Pay-As-You-Go OWL Query Answering Using a Triple Store
Authors: Yujiao Zhou, Yavor Nenov, Bernardo Cuenca Grau, Ian Horrocks
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have implemented these techniques in a prototype system, a preliminary evaluation of which has produced very encouraging results. ... We have implemented our procedure in a prototypical system using the RDFox triple store (Motik et al. 2014) and we present a preliminary evaluation over both benchmark and realistic data which suggests that the system can provide scalable pay-as-you-go query answering for a wide range of OWL 2 ontologies, RDF data and queries. ... We compared our system with Pellet (v. 2.3.1) and Tr OWL (Thomas, Pan, and Ren 2010) on all datasets. |
| Researcher Affiliation | Academia | Yujiao Zhou and Yavor Nenov and Bernardo Cuenca Grau and Ian Horrocks Department of Computer Science, University of Oxford, UK |
| Pseudocode | No | The paper describes a method for tracking rules and facts in a datalog program by extending it with additional rules, and defines a function (K, F), but it does not present this or any other procedure in a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper mentions implementing a prototype system using RDFox but does not provide a link or explicit statement that *their* code (the prototype) is open-source or publicly available. The footnote for 'Data, ontologies, and queries are available online' points to resources but not the system's source code. |
| Open Datasets | Yes | In our experiments we used the LUBM and UOBM benchmarks, as well as the Fly Anatomy ontology, DBPedia and NPD Fact Pages (their features are summarised in Table 3). Data, ontologies, and queries are available online.2. [footnote:] 2http://www.cs.ox.ac.uk/isg/people/yujiao.zhou/#resources |
| Dataset Splits | No | The paper uses standard benchmarks like LUBM and UOBM but does not explicitly describe training/validation/test splits, percentages, or sample counts for these datasets within the text. It focuses on materialization and query answering times. |
| Hardware Specification | Yes | Tests were performed on a 16 core 3.30GHz Intel Xeon E5-2643 with 125GB of RAM, and running Linux 2.6.32. |
| Software Dependencies | Yes | We have implemented a prototype based on RDFox and Hermi T (v. 1.3.8). |
| Experiment Setup | No | The paper describes the overall strategy for evaluating their system (e.g., measuring materialization times, query answering times, comparing with other systems) but does not provide specific experimental setup details like hyperparameter values (e.g., learning rates, batch sizes), model initialization, or specific training schedules. |