Take it personally - A Python library for data enrichment for infometrical applications

Like every other social sphere, science is influenced by individual characteristics of researchers. However, for investigations on scientific networks, only little data about the social background of researchers, e.g. social origin, gender, affiliation etc., is available. This paper introduces ”Take it personally - TIP”, a conceptual model and library currently under development, which aims to support the semantic enrichment of publication databases with semantically related background information which resides elsewhere in the (semantic) web, such as Wikidata. The supplementary information enriches the original information in the publication databases and thus facilitates the creation of complex scientific knowledge graphs. Such enrichment helps to improve the scientometric analysis of scientific publications as they can also take social backgrounds of researchers into account and to understand social structure in research communities

ORCID for Wikidata. Data enrichment for scientometric applications

Due to its numerous bibliometric entries of scholarly articles and connected information Wikidata can serve as an open and rich source for deep scientometrical analyses. However, there are currently certain limitations: While 31.5% of all Wikidata entries represent scientific articles, only 8.9% are entries describing a person and the number of entries researcher is accordingly even lower. Another issue is the frequent absence of established relations between the scholarly article item and the author item although the author is already listed in Wikidata. To fill this gap and to improve the content of Wikidata in general, we established a workflow for matching authors and scholarly publications by integrating data from the ORCID (Open Researcher and Contributor ID) database. By this approach we were able to extend Wikidata by more than 12k author-publication relations and the method can be transferred to other enrichments based on ORCID data. This is extension is beneficial for Wikidata users performing bibliometrical analyses or using such metadata for other purposes

Früherkennung wissenschaftlicher Konvergenz im Hochschulmanagement

It is crucial for universities to recognize early signals of scientific convergence. Scientific convergence describes a dynamic pattern where the distance between different fields of knowledge shrinks over time. This knowledge space is beneficial to radical innovations and new promising research topics. Research in converging areas of knowledge can therefore allow universities to establish a leading position in the science community. The Q-AKTIV project develops a new approach on the basis of machine learning to identify scientific convergence at an early stage. In this work, we briefly present this approach and the first results of empirical validation. We discuss the benefits of an instrument building on our approach for the strategic management of universities and other research institutes

Acquisition and Relevance of Geotagged Information in Tourism

In the case of tourism applications, it is particularly evident that geography is emerging as a fundamental principle for structuring Web resources. Recent improvements in semantic and geographic Web technology, often referred to as the Geospatial Web, acknowledge the relevance of adding location metadata to existing databases and accessing the vast amounts of information stored in these databases via geospatial services. This paper outlines the acquisition of geospatial context information, describes usage scenarios and real-world applications in the tourism industry, and presents an automated software tool for annotating large collections of Web documents automatically. The quality of this tool is tested based upon Web pages from the Austrian National Tourism Organization. Initial results are encouraging and help define a roadmap for further improving the automated tagging of tourism resources

212 ACM ECHT CONFERENCE Abstract Fishing for Clarity in Hyperdocuments with Enhanced Fisheye-Views

It is known that fisheye-views prove beneficial to users who get lost in hyperspace. Until now, the fisheye-view strategy, introduced by Fumas, is only applicable in structures where the necessary components of the fisheye-view function can be defined. Unfortunately, directed graphs are structures where the fisheye-view function of Furnas cannot be applied. Therefore the fisheye-view concept was of limited value in hyperdocuments represented by such graphs. To overcome this lack, this paper proposes an enhancement of Furnas ’ function to allow its application in hyperdoeuments of that kind. We will begin with a short review of Furnas’ well-known fisheye-view concept. Thereafter, we wilt discuss the problems that arise when one attempts to apply the concept in so-called,,unstructured” hyperdocuments. The results of this discussion lead to the development of a function which satisfies the requirements of the concept, and allows its use in hyperdocuments of almost any structure. To show that the fisheye-view concept of Fumas remains fulfilled, an appropriate theorem is formulated. The result is that the benefits of the fisheye-view concept can now be appreciated in,,unstructured” hyperdocuments. In closing, we offer a detailed example, which illustrates the behavior of the enhanced fisheye-view function. Permission to copy without fee all or part of this material is granted provided that copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission