Refine
Year of publication
- 2016 (2) (remove)
Document Type
- Preprint (2) (remove)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- Copenhagen School (1)
- Entity Disambiguation (1)
- Linked Data (1)
- Neural Networks (1)
- Psychology (1)
- Securitization (1)
- Security Studies (1)
- Semantic Web (1)
Institute
Securitization Theory has been applied and advanced continuously since the publication of the seminal work “Security – A New Framework for Analysis” by Buzan et al. in 1998. Various extensions, clarifications and definitions have been added over the years. Ontological and epistemological debates as well as debates about the normativity of the concept have taken place, furthering the approach incrementally and adapting it to new empirical cases. This paper aims at contributing to the improvement of the still useful framework in a more general way by amending it with well-established findings from another discipline: Psychology. The exploratory article will point out what elements of Securitization Theory might benefit most from incorporating insights from Psychology and in which ways they might change our understanding of the phenomenon. Some well-studied phenomena in the field of (Social) Psychology, it is argued here, play an important role for the construction and perception of security threats and the acceptance of the audience to grant the executive branch extraordinary measures to counter these threats: availability heuristic, loss-aversion and social identity theory are central psychological concepts that can help us to better understand how securitization works, and in which situations securitizing moves have great or little chances to reverberate. The empirical cases of the 9/11 and Paris terror attacks will serve to illustrate the potential of this approach, allowing for variances in key factors, among them: (point in) time, system of government and ideological orientation. As a hypotheses-generating pilot study, the paper will conclude by discussing further research possibilities in the field of Securitization.
DoSeR - A Knowledge-Base-Agnostic Framework for Entity Disambiguation Using Semantic Embeddings
(2016)
Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. In this work, we propose DoSeR (Disambiguation of Semantic Resources), a (named) entity disambiguation framework that is knowledge-base-agnostic in terms of RDF (e.g. DBpedia) and entity-annotated document knowledge bases (e.g. Wikipedia). Initially, our framework automatically generates semantic entity embeddings given one or multiple knowledge bases. In the following, DoSeR accepts documents with a given set of surface forms as input and collectively links them to an entity in a knowledge base with a graph-based approach. We evaluate DoSeR on seven different data sets against publicly available, state-of-the-art (named) entity disambiguation frameworks. Our approach outperforms the state-of-the-art approaches that make use of RDF knowledge bases and/or entity-annotated document knowledge bases by up to 10% F1 measure.