@conference {cFernandezf, title = {Enhancing Online Knowledge Graph Population with Semantic Knowledge}, booktitle = {19th International Semantic Web Conference (ISWC)}, year = {2020}, month = {11/2020}, address = {Virtual}, abstract = {

Knowledge Graphs (KG) are becoming essential to organize, represent and store the world{\textquoteright}s knowledge, but they still rely heavily on humanly-curated structured data. Information Extraction (IE) tasks, like disambiguating entities and relations from unstructured text, are key to automate KG population. However, Natural Language Processing (NLP) methods alone can not guarantee the validity of the facts extracted and may introduce erroneous information into the KG.\ This work presents an end-to-end system that combines Semantic Knowledge and Validation techniques with NLP methods, to provide KG population of novel facts from clustered news events.\ The contributions of this paper are two-fold: First, we present a novel method for including entity-type knowledge into a Relation Extraction model, improving F1-Score over the baseline with TACRED and TypeRE datasets. Second, we increase the precision by adding data validation on top of the Relation Extraction method. These two contributions are combined in an industrial pipeline for automatic KG population over aggregated news, demonstrating increased data validity when performing online learning from unstructured web data. Finally, the TypeRE and AggregatedNewsRE datasets build to benchmark these results are also published to foster future research in this field.

}, keywords = {Data Validation, Knowledge Graph, Relation Extraction}, author = {Fern{\`a}ndez, D{\`e}lia and Rimmek, Joan Marco and Espadaler, Joan and Garolera, Blai and Barja, Adri{\`a} and Codina, Marc and Sastre, Marc and Xavier Gir{\'o}-i-Nieto and Riveiro, Juan Carlos and Bou-Balust, Elisenda} } @conference {cFernandeza, title = {ViTS: Video Tagging System from Massive Web Multimedia Collections}, booktitle = {ICCV 2017 Workshop on Web-scale Vision and Social Media }, year = {2017}, month = {10/2017}, address = {Venice, Italy}, abstract = {

The popularization of multimedia content on the Web has arised the need to automatically understand, index and retrieve it. In this paper we present ViTS, an automatic Video Tagging System which learns from videos, their web context and comments shared on social networks. ViTS analyses massive multimedia collections by Internet crawling, and maintains a knowledge base that updates in real time with no need of human supervision. As a result, each video is indexed with a rich set of labels and linked with other related contents. ViTS is an industrial product under exploitation with a vocabulary of over 2.5M concepts, capable of indexing more than 150k videos per month. We compare the quality and completeness of our tags with respect to the ones in the YouTube-8M dataset, and we show how ViTS enhances the semantic annotation of the videos with a larger number of labels (10.04 tags/video), with an accuracy of 80,87\%.

}, author = {Fern{\`a}ndez, D{\`e}lia and David Varas and Espadaler, Joan and Ferreira, Jordi and Woodward, Alejandro and Rodr{\'\i}guez, David and Xavier Gir{\'o}-i-Nieto and Riveiro, Juan Carlos and Bou, Elisenda} }