Vocabularies & Ontologies

Session 4.3

Thursday, September 17, 2015 - 10:30 to 12:00
LC Club
Marta Sabou


Owner at Linked Data Factory


The importance of SKOS in ontology development

Organisations that develop semantic models have a lot to think about. The envisioned benefits from semantic solutions have to compete with many factors of uncertainty that threaten project results. Challenges lie not only in the evident scarcity of knowledge, skills and tools but also in other factors such as business objectives and requirements. 

In this talk Lieke Verhelst shares her long experience as a semantic modeller. Side by side with subject matter experts she has constructed semantic models and infrastructures for the environment, construction and education sectors. She will point out which common pitfalls she has seen during a ontology development process. While illustrating these, she will come to answering the question why SKOS is the key to success for semantic solution projects.

Cross-Lingual Lexical Matching with Word Translation and Local Similarity Optimization

Cross-Lingual Mapping (CLM) establishes semantic relations between source and target concepts to align two resources lexicalized in different languages, e.g., ontologies, thesauri, or concept inventories, or to enrich a multilingual resource. In this paper, we focus on purely lexical matching algorithms to support CLM between lexically-rich resources, where concepts can be identified by synsets. The key idea of these algorithms is to use the results of word translations as evidence to map synsets lexicalized in different languages. We propose a new cross-lingual similarity measure inspired by a classification-based mapping semantics. Then we apply a novel local similarity optimization method to select the best matches for each source synset. To evaluate our approach we use wordnets in four different languages, which have been manually mapped to the English WordNet. Results show that despite our method uses only lexical information about the concepts, it obtains good performance and significantly outperforms several baseline methods.

Updating OWL2 Ontologies Using Pruned Rulesets

Evolution in Semantic Web content produces difference files (deltas) that track changes between ontology versions. These changes may represent ontology modifications or simply changes in application data. An ontology is typically expressed in a combination of OWL and RDF knowledge representation languages. A data repository that represents an ontology may be large and may be duplicated over the Internet, often in the form of a relational datastore. The deltas can be used to reduce the storage and bandwidth overhead involved in disseminating ontology updates. Minimising the delta size can be achieved by reasoning over the underlying knowledge base. OWL 2 is a development of the OWL 1 standard that incorporates new features to aid application construction. Among the sub languages of OWL 2, OWL 2 RL/RDF provides an enriched rule set that extends the semantic capability of the OWL environment. This additional semantic content can be exploited in change detection approaches that strive to minimise the alterations to be made when ontologies are updated. The presence of blank nodes (i.e. nodes that are neither a URI nor a literal) in RDF collections provides a further challenge to ontology change detection. This is a consequence of the practical problems they introduce when comparing data structures before and after update. In the light of OWL 2 RL/RDF, this paper examines the potential for reducing the delta size by pruning the application of unnecessary rules from the reasoning process and using an approach to delta generation that produces the smallest number of updates. It also assesses the impact of alternative approaches to handling blank nodes during the change detection process in ontology structures. The results indicate that pruning the rule set is a potentially expensive process but has the benefit of reducing the joins over relational data stores when carrying out the subsequent inferencing.