Felix Sasaki

Towards a Platform for Curation Technologies: Enriching Text Collections with a Semantic-Web Layer

In an attempt to put a Semantic Web-layer that provides linguistic analysis and discourse information on top of digital content, we develop a platform for digital curation technologies. The platform offers language-, knowledge- and data-aware services as a flexible set of workflows and pipelines for the efficient processing of various types of digital content. The platform is intended to enable human experts (knowledge workers) to get a grasp and understand the contents of large document collections in an efficient way so that they can curate, process and further analyse the collection according to their sector-specific needs.

Chainable and Extendable Knowledge Integration Web Services

This paper introduces the current state of the FREME framework. The paper puts FREME into the context of linguistic linked data and related approaches of multilingual and semantic processing. In addition, we focus on two specific aspects of FREME: the FREME NER e-Service, and chaining of FREME e-Services. We believe that the flexible and distributed combination of e-Services bears a potential for their mutual improvement. The FREME framework is an open source software available for free download (https://github.com/freme-project/).

Semantische Technologien und Standards für das mehrsprachige Europa

Der Beitrag beleuchtet im Kontext mehrsprachiger semantischer Anwendungen die Rolle ausgewahlter Technologien und Standards. Standardisierte semantische Ressourcen und standardisierte Verfahren fur ihre Nutzung in sprachtechnologischen Anwendungen und Workflows besitzen das Potential, die Qualitat der Anwendungen entscheidend zu verbessern und den Prozess der Anwendungsentwicklung erheblich zu vereinfachen. Im Zentrum steht zum einen die Infrastruktur META-SHARE. Diese wurde im Rahmen der Initiative META-NET entwickelt und umfasst ein XML-Metadatenschema fur die Katalogisierung von Sprachressourcen. Zum anderen behandelt der Beitrag die Nutzung von Linked Data zur Reprasentation von Metadaten und Sprachressourcen. Relevant hierfur sind Standards wie DCAT, NIF und ITS. Nach diesen praxisorientierten Betrachtungen schliest der Beitrag mit der Einbettung in einen groseren Kontext: die mehrsprachige, europaische Informationsgesellschaft.

Improving Machine Translation through Linked Data

Abstract With the ever increasing availability of linked multilingual lexical resources, there is a renewed interest in extending Natural Language Processing (NLP) applications so that they can make use of the vast set of lexical knowledge bases available in the Semantic Web. In the case of Machine Translation, MT systems can potentially benefit from such a resource. Unknown words and ambiguous translations are among the most common sources of error. In this paper, we attempt to minimise these types of errors by interfacing Statistical Machine Translation (SMT) models with Linked Open Data (LOD) resources such as DBpedia and BabelNet. We perform several experiments based on the SMT system Moses and evaluate multiple strategies for exploiting knowledge from multilingual linked data in automatically translating named entities. We conclude with an analysis of best practices for multilingual linked data sets in order to optimise their benefit to multilingual and cross-lingual applications.