SessionCQA

The repository holds the code for mining logical queries/creating user intention KG from session data.

Data

Just download the data by clicking this link, and extract them in root of this repository. Three datasets are all included. There is more pre-processing for LSGT and some of the baselines, so please remember that using the correct corresponding files otherwise will cause data-loading errors. It is too abstract to describe how to run the code because there are many combinations of logical encoder, session encoder, and datasets. We make it clearer by the following example scripts, and you can substitute the flags.

Baselines of Logical Encoder + Session Encoder

You can run the following script for logical encoder + session encoder on the Amazon dataset

python ./deduction_model/train.py \
-dn hyper_graph_data_en \
-m fuzzqe \
--train_query_dir ./sampled_hyper_train_merged \
--valid_query_dir ./sampled_hyper_valid \
--test_query_dir ./sampled_hyper_test \
--checkpoint_path ./logs \
-fol  \
-b 512 \
--log_steps 120000 \
-lr 0.001 \
--session_encoder AttnMixer

The -m fuzzqe denotes the logical encoder which we use here. The ```--session_encoder''' denotes the session encoder here. In this implementation, we implemented the combinations of these two flags, which have the following options.

Baseline Logical Encoders

The baseline logical encoder implementations are adopted from the SQE repo, a general code base. Here is the code for the

Model Flag (-m)	Paper
gqe	Embedding logical queries on knowledge graphs
q2b	Query2box: Reasoning over knowledge graphs in vector space using box embeddings
betae	Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
cone	Cone: Cone embeddings for multihop reasoning over knowledge graphs
q2p	Query2Particles: Knowledge Graph Reasoning with Particle Embeddings
fuzzqe	Fuzzy Logic Based Logical Query Answering on Knowledge Graphs
tree_lstm	Sequential Query Encoding for Complex Query Answering on Knowledge Graphs

Session Encode

Here are the options for the session encoder flags.

Session Encoder Flag (--session_encoder)	Paper
GRURec	Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
SRGNN	Session-based Recommendation with Graph Neural Networks
AttnMixer	Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network

Baselines of NQE and SQE on Dressipi

python ./deduction_model/train.py \
-dn hyper_graph_dressipi \
-m nqe \
--train_query_dir ./sampled_hyper_train_dressipi_merged \
--valid_query_dir ./sampled_hyper_valid_dressipi \
--test_query_dir ./sampled_hyper_test_dressipi \
--checkpoint_path ./logs \
-fol  \
-b 512 \
--log_steps 120000 \
-lr 0.0001

Running LSGT on Diginetica

Note that there is a trailing _dgl in the queries dirs, in which we use dgl to convert the query graph into graph tokens.

python ./deduction_model/train.py \
-dn hyper_graph_diginetica \
-m lsgt \
--train_query_dir ./sampled_hyper_train_diginetica_merged_graph_dgl \
--valid_query_dir ./sampled_hyper_valid_dininetica_graph_dgl \
--test_query_dir ./sampled_hyper_test_diginetica_graph_dgl \
--checkpoint_path ./logs \
--num_layers 2 \
-fol  \
-b 1024 \
--log_steps 120000 \
-lr 0.001

Scripts to reproduce the result in this paper

We also include the shell scripts that we run to reproduce the number shown in the paper.

Others

If anything is unclear, please get in touch with Jiaxin Bai via jbai@connect.ust.hk. If you find this useful, please cite:

@article{DBLP:journals/corr/abs-2312-13866,
  author       = {Jiaxin Bai and
                  Chen Luo and
                  Zheng Li and
                  Qingyu Yin and
                  Yangqiu Song},
  title        = {Understanding Inter-Session Intentions via Complex Logical Reasoning},
  journal      = {CoRR},
  volume       = {abs/2312.13866},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2312.13866},
  doi          = {10.48550/ARXIV.2312.13866},
  eprinttype    = {arXiv},
  eprint       = {2312.13866},
  timestamp    = {Wed, 17 Jan 2024 17:14:38 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2312-13866.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}