Evaluation of semantic web ontologies for modelling art collections

© 2017, Springer International Publishing AG. The need for organising, sharing and digitally processing Cultural Heritage (CH) information has led to the development of formal knowledge representation models (ontologies) for the CH domain. Based on RDF and OWL, the standard data model and ontology language of the Semantic Web, ontologies such as CIDOC-CRM, the Europeana Data Model and VRA, offer enhanced representation capabilities, but also support for inference, querying and interlinking through the Web. This paper presents the results of a small-scale evaluation of the three most commonly used CH ontologies, with respect to their capacity to fulfil the data modelling requirements of art collections

Distributed Web-Scale Infrastructure For Crawling, Indexing And Search With Semantic Support

In this paper, we describe our work in progress in the scope of web-scale informationextraction and information retrieval utilizing distributed computing. Wepresent a distributed architecture built on top of the MapReduce paradigm forinformation retrieval, information processing and intelligent search supportedby spatial capabilities. Proposed architecture is focused on crawling documentsin several different formats, information extraction, lightweight semantic annotationof the extracted information, indexing of extracted information andfinally on indexing of documents based on the geo-spatial information foundin a document. We demonstrate the architecture on two use cases, where thefirst is search in job offers retrieved from the LinkedIn portal and the second issearch in BBC news feeds and discuss several problems we had to face duringthe implementation. We also discuss spatial search applications for both casesbecause both LinkedIn job offer pages and BBC news feeds contain a lot of spatialinformation to extract and process