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Conventional terminology resources reach their limits when it comes to automatic content classification of texts in the domain of expertlayperson communication. This can be attributed to the fact that (non-normalized) language usage does not necessarily reflect the terminological elements stored in such resources. We present several strategies to extend a terminological resource with term-related elements in order to optimize automatic content classification of expert-layperson texts.
In our paper, we present a case study on the quality of concept relations in the manually developed terminological resource of grammis, an information system on German grammar. We assess a SKOS representation of the resource using the tool qSKOS, create a typology of the issues identified by the tool, and conduct a qualitative analysis of selected cases. We identify and discuss aspects that can motivate quality issues and uncover that ill-formed relations are frequently indicative of deeper issues in the data model. Finally, we outline how these findings can inform improvements in our resource’s data model, discussing implications for the machine readability of terminological data.
Terminological resources play a central role in the organization and retrieval of scientific texts. Both simple keyword lists and advanced modelings of relationships between terminological concepts can make a most valuable contribution to the analysis, classification, and finding of appropriate digital documents, either on the web or within local repositories. This seems especially true for long-established scientific fields with elusive theoretical and historical branches, where the use of terminology within documents from different origins is often far from being consistent. In this paper, we report on the progress of a linguistically motivated project on the onomasiological re-modeling of the terminological resources for the grammatical information system grammis. We present the design principles and the results of their application. In particular, we focus on new features for the authoring backend and discuss how these innovations help to evaluate existing, loosely structured terminological content, as well as to efficiently deal with automatic term extraction. Furthermore, we introduce a transformation to a future SKOS representation. We conclude with a positioning of our resources with regard to the Knowledge Organization discourse and discuss how a highly complex information environment like grammis benefits from the re-designed terminological KOS.
In this paper, we present our approach to automatically extracting German terminology in the domain of grammar using texts from the online information system grammis as our corpus. We analyze existing repositories of German grammatical terminology and develop Part-of-speech patterns for our extraction thereby showing the importance of unigrams in this domain. We contrast the results of the automatic extraction with a manually extracted standard. By comparing the performance of well-known statistical measures, we show how measures based on corpus comparison outperform alternative methods.
Grammis is a web-based information system on German grammar, hosted by the Institute for the German Language (IDS). It is human-oriented and features different theoretical perspectives on grammar. Currently, the terminology component of grammis is being redesigned for this theoretical diversity to play a more prominent role in the data model. This also opens opportunities for implementing some machine-oriented features. In this paper, we present the re-design of both data model and knowledge base. We explore how the addition of machine-oriented features to the data model impacts the knowledge base; in particular, how this addition shifts some of the textual complexity into the data model. We show that our resource can easily be ported to a SKOS-XL representation, which makes it available for data science, knowledge-based NLP applications, and LOD in the context of digital humanities.
The compilation of terminological vocabularies plays a central role in the organization and retrieval of scientific texts. Both simple keyword lists as well as sophisticated modellings of relationships between terminological concepts can make a most valuable contribution to the analysis, classification, and finding of appropriate digital documents, either on the Web or within local repositories. This seems especially true for long-established scientific fields with various theoretical and historical branches, such as linguistics, where the use of terminology within documents from different origins is sometimes far from being consistent. In this short paper, we report on the early stages of a project that aims at the re-design of an existing domain-specific KOS for grammatical content grammis. In particular, we deal with the terminological part of grammis and present the state-of-the-art of this online resource as well as the key re-design principles. Further, we propose questions regarding ramifications of the Linked Open Data and Semantic Web approaches for our re-design decisions.