A Framework for the Needs of Different Types of Users in Multilingual Semantic Enrichment

A Framework for the Needs of Different Types of Users in Multilingual Semantic Enrichment Jan Nehring+, Felix Sasaki++ +German Research Centre for Artifical Intelligence Berlin, Germany ++Cornelsen, Berlin, Germany jan.nehring@dfki.de, felix@sasakiatcf.com Abstract The FREME framework bridges Language Technologies (LT) and Linked Data (LD). It establishes workflows between LT and LD in a well defined, coherent way. FREME addresses common challenges that both researchers and industry face when integrating LT and LD: interoperability, ”silo” solutions and the lack of adequate tooling. Usability, reusability and interoperability are often attributes of frameworks and toolkits for LT and LD. In this paper, we take a novel approach: We define user types and user levels and describe how they influence design decisions in a LT and LD processing framework. In this way, we combine research outcomes from various communities: language technology, linked data and software interface engineering. This paper explains the different user types and how FREME addresses the specific needs of each user type. Core attributes of FREME are usability, reusability and interoperability. Keywords: language resource, language technology, usability, user types, linked data, software interface engineering 1. Introduction Language Technology (LT) profits from the current rise of Artificial Intelligence and is being more and more inte- grated in commercial applications. The growing amount of digital content calls for technologies to process and en- rich digital content in an automated manner. Both industry and academia need adequate tooling to process digital con- tent. The amount of available tools is growing, but each tool uses its own data format. Merging annotations of digi- tal content lacks a unified approach (Hellmann et al., 2013; Sanderson et al., 2013). Knowledge resources have be- come a key component of current systems in Artifical Intel- ligence (Flati et al., 2014). Construction and exploitation of such knowledge sources has gained attraction from both re- searchers (Mitchell, 2005; Navigli and Ponzetto, 2012) and big industry players like Google (Singhal, 2012) or IBM (Ferrucci, 2012). A popular approach to store and connect knowledge sources is Linked Data (LD). The FREME framework bridges Language Technologies and Linked Data (Dojchinovski et al., 2016). It establishes workflows between LT and LD in a well defined, coherent way. FREME addresses common challenges that both re- searchers and industry face when integrating LT and LD: interoperability, ”silo” solutions and the lack of adequate tooling (Sasaki et al., 2015a). Usability, reusability and interoperability are often at- tributes of frameworks and toolkits for Language Technolo- gies (LT) and Linked Data (LD). Examples are (Bachmann- Gmur, 2013; Hinrichs et al., 2010; Klein et al., 2017; Noji and Miyao, 2016). In this paper, we take a novel approach: We define user types and user levels and de- scribe how they influence design decisions in a LT and LD processing framework. In this way, we combine research outcomes from various communities: language technology, linked data and software interface engineering. This pa- per explains the different user types and how FREME ad- dresses the specific needs of each user type. Core attributes of FREME …

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