Section: New Results

Cultural knowledge evolution

Our cultural knowledge evolution work currently focusses on alignment evolution.

Agents may use ontology alignments to communicate when they represent knowledge with different ontologies: alignments help reclassifying objects from one ontology to the other. Such alignments may be provided by dedicated algorithms [7], but their accuracy is far from satisfying. Yet agents have to proceed. They can take advantage of their experience in order to evolve alignments: upon communication failure, they will adapt the alignments to avoid reproducing the same mistake.

We performed such repair experiments [2] and revealed that, by playing simple interaction games, agents can effectively repair random networks of ontologies or even create new alignments.

Strengthening modality for cultural alignment repair

Participants : Jérôme Euzenat [Correspondent] , Iris Lohja.

Our previous work on cultural alignment repair achieved 100% precision for all adaptation operators, i.e., all the correspondences in the alignments were correct, but were still missing some correspondences, and did not achieve 100% recall. We had conjectured that this was due to a phenomenon called reverse shadowing [2], avoiding to find specific correspondences.

This year we introduced a new adaptation modality, strengthening, to test this hypothesis. The strengthening modality replaces a successful correspondence by one of its subsumed correspondences covering the current instance. This modality is different from those developed so far, because it leads agents to adapt their alignment when the game played has been a success (previously, it was always when a failure occurred). We defined three alternative definitions of this modality depending on if the agent chooses the most general, most specific or a random such correspondence.

The strengthening modality has been implemented in our Lazy lavender software. We experimentally showed that it was not interferring with the other modalities as soon as the add operator was used. This means that all properties of the previous adaptation operators are preserved. Moreover, as expected, recall was greatly increased, to the point that some operators achieve 99% F-measure. However, the agents still do not reach 100% recall.

Experiment reproducibility through container technology

Participants : Jérôme Euzenat [Correspondent] , Bilal Lahmami.

Performing experiments and reporting them requires care in order for others to be able to repeat them.

We experimented with container technology in order to embed our experiments and offer to others to run them easily. To that extent, we developed scripts associated to the Lazy lavender software to specify, run, and analyse experiments. In particular, these scripts are able to generate a Docker container specification that can perform experiments in the same conditions or with updated software. The documentation of the experiments on our Wiki platform (https://gforge.inria.fr/plugins/mediawiki/wiki/lazylav/index.php/Lazy_Lavender) is also eased by this process.