Program

This workshop will take place on Monday, 8 October 2018, from 14:00 to 18:00. Preliminary versions of the papers and the presentaitons of the workshops are for now linked below until the official proceedings will come out.

14:00 - 14:10	Opening and Welcome [slides]
14:10 - 15:00	Invited Talk by Marco Rospocher
	Learning Expressive Ontological Concept Descriptions via Neural Networks [slides] [abstract]
15:00 - 15:30	Muhammad Rahman and Tim Finin
	Understanding and Representing the Semantics of Large Structured Documents [slides] [paper]
15:30 - 16:00	Coffee break
16:00 - 16:30	Gengchen Mai, Krzysztof Janowicz and Bo Yan
	Combining Text Embedding and Knowledge Graph Embedding Techniques for Academic Search Engines [slides] [paper]
16:30 - 17:00	Asan Agibetov and Matthias Samwald
	Global and Local Evaluation of Link Prediction Tasks with Neural Embeddings [slides] [paper]
17:00 - 17:30	Szymon Wieczorek, Dominik Filipiak and Agata Filipowska
	Semantic Image-Based Profiling of Users' Interests with Neural Networks [slides] [paper]
	This paper has won the best paper award of SemDeep-4!
17:30 - 17:50	Michael Cochez, Martina Garofalo, Jérôme Lenßen and Maria Angela Pellegrino
	A First Experiment on Including Text Literals in KGloVe [slides] [paper]

SemDeep-4 Invited Speaker

Marco Rospocher

Fondazione Bruno Kessler (FBK), ICT Center, DKM Unit
Website: Marco Rospocher

Dr. Marco Rospocher is a tenured research scientist at Fondazione Bruno Kessler (FBK), within the Data and Knowledge Management (DKM) research unit. He received his PhD in Information and Communication Technologies from the University of Trento in 2006. His current research interests are in the area of Artificial Intelligence, focusing in particular on ontologies, formalisms for Knowledge Representation and Reasoning, and methodologies and tools for Knowledge Acquisition and Information Extraction. He has been involved in a number of international research projects, including the EU-funded projects APOSDLE, PESCaDO, and NewsReader. He co-authored more than 80 scientific publications in international journals, conferences and workshops. He co-chaired the ACM SAC Cognitive Computing track (2017 and 2018) and Knowledge and Language track (forthcoming in 2019), the ISWC 2014 Posters and Demos track, and the AI*IA 2018 Doctoral Consortium. He regularly serves in the programme committees of relevant international conferences and workshops, and reviews submissions for top-ranked international journals.

Invited talk: "Learning Expressive Ontological Concept Descriptions via Neural Networks"

Abstract. In this talk I will overview some recent work we have done on applying Neural Networks to learn expressive ontological concept descriptions from natural language text. The intuition behind the work is that the problem of encoding natural language definitions into Description Logics axioms can be framed as a syntactic transformation of the input sentence into a formula. We proposed two different approaches to implement this transformation. The first (presented in [1]) employs a process with two parallel phases (transduction & tagging). The second (submitted work, under review), tackles the problem as a neural machine translation task. Since no pre-existing dataset was available to adequately train Neural Networks for this task, we designed a data generation pipeline to produce datasets to train and evaluate the proposed architectures.

[1] Giulio Petrucci, Chiara Ghidini, and Marco Rospocher. 2016. Ontology Learning in the Deep. In 20th International Conference on Knowledge Engineering and Knowledge Management - Volume 10024 (EKAW 2016), Eva Blomqvist, Paolo Ciancarini, Francesco Poggi, and Fabio Vitali (Eds.), Vol. 10024. Springer-Verlag New York, Inc., New York, NY, USA, 480-495. DOI: https://doi.org/10.1007/978-3-319-49004-5_31