Principles from nature-inspired selforganization can help to attack the massive scalability challenges in future internet infrastructures. We researched into ant-like mechanisms for clustering semantic information. We outline algorithms to store related information within clusters to facilitate efficient and scalable retrieval.
At the core are similarity measures that cannot consider global information such as a completely shared ontology. Mechanisms for syntax-based URI-similarity and the usage of a dynamic partial view on an ontology for path-length based similarity are described and evaluated. We give an outlook on how to consider application specific relations for clustering with a usecase in geo-information systems.
- What about a similarity function where “sim = 1.0?”
- What about ants with different similarity functions?
- The similarity measure is RDF bound. What other similarity measures are in use?
Observation: The Wordnet Ontology is used for the evaluation. It occurred to me that Wordnet gets used a lot, but never reused. Or rather, the results of using Wordnet are never reused.
Isn’t it odd that we keep reasoning about sparrows being like ducks, over and over again? Seems like we should be able to take the results of others and build upon them. What prevents that from happening? Either in searching or ontology systems.