Our long paper submission
„Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models " (Anne Lauscher, Goran Glavaš, Kai Eckert, and Simone Paolo Ponzetto)
got accepted at the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), one of the top-tier conferences in natural language processing!
The Data and Web Science Group is hosting the Data Science Conference LWDA 2018 in Mannheim on August 22-24, 2018.
LWDA, which expands to „Lernen, Wissen, Daten, Analysen“ („Learning, Knowledge, Data, Analytics“), covers recent research in areas such as knowledge discovery, machine learning & data mining, knowledge management, database management & information systems, information retrieval.
The LWDA conference is organized by and brings together the various special interest groups of the Gesellschaft für Informatik (German Computer Science Society) in this area. The program comprises of joint research sessions and keynotes as well as of workshops organized by each special interest group.
Further information can be found on the conference website: https://www.uni-mannheim.de/lwda-2018/.
Download the conference poster.
The paper „Fine-grained Evaluation of Rule- and Embedding-based Systems for Knowledge Graph Completion“ by Christian Meilicke, Manuel Fink, Yanjie Wang, Daniel Ruffinelli, Rainer Gemulla, and Heiner Stuckenschmidt has been accepted at the 2018 International Semantic Web Conference (ISWC). Abstract: Over the recent years, embedding methods have attracted increasing focus as a means for knowledge graph completion. Similarly, rule-based systems have been studied for this task in the past. What is missing so far is a common evaluation that includes more than one type of method. We close this gap by comparing representatives of both types of systems in a frequently used evaluation protocol. Leveraging the explanatory qualities of rule-based systems, we present a fine-grained evaluation that gives insight into characteristics of the most popular datasets and points out the different strengths and shortcomings of the examined approaches. Our results show that models such as TransE, RESCAL or HolE have problems in solving certain types of completion tasks that can be solved by a rule-based approach with high precision. At the same time, there are other completion tasks that are difficult for rule-based systems. Motivated by these insights, we combine both families of approaches via ensemble learning. The results support our assumption that the two methods complement each other in a beneficial way.