Hube, C., Idahl, M., Fetahu, B.(2020)“Debiasing Word Embeddings from Sentiment Associations in Names.”, in Caverlee, J., Hu, X. (B.), Lalmas, M. and Wang, W., eds., Wsdm, ACM, 259-267, available: http://dblp.uni-trier.de/db/conf/wsdm/wsdm2020.html#HubeIF20.
Reimann, L., Kniesel-Wünsche, G.(2020)“Achieving guidance in applied machine learning through software engineering techniques.”, in Aguiar, A., Chiba, S. and Boix, E.G., eds., Programming, ACM, 7-12, available: http://dblp.uni-trier.de/db/conf/programming/programming2020.html#ReimannK20.
Gottschalk, S., Demidova, E.(2020)“EventKG+BT: Generation of Interactive Biography Timelines from a Knowledge Graph.”, in Harth, A., Presutti, V., Troncy, R., Acosta, M., Polleres, A., Fernández, J.D., Parreira, J.X., Hartig, O., Hose, K. and Cochez, M., eds., Eswc (Satellite Events), Lecture Notes In Computer Science, Springer, 91-97, available: http://dblp.uni-trier.de/db/conf/esws/eswc2020s.html#GottschalkD20.
Kassawat, F., Chaudhuri, D., Lehmann, J.(2019)“Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-task Learning.”, in Hitzler, P., Fernández, M., Janowicz, K., Zaveri, A., Gray, A.J.G., López, V., Haller, A. and Hammar, K., eds., Eswc, Lecture Notes In Computer Science, Springer, 225-239, available: http://dblp.uni-trier.de/db/conf/esws/eswc2019.html#KassawatC019.
Gottschalk, S., Demidova, E.(2019)“HapPenIng: Happen, Predict, Infer --- Event Series Completion in a Knowledge Graph”, in Proc. Of The 18Th International Semantic Web Conference (Iswc 2019), Springer.
Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: 1) prediction of sub-event relations, and 2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52 percentage points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.
Gottschalk, S., Tempelmeier, N., Kniesel, G., Iosifidis, V., Fetahu, B., Demidova, E.(2019)“Simple-ML: Towards a Framework for Semantic Data Analytics Workflows”, in Proc. Of Semantics 2019.
Redi, M., Fetahu, B., Morgan, J., Taraborelli, D.(2019)“Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's Verifiability”, in In Proceedings Of The Web Conference (Www), San Francisco, Usa.
Fetahu, B., Anand, A., Koutraki, M.(2019)“TableNet: A Knowledge Graph of Interlinked Wikipedia Tables”, in In Proceedings Of The Web Conference (Www), San Francisco, Usa.
Hube, C., Fetahu, B.(2019)“Neural Based Statement Classification for Biased Language”, in 12Th Acm International Conference On Web Search And Data Mining (Wsdm).
The NLIWOD and PROFILES workshop @iswc_conf #iswc_conf is about to start in 15mins. We are looking forward to seeing two interesting keynotes and 8 paper presentations as well as all of YOU during this pandemic 🧟🧟♀️
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