Ontology similarity in the alignment space

david2010bInternational audienceMeasuring similarity between ontologies can be very useful for different purposes, e.g., finding an ontology to replace another, or finding an ontology in which queries can be translated. Classical measures compute similarities or distances in an ontology space by directly comparing the content of ontologies. We introduce a new family of ontology measures computed in an alignment space: they evaluate the similarity between two ontologies with regard to the available alignments between them. We define two sets of such measures relying on the existence of a path between ontologies or on the ontology entities that are preserved by the alignments. The former accounts for known relations between ontologies, while the latter reflects the possibility to perform actions such as instance import or query translation. All these measures have been implemented in the OntoSim library, that has been used in experiments which showed that entity preserving measures are comparable to the best ontology space measures. Moreover, they showed a robust behaviour with respect to the alteration of the alignment space

Effective Retrieval Model for Entity with Multi Value Attributes

Submitted at: 18th International Conference on Knowledge Engineering and Knowledge Management EKAW 2012

On the Modeling of Entities for Ad-Hoc Entity Search in the Web of Data

Abstract. The Web of Data describes objects, entities, or “things " in terms of their attributes and their relationships, using RDF statements. There is a need to make this wealth of knowledge easily accessible by means of keyword search. Despite recent research efforts in this direction, there is a lack of understanding of how structured semantic data is best represented for text-based entity retrieval. The task we are addressing in this paper is ad-hoc entity search: the retrieval of RDF resources that represent an entity described in the keyword query. We build upon and formalise existing entity modeling approaches within a generative language modeling framework, and compare them experimentally using a standard test collection, provided by the Semantic Search Challenge evaluation series. We show that these models outperform the current state-of-the-art in terms of retrieval effectiveness, however, this is done at the cost of abandoning a large part of the semantics behind the data. We propose a novel entity model capable of preserving the semantics associated with entities, without sacrificing retrieval effectiveness.