Interaction-based ontology alignment repair with expansion and relaxation
Authors: Jérôme Euzenat
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The present work aims at eliciting techniques through which agent may adapt these alignments when they find them incorrect. Work has already been performed to understand how this can happen [Euzenat, 2014; Chocron and Schorlemmer, 2016]. We further investigate these by (a) repeating an already performed experiment with updated tools, (b) introducing new similar operators, and (c) introducing new modalities addressing limitations of such operators. The repetition of existing experiments allows us to correct the reference against which initial results were obtained and to show that agents reach alignments closer to the correct alignments than previously reported. We also highlight that, contrary to previously reported, agents may not achieve full correctness (100% precision). This is due to correct correspondences shadowing incorrect ones. We introduce new operators and modalities aiming at addressing these problems: new operators correct faulty correspondences by refining them, the expansion modality adds new correspondences when regular operators destroy correspondences, and the relaxation modality enables agents to use shadowed correspondences. The resulting operators improve the coverage of the alignments through preserving the convergence to 100% success rate, fully correct alignments and a high level of recall (70%). |
| Researcher Affiliation | Academia | J erˆome Euzenat Univ. Grenoble Alpes, Grenoble, France INRIA Jerome.Euzenat@inria.fr |
| Pseudocode | No | The paper describes the adaptation operators (delete, replace, add, refine, addjoin, refadd) in natural language, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using and extending the 'Lazy lavender platform' (http://lazylav.gforge.inria.fr) and providing experiment records, but does not explicitly state that the source code for their specific methods or extensions described in the paper is openly available with a direct link or via supplementary material. |
| Open Datasets | No | The paper describes a synthetic experimental setting where 'Objects in the world are described by a finite set of Boolean features' and 'Ontologies are incomplete'. It also states 'These experiments are randomised'. While it mentions using a 'new reference alignment with 86 rather than 70 correspondences', it does not provide concrete access information (link, DOI, formal citation) to a publicly available dataset used for training or evaluation. |
| Dataset Splits | No | The paper describes an agent interaction framework where 'games' are played and agents adapt alignments. It mentions 'Success rate', 'Precision', 'Recall', 'Incoherence rate', and 'Convergence' as measures, but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or citations to predefined splits) in the traditional machine learning context. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory specifications, or cloud computing resources used for running the experiments. |
| Software Dependencies | No | The paper mentions the 'Lazy lavender platform' and comparison tools 'Log Map Repair' and 'Alcomo', stating 'We used the version of Log Map made available for OAEI 2016'. However, it does not provide specific version numbers for the Lazy Lavender platform itself or other key software dependencies used in their own experimental setup that would ensure reproducibility. |
| Experiment Setup | Yes | We run the Lazy lavender platform with 4 agents over 10000 games. All results are the average of 10 runs (not necessarily the same). Additionally, for the relaxation modality, 'We set the immediate consumption probability to 80% (we found this value very close to the optimum, but make no claim about it here).' |