GraphQ Trans is a source-to-source compiler that supports the transpilation among multiple graph query languages via a unified intermediate representation. You may install this package via pip:

pip install graphq-trans

For our demonstration website, please visit https://graphqtrans.xlore.cn/

This package has the following dependencies:

• Python >= 3.6.2

• ANTLR >= 4.9.2

• antlr4-python3-runtime >= 4.9.2

This toolkit relies on ANTLR4 for front-end analysis, please refer to their tutorial for setup.

GraphQ Trans provides a set of easy-to-use APIs for transpiling graph query languages. Here's a simple example of how to use the toolkit to convert between two graph query languages:

from graphq_trans.sparql.translator import Translator as SparqlTranslator
from graphq_trans.ir.translator import Translator as IRTranslator

sparql_translator = SparqlTranslator() # Create a SparqlTranslator that translates SPARQL to graphqIR
ir_translator = IRTranslator() # Create a IRTranslator that translates graphqIR to Cypher

# the SPARQL query for "Get all entities that are human"
sparql_query = 'SELECT DISTINCT ?e WHERE { ?e <pred:instance_of> ?c . ?c <pred:name> "human" } '

ir = sparql_translator.to_ir(sparql_query) # translates sparql to ir
cypher_query = ir_translator.to_cypher(ir) # translates ir to cypher
print(cypher_query) 

The returned Cypher query will be:

MATCH (n1:human)
RETURN n1.name

Our SPARQL naming rules are adopted from KBQA dataset KQA Pro. For variables, we have

For common predicates, we have

For convenience of KBQA, we design the transpiler based on the need of getting the labels directly. Therefore, to query variable x, please use

...
RETURN x.name
...

For KoPL, we support all styles that satisfy the language grammar. Please refer to their repository for detailed documentation: https://github.com/THU-KEG/KoPL

For lambda-DCS, please refers to the original paper (Liang, 2013) for more specific instructions

If you find our work helpful, please cite it as follows:

@inproceedings{nie2022graphq,
  title={GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation},
  author={Nie, Lunyiu and Cao, Shulin and Shi, Jiaxin and Sun, Jiuding and Tian, Qi and Hou, Lei and Li, Juanzi and Zhai, Jidong},
  booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  pages={5848--5865},
  year={2022}
}