Ant Colony Optimisation for Automatically Populating Ontologies with Individuals

With the rapid spread of the social web and of information retrieval systems, the need of structuring information and of making it more accessible for automatic evaluation has also increased, therefore justifying the use of semantic repositories as knowledge bases and enabling the transition to a semantic web. Besides defining an ontology in terms of concepts and relations, the actual process of populating an ontology with individuals has become a more and more time-consuming task due to the multitude of information sources. Therefore, the extraction of proper individuals may be very difficult when one is dealing with such amount of information freely available on the web and with so many classes within the hierarchy. On the other side, the classical approach of populating ontologies consists of parsing the input and matching it against certain regular expressions. Due to its intrinsic limitations, we propose a novel approach based on the Ant Colony Optimization (ACO) algorithm that is used to create rules by reading the predefined ontologies (mostly generated via expert knowledge) and later to apply them on the given text. The ants build individuals iteratively by searching keywords in the text that better fit as values for the attributes defined within each ontology class. The validation results prove that our method provides more specific individuals than classic pattern matching techniques as the algorithm scans every word from the text in order to compare it against a list of keywords.