Keyword-Based Knowledge Graph Exploration Based on Quadratic Group Steiner Trees
Authors: Yuxuan Shi, Gong Cheng, Trung-Kien Tran, Jie Tang, Evgeny Kharlamov
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on public KGs and queries. B3F computes more cohesive answers than the classical GST and it runs in comparable time. 5 Experiments We experimented with a 3.5GHz CPU and 24GB memory. |
| Researcher Affiliation | Collaboration | Yuxuan Shi1,2 , Gong Cheng1 , Trung-Kien Tran2 , Jie Tang3 and Evgeny Kharlamov2,4 1State Key Laboratory for Novel Software Technology, Nanjing University, China 2Bosch Center for Artificial Intelligence, Renningen, Germany 3Department of Computer Science and Technology, Tsinghua University, China 4Department of Informatics, University of Oslo, Norway |
| Pseudocode | Yes | Algorithm 1 B3F |
| Open Source Code | Yes | Code: https://github.com/nju-websoft/B3F . |
| Open Datasets | Yes | We used five versions of three public KGs. They represent small to medium-sized KGs as shown in Table 1. MONDIAL1 (MND) is a geographical KG that has been popularly used for evaluating keyword querying. LUBM2 is a benchmark for generating synthetic KGs in the university domain. From the well-known encyclopedic KG of DBpedia3 we extracted two subgraphs D20K and D100K. |
| Dataset Splits | No | The paper discusses the datasets and queries used for experiments but does not provide specific training/validation/test splits, percentages, or explicit methodology for partitioning data for model validation or cross-validation. |
| Hardware Specification | Yes | We experimented with a 3.5GHz CPU and 24GB memory. |
| Software Dependencies | No | The paper mentions 'py RDF2Vec' as a library used but does not specify its version number or any other software dependencies with explicit version details. |
| Experiment Setup | Yes | For the parameter α in the cost function we compared two values: α = 0.3 and α = 0.7. For the largest allowed depth of search in B3F we set d = 3 since subgraphs of diam > 6 would be too large to present in real applications. |