Kern Roman, Falk Stefan, Rexha Andi
2017
This paper describes our participation inSemEval-2017 Task 10, named ScienceIE(Machine Reading for Scientist). We competedin Subtask 1 and 2 which consist respectivelyin identifying all the key phrasesin scientific publications and label them withone of the three categories: Task, Process,and Material. These scientific publicationsare selected from Computer Science, MaterialSciences, and Physics domains. We followeda supervised approach for both subtasksby using a sequential classifier (CRF - ConditionalRandom Fields). For generating oursolution we used a web-based application implementedin the EU-funded research project,named CODE. Our system achieved an F1score of 0.39 for the Subtask 1 and 0.28 forthe Subtask 2.
Rexha Andi, Kern Roman, Ziak Hermann, Dragoni Mauro
2017
Retrieval of domain-specific documents became attractive for theSemantic Web community due to the possibility of integrating classicInformation Retrieval (IR) techniques with semantic knowledge.Unfortunately, the gap between the construction of a full semanticsearch engine and the possibility of exploiting a repository ofontologies covering all possible domains is far from being filled.Recent solutions focused on the aggregation of different domain-specificrepositories managed by third-parties. In this paper, wepresent a semantic federated search engine developed in the contextof the EEXCESS EU project. Through the developed platform,users are able to perform federated queries over repositories in atransparent way, i.e. without knowing how their original queries aretransformed before being actually submitted. The platform implementsa facility for plugging new repositories and for creating, withthe support of general purpose knowledge bases, knowledge graphsdescribing the content of each connected repository. Such knowledgegraphs are then exploited for enriching queries performed byusers.
Rexha Andi, Kröll Mark, Ziak Hermann, Kern Roman
2017
Our work is motivated by the idea to extend the retrieval of related scientific literature to cases, where the relatedness also incorporates the writing style of individual scientific authors. Therefore we conducted a pilot study to answer the question whether humans can identity authorship once the topological clues have been removed. As first result, we found out that this task is challenging, even for humans. We also found some agreement between the annotators. To gain a better understanding how humans tackle such a problem, we conducted an exploratory data analysis. Here, we compared the decisions against a number of topological and stylometric features. The outcome of our work should help to improve automatic authorship identificationalgorithms and to shape potential follow-up studies.