Inductive QE. |
| Open Datasets | Yes | We create a novel suite of datasets based on FB15k-237 [29] (open license) and following the query generation process of Beta E [24]... Finally, we perform a scalability experiment evaluating complex query answering in the inductive mode on a new large dataset Wiki KG-QE constructed from OGB Wiki KG 2 [16] (CC0 license). |
| Dataset Splits | Yes | For validation and test graphs, we split the remaining set of entities into two non-overlapping sets each with 1 r / 2 |E| nodes. We then merge training and unseen nodes into the inference set of nodes Einf and induce inference graphs for validation and test from those sets, respectively, i.e., Eval inf = Etrain [ Eval and Etest inf = Etrain [ Etest. ...Training queries are sampled from the training graph Gtrain, validation and test queries are sampled from their respective inference graphs Ginf... |
| Hardware Specification | Yes | Node Piece-QE models were pre-trained and evaluated on a single Tesla V100 32 GB GPU whereas GNN-QE models were trained and evaluated on 4 Tesla V100 16GB. |
| Software Dependencies | No | The paper states: 'Both Node Piece-QE and GNN-QE are implemented4 with Py Torch [22] and trained with the Adam [18] optimizer.' However, it does not provide specific version numbers for PyTorch, Adam, or any other software libraries. |
| Experiment Setup | Yes | The non-parametric CQD-Beam [5] decoder for answering complex queries is tuned for each query type based on the validation set of queries, most of the setups employ a product t-norm, sigmoid entity score normalization, and beam size of 32. Following the literature, the GNN-QE models [40] were trained on 10 query patterns (1p/2p/3p/2i/3i/2in/3in/inp/pin/pni) where ip/pi/2u/up are only seen at inference time. Each model employs a 4-layer NBFNet [42] as a trainable projection operator with Dist Mult [33] composition function and PNA [10] aggregation. |