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Erol S.,Vienna University of Economics and Business | Granitzer M.,Know Center Graz | Happ S.,A-T Solutions | Jantunen S.,Lappeenranta University of Technology | And 7 more authors.
Journal of Software Maintenance and Evolution | Year: 2010

Social software has received much attention in the academia and industry due to many success stories. However, although social software is used widely for business support, its relationship with Business Process Management has not been analysed. The results of the workshop on Business Process Management and Social Software (BPMS2'08), as part of the International Conference on Business Process Management in Milano, show the manifold possibilities of combining concepts from Business Process Management and social software. Social software provides a better integration of all stakeholders into the business process life cycle and offers new possibilities for a more effective and flexible design of business processes. The modelling of business processes may profit particularly from using social software techniques by alleviating the integration of process knowledge from all stakeholders. In addition, the implementation and deployment phase of the business process life cycle may profit from social software by collecting valuable information for continuous process improvement from a larger set of sources than before. Furthermore, social software environments may be used to provide workflow support. Moreover, the use of social software also requires new considerations about the digital identity and reputation in business processes. Copyright © 2010 John Wiley & Sons, Ltd. Source

Dragoni M.,FBK irst | Rexha A.,Know Center Graz | Casu M.,CELI S.r.l. | Bosca A.,CELI S.r.l.
CEUR Workshop Proceedings | Year: 2014

In this paper, we present a multilingual matching approach aiming at building matches between terms belonging to multilingual thesauri. The approach is presented as a variant of the schema matching problem and present its evaluation on domain-specific use cases by demonstrating the viability of the proposed technique for facing the multilingual thesaurus matching approach. Source

Granitzer M.,University of Graz | Granitzer M.,Know Center Graz
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Term weighting strongly influences the performance of text mining and information retrieval approaches. Usually term weights are determined through statistical estimates based on static weighting schemes. Such static approaches lack the capability to generalize to different domains and different data sets. In this paper, we introduce an on-line learning method for adapting term weights in a supervised manner.Via stochastic optimizationwe determine a linear transformation of the termspace to approximate expected similarity values among documents.We evaluate our approach on 18 standard text data sets and show that the performance improvement of a k-NN classifier ranges between 1% and 12% by using adaptive term weighting as preprocessing step. Further, we provide empirical evidence that our approach is efficient to cope with larger problems. © Springer-Verlag 2010. Source

Strohmaier M.,University of Graz | Helic D.,University of Graz | Benz D.,University of Kassel | Korner C.,University of Graz | KlERN R.,Know Center Graz
ACM Transactions on Intelligent Systems and Technology | Year: 2012

Algorithms for constructing hierarchical structures from user-generated metadata have caught the interest of the academic community in recent years. In social tagging systems, the output of these algorithms is usually referred to as folksonomies (from folk-generated taxonomies). Evaluation of folksonomies and folksonomy induction algorithms is a challenging issue complicated by the lack of golden standards, lack of comprehensive methods and tools as well as a lack of research and empirical/simulation studies applying these methods. In this article, we report results from a broad comparative study of state-of-the-art folksonomy induction algorithms that we have applied and evaluated in the context of five social tagging systems. In addition to adopting semantic evaluation techniques, we present and adopt a new technique that can be used to evaluate the usefulness of folksonomies for navigation. Our work sheds new light on the properties and characteristics of state-of-the-art folksonomy induction algorithms and introduces a new pragmatic approach to folksonomy evaluation, while at the same time identifying some important limitations and challenges of folksonomy evaluation. Our results show that folksonomy induction algorithms specifically developed to capture intuitions of social tagging systems outperform traditional hierarchical clustering techniques. To the best of our knowledge, this work represents the largest and most comprehensive evaluation study of state-of-the-art folksonomy induction algorithms to date. © 2012 ACM. Source

Latif A.,German National Library of Economics Leibniz Information Center for Economics | Hoefler P.,Know Center Graz | Tochtermann K.,German National Library of Economics Leibniz Information Center for Economics
Communications in Computer and Information Science | Year: 2012

Linked Data has played a vital role in the realization of the Semantic Web on a global level. It motivates people to publish datasets which can be important for information linking and discovery and can further make contributions in streamlining the Web as a single connected data space. This effort has successfully amassed a variety of Linked Data and has introduced many novel ways for the publishing of data. As a result, putting Linked Data online has become rather easy, but actually linking the data with already existing data in the cloud is still a challenge. The search and identification of relevant datasets as well as devising a strategy for linking to these datasets is still a difficult task. In this paper, a novel approach is presented which highlights and implements the steps involved in the interlinking process. This approach is further applied and presented as a case study focusing on interlinking scholarly communication datasets and highlighting the potential benefits. © Springer-Verlag Berlin Heidelberg 2012. Source

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