Supporting SPARQL Update Queries in RDF-XML Integration

The Web of Data encourages organizations and companies to publish their data according to the Linked Data practices and offer SPARQL endpoints. On the other hand, the dominant standard for information exchange is XML. The SPARQL2XQuery Framework focuses on the automatic translation of SPARQL queries in XQuery expressions in order to access XML data across the Web. In this paper, we outline our ongoing work on supporting update queries in the RDF-XML integration scenario.Comment: 13th International Semantic Web Conference (ISWC '14

An Ontology for Gendered Content Representation of Cultural Heritage Artefacts

The need for organising and digitally processing the vast amount of Cultural Heritage (CH) information has recently led to the development of formal knowledge representation models (ontologies) for the CH domain. Existing models, however, do not capture gender-related concepts. This article presents an effort to fill this gap by developing a new ontology for the representation of gendered concepts in CH resources.1 The new ontology, named ‘GenderedCHContents’ resulted from combined research in women’s studies, gender theory and computer science. Its primary aim is to draw attention to the presence of women within CH artefacts. The proposed ontology extends the Europeana Data Model (EDM) with twenty-two new classes, sixteen object properties and seven datatype properties. The article presents a demonstration of the ‘GenderedCHContents’ ontology’s use in five different representation tasks, which describe five resources related to Pandora’s myth. Lastly, the study stresses the benefits of reasoning support (i.e. enabling computers to infer further information from a set of asserted facts) in revealing different gender ideals and inferred relationships between metaphorical concepts, along with the benefits of the Semantic Web in making information about gendered contents more easily retrievable to the users

Towards a Semantics-Based Recommendation System for Cultural Heritage Collections

While the use of semantic technologies is now commonplace in the cultural heritage sector and several semantically annotated cultural heritage datasets are publicly available, there are few examples of cultural portals that exploit these datasets and technologies to improve the experience of visitors to their online collections. Aiming to address this gap, this paper explores methods for semantics-based recommendations aimed at visitors to cultural portals who want to explore online collections. The proposed methods exploit the rich semantic metadata in a cultural heritage dataset and the capabilities of a graph database system to improve the accuracy of searches through the collection and the quality of the recommendations provided to the user. The methods were developed and tested with the Archive of the Art Textbooks of Elementary and Public Schools in the Japanese Colonial Period. However, they can easily be adapted to any cultural heritage collection dataset modelled in RDF

Contextual and Possibilistic Reasoning for Coalition Formation

In multiagent systems, agents often have to rely on other agents to reach their goals, for example when they lack a needed resource or do not have the capability to perform a required action. Agents therefore need to cooperate. Then, some of the questions raised are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents' abilities to carry out certain tasks? In this article, we address the question of how to find and evaluate coalitions among agents in multiagent systems using MCS tools, while taking into consideration the uncertainty around the agents' actions. Our methodology is the following: We first compute the solution space for the formation of coalitions using a contextual reasoning approach. Second, we model agents as contexts in Multi-Context Systems (MCS), and dependence relations among agents seeking to achieve their goals, as bridge rules. Third, we systematically compute all potential coalitions using algorithms for MCS equilibria, and given a set of functional and non-functional requirements, we propose ways to select the best solutions. Finally, in order to handle the uncertainty in the agents' actions, we extend our approach with features of possibilistic reasoning. We illustrate our approach with an example from robotics