HUKA is a framework which maintains result of graph queries along with their provenance under dynamic knowledge graphs (KG). Addition or removal of new information in a knowledge graph often leads to insertion or deletion of an edge in the KG. HUKA efficiently identifies and update precomputed answers of all the registered queries which would be affected by a given change in the KG. HUKA currently supports positive conjunctive SPARQL queries. The provenance model employed is an adaptation of the popular how-provenance model, provenance semiring.
-
An installation of
Neo4jgraph database server along with bulk-import utility. The framework currently uses Neo4j 3.9.9 version. You can download Neo4j from their official download page (https://neo4j.com/download-center/#enterprise). Unzip the folder and rename it to neo4j and place it in lib/ directory of the file -
Setup open-source relational database
MariaDBserver https://mariadb.org/download/. HUKA uses MariaDB 2.4.1. Also, download MariaDB JDBC connector from https://downloads.mariadb.org/connector-java/+releases/ and put the .jar files in directory lib/. -
Apache Jena, a freely available Java framework for semantic web applications, can be downloaded from https://jena.apache.org/download/index.cgi. Unzip and rename the downloaded folder to jena and move it to the lib/ directory of the repository. -
Finally, download Google core java library guava.jar from http://www.java2s.com/Code/Jar/g/Downloadguavajar.htm and move it directory lib/. directory.
The format of 3 files which user needs to supply to the framework are as following,
- A tab-separated file factFile.tsv listing all the facts of the knowledge graph as triples.
<subject1-URI> <predicate1-URI> <object1-URI>
..
<subject-URI> <predicate1-URI> <object1-URI>
- A file, rawQueryList.txt, containing all the sparql queries which user wants to register with HUKA for maintenance. Each line should contain a single query as shown below,
SPARQL-Query1
..
SPARQL-Queryn
- Lastly, a tab-separated file updateRequest.txt listing down all the edge update (insertion/deletion) requests in the following format,
<OutgoingVertexId_i> <IncomingVertexId_i> <OutgoingVertexLabel_i> <IncomingVertexLabel_i> <EdgeLabel_i> <EdgeId_i> <Operation_i (I/D)>
..
<OutgoingVertexId_j> <IncomingVertexId_j> <OutgoingVertexLabel_j> <IncomingVertexLabel_j> <EdgeLabel_j> <EdgeId_j> <Operation_j (I/D)>
A sample of each expected file is given in sample/ directory. These sample files have headers for the convenience of explaining the data, however original files do not require headers.
HUKA performs three main task (in order) -- creating and populating databases, registering queries and then finally, handling KG update requests. We next provide details of how to perform each task, along with their input file format.
- Database construction: Run the bash script prepareDataFile.sh in directory scripts. It works with the file containing list of all the triples consisting a dataset.
cd scripts
./prepareDataFile.sh <factFile> <datasetName>
After execution of prepareDataFile.sh, the fact file could be found in directory /meta/dataset/kg/raw/. Before, next two tasks, query registration and maintaining query results, set few parameters in conf file. The parameter values which a user needs to set are marked with * in conf file.
- Query Registration: A bash script query_registration.sh compiles and runs query registration module which build all required supporting data structures.
./query_registration.sh <queryFile> <datasetName>
- Update request handling: Run update.sh with the updateRequests.txt file listing all the update requests.
./update.sh <updateFile> <datasetName>
HUKA is provided as open-source software under the MIT License. See LICENSE.
https://github.com/gaurgarima/HUKA
Garima Gaur garimag@cse.iitk.ac.in