An Infrastructure for acquiring high quality semantic metadata

Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a erification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation omparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata

Comparing international coverage of 9/11 : towards an interdisciplinary explanation of the construction of news

This article presents an interdisciplinary model attempting to explain how news is constructed by relying on the contributions of different fields of study: News Sociology, Political Communications, International Communications, International Relations. It is a first step towards developing a holistic theoretical approach to what shapes the news, which bridges current micro to macro approaches. More precisely the model explains news variation across different media organization and countries by focusing on the different way the sense of newsworthiness of journalists is affected by three main variables: national interest, national journalistic culture, and editorial policy of each media organization. The model is developed on the basis of an investigation into what shaped the media coverage of 9/11 in eight elite newspapers across the US, France, Italy and Pakistan

Using background knowledge for ontology evolution

One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery. We perform a relation discovery experiment focusing on the use of WordNet and Semantic Web ontologies as sources of background knowledge. We back our experiment with a thorough analysis that highlights various issues on how to improve and validate relation discovery in the future, which will directly improve the task of automatically performing ontology changes during evolution

Seeing is Believing : The Capacity of the Manipulated Photograph to Represent Scenes of Mythology and the Supernatural

This illustrated paper explores the capacity of the manipulated photograph to represent scenes of mythology and the supernatural. Can a photograph, which is said to be an index of the real, render a mythical realm into a believable scene? Practices such a double exposures and combination printing have historically been used to create famous faked images of the supernatural, such as the Cottingley Fairy images and Spurgen’s photograph of the Loch Ness monster. The photograph has a causal link with reality and as such a carefully manipulated image has the power to deceive or persuade the viewer. In her photography project ‘Realm’ Carolyn Lefley explores this apparent truth-telling phenomenon by constructing double exposure photographs that create a layering of realities. A familiar domestic interior and a potentially mythological landscape combine to create scenes of make-believe, which reference texts such as Alice in Wonderland and The Lion, The Witch and the Wardrobe. Down the rabbit hole, through the looking glass and into the wardrobe, all of these paths lead from the realm of the real, into the realm of myth. The kingdom of Narnia is entered through an ordinary wardrobe. The photograph of a homely interior becomes a portal into a mythical realm. The idea of creating fictional realms and in essence writing new mythology is a practice known as mythopoeia, which fascinated authors such as JRR Tolkien, CS Lewis and George MacDonald. The photographs in ‘Realm’ depict new image-worlds of myth and wonder. Post-production techniques have been utilised to achieve these images. The paper will conclude with a consideration of the next era in photography, that of computer simulated reality. Sarah Kember notes in her book Virtual Anxiety that the veracity of the photograph is not threatened by this paradigm shift, suggesting that any representation only constructs an ‘image-idea’ of reality.Non peer reviewedFinal Accepted Versio

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

Ontology-driven document enrichment: principles, tools and applications

In this paper, we present an approach to document enrichment, which consists of developing and integrating formal knowledge models with archives of documents, to provide intelligent knowledge retrieval and (possibly) additional knowledge-intensive services, beyond what is currently available using “standard” information retrieval and search facilities. Our approach is ontology-driven, in the sense that the construction of the knowledge model is carried out in a top-down fashion, by populating a given ontology, rather than in a bottom-up fashion, by annotating a particular document. In this paper, we give an overview of the approach and we examine the various types of issues (e.g. modelling, organizational and user interface issues) which need to be tackled to effectively deploy our approach in the workplace. In addition, we also discuss a number of technologies we have developed to support ontology-driven document enrichment and we illustrate our ideas in the domains of electronic news publishing, scholarly discourse and medical guidelines