@conference {cFernandeze, title = {VLX-Stories: a Semantically Linked Event platform for media publishers}, booktitle = {Proceedings of the ISWC 2019 Satellite Tracks (Posters \& Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019)}, year = {2019}, month = {10/2019}, publisher = {CEUR Workshop Proceeedings}, organization = {CEUR Workshop Proceeedings}, address = {Auckland, New Zealand}, abstract = {

In this article we present a web platform used by media producers to monitor word events, detected by VLX-Stories.\ The event detector system retrieves multi-regional articles from news sites, aggregates them by topic, and summarizes them by extracting and structuring its most relevant entities in order to answer the journalism W{\textquoteright}s: who, what, when and where.\ The dashboard displays online detected events in a semantically linked space which allows navigation among trending news stories on distinct countries, categories and time.\ Moreover, detected events are linked to costumer contents, helping editorial process by providing real time access to breaking news related to their contents.\ (Demo paper)

}, url = {http://ceur-ws.org/Vol-2456/paper61.pdf}, author = {Fern{\`a}ndez, D{\`e}lia and Bou, Elisenda and Xavier Gir{\'o}-i-Nieto} } @conference {cFernandezd, title = {VLX-Stories: building an online Event Knowledge Base with Emerging Entity detection}, booktitle = {The Semantic Web {\textendash} ISWC 2019}, year = {2019}, month = {10/2019}, pages = {382-399}, publisher = {Springer, Cham}, organization = {Springer, Cham}, chapter = {24}, address = {Auckland, New Zealand}, abstract = {

We present an online multilingual system for event detection and comprehension from media feeds. The system retrieves information from news sites and social networks, aggregates them into events (event detection), and summarizes them by extracting semantic labels of its most relevant entities (event representation) in order to answer the journalism W{\textquoteright}s: who, what, when and where. The generated events populate VLX-Stories -an event Knowledge Base (KB)- transforming unstructured text data to a structured knowledge base representation.\ Our system exploits an external entity Knowledge Base (VLX-KG) to help populate VLX-Stories. At the same time, this external knowledge base can also be extended with a Dynamic Entity Linking (DEL) module, which detects Emerging Entities (EE) on unstructured data and adds them to VLX-KG.\ The system is currently used in production, detecting over 6000 monthly events from over 3500 news feeds from seven different countries and in three different languages.

}, keywords = {emerging entities, Entity Linking, event encoding, knowledge base population. knowledge graph, topic detection}, issn = {978-3-030-30796-7}, doi = {10.1007/978-3-030-30796-7_24}, url = {https://link.springer.com/chapter/10.1007/978-3-030-30796-7_24}, author = {Fern{\`a}ndez, D{\`e}lia and Bou, Elisenda and Xavier Gir{\'o}-i-Nieto} } @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} }