Paper 1
Structure Inference for Linked Data Sources using ClusteringAuthors: Klitos Christodoulou, Norman W. Paton, and Alvaro A.A. Fernandes |
AbstractLinked Data (LD) overlays the World Wide Web of documents with a Web of Data. This is becoming signi cant as shown in the growth of LD repositories available as part of the Linked Open Data (LOD) cloud. At the instance-level, LD sources use a combination of terms from various vocabularies, expressed as RDFS/OWL, to describe data and publish it to the Web. However, LD sources do not organise data to conform to a speci c structure analogous to a relational schema; instead data can adhere to multiple vocabularies. Expressing SPARQL queries over LD sources { usually over a SPARQL endpoint that is presented to the user { requires knowledge of the predicates used so as to allow queries to express user requirements as graph patterns. Although LD provides low barriers to data publication using a single language (i.e., RDF), sources organise data with di erent structures and terminologies. This paper describes an approach to automatically derive structural summaries over instance-level data expressed as RDF triples. The technique builds on a hierarchical clustering algorithm that organises RDF instance-level data into groups that are then utilised to infer a structural summary over a LD source. The resulting structural summaries are expressed in the form of classes, properties and, relationships. Our experimental evaluation shows good results when applied to di erent types of LD sources. |