Two Approaches to Ontology Aggregation Based on Axiom Weakening
Authors: Daniele Porello, Nicolas Troquard, Rafael Peñaloza, Roberto Confalonieri, Pietro Galliani, Oliver Kutz
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our aggregation approaches over 7 ontologies from Bio Portal [Matentzoglu and Parsia, 2017]. We compared the agents happiness of the collective repairing by voting and weakening (Algorithm 1) with the ones of the turnbased procedure (Algorithm 2) by first making the ontologies inconsistent through the addition of random axioms. We also considered, as baselines, the approach unanimity which takes the intersection of the votes of all agents (i.e., the ontology aggregator Fu(O) := {ϕ Φ | |AO ϕ | = k}), and the approach removal which removes random axioms from the (inconsistent) global agenda until consistency is achieved. We made each ontology inconsistent 250 times, by selecting 10% of the axioms in an ontology to generate inconsistent subclass axiom chains. Each time and for each agent i, we randomly generated a preference order <i, and chose the individual ontology to be a consistent set of the best axioms of the agenda w.r.t. <i. We then built up two collective ontologies running Algorithm 1 and Algorithm 2. Finally, we measured the average agent happiness (strict and tolerant) for our aggregation procedures. Our hypothesis was that our procedure would yield better agents happiness than the removaland unanimity-based procedures. Furthermore, we expected the turn-based approach to prove itself better than the voting-based approach insofar as tolerant-based agent happiness is concerned, but not with respect to strict agent happiness, due to the fact that voting allows a majority of agents to reject axioms proposed by others while this is not the case in the turn-based approach. On the other hand, we expected the voting-based procedure to be preferable w.r.t. strict happiness: under this regime a majority of agents can force any axiom they disagree with not to appear in the collective ontology, but in the turn-based approach there is no such guarantee. As shown in Figure 1 and Table 2, the results of our experiments strongly confirm our hypothesis2 and illustrate the benefits of our weakening-based approach to ontology aggregation. |
| Researcher Affiliation | Academia | Free University of Bozen-Bolzano, Faculty of Computer Science, I-39100 Bozen-Bolzano, Italy |
| Pseudocode | Yes | Algorithm 1 Vote Based Collective Ontology(Φ, (<i)i, (Oi)i) |
| Open Source Code | No | No statement regarding the availability of open-source code was found. |
| Open Datasets | Yes | We evaluated our aggregation approaches over 7 ontologies from Bio Portal [Matentzoglu and Parsia, 2017]. |
| Dataset Splits | No | The paper describes generating inconsistent ontologies and agent preferences for experiments but does not provide specific train/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | No specific hardware specifications (e.g., CPU, GPU models, memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions the use of description logics (DLs) and refers to 'refinement operators from [Troquard et al., 2018]', but does not provide specific software names with version numbers for implementation or analysis. |
| Experiment Setup | Yes | We made each ontology inconsistent 250 times, by selecting 10% of the axioms in an ontology to generate inconsistent subclass axiom chains. Each time and for each agent i, we randomly generated a preference order <i, and chose the individual ontology to be a consistent set of the best axioms of the agenda w.r.t. <i. |