Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization
Authors: Gong Cheng, Cheng Jin, Yuzhong Qu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We systematically experiment with our approach on real-world RDF datasets. In this section, we empirically study the quality of summaries for real-world RDF datasets generated by HIEDS under various configurations, compare it with a baseline method, and report its running time. |
| Researcher Affiliation | Academia | Gong Cheng , Cheng Jin, Yuzhong Qu National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1: Computing CPV 0(G) and Algorithm 2: Finding Relations that Satisfy Eq. (11) |
| Open Source Code | No | The paper states 'We have implemented an online prototype of our HIEDS approach.1' with a footnote '1http://ws.nju.edu.cn/hieds/', which links to a project page demonstrating the prototype, but does not provide explicit access to the source code for the methodology. |
| Open Datasets | Yes | Two extensively used RDF datasets were tested. SWDF2 (Semantic Web Dog Food) offers 200K entity-property-value triples describing 20K entities in the research domain (e.g., papers, researchers). Linked MDB3 offers 6M entity-property-value triples describing 0.6M movie-related entities (e.g., actors). Footnotes: 2http://data.semanticweb.org/ 3http://data.linkedmdb.org/ |
| Dataset Splits | No | The paper discusses how groups are iteratively subdivided and evaluated, but does not specify train, validation, or test splits of the datasets themselves, nor does it refer to cross-validation or predefined splits. |
| Hardware Specification | Yes | We tested the running time of HIEDS on an Intel E3-1225 v3 with 30G memory for our Java program. |
| Software Dependencies | No | The paper mentions 'our Java program' and 'Lucene (lucene.apache.org)' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | In this experiment, groups were iteratively subdivided until each leaf group contained not more than 50 entities. We fixed µE = +1 and E = 0.01 (c.f. Algorithm 1), to focus on the effects of other parameters. The following parameters were fixed for simplicity: = β = 0.5, δ = 0, E = R = 0.01, k = 7, and not introducing constraint 4 . |