@phdthesis {dFernandez23, title = {Knowledge graph population from news streams}, volume = {Doctorate}, year = {2023}, month = {10/2023}, school = {Universitat Polit{\`e}cnica de Catalunya}, type = {Industrial}, address = {Barcelona, Catalonia}, abstract = {

Media producers publish large amounts of multimedia content online - both text, audio, image and video.\  As the online media market grows, the management and delivery of contents becomes a challenge. Semantic and linking technologies can be used to organize and exploit these contents through the use of knowledge graphs. This industrial doctorate dissertation addresses the problem of constructing knowledge resources and integrating them into a system used by media producers to manage and explore their contents. For that purpose, knowledge graphs and their maintenance through Information Extraction (IE) from news streams is studied. This thesis presents solutions for multimedia understanding and knowledge extraction from online news, and their exploitation in real product applications, and it is structured in three parts.

The first part consists on the construction of IE tools that will be used for knowledge graph population. For that, we built an holistic Entity Linking (EL) system capable of combining multimodal data inputs to extract a set of semantic entities that describe news content.\  The EL system is followed by a Relation Extraction (RE) model that predicts relations between pairs of entities with a novel method based on entity-type knowledge. The final system is capable of extracting triples describing the contents of a news article.

The second part focuses on the automatic construction of a news event knowledge graph. We present an online multilingual system for event detection and comprehension from media feeds, called VLX-Stories. The system retrieves information from news sites, aggregates them into events (event detection), and summarizes them by extracting semantic labels of its most relevant entities (event representation) in order to answer four Ws from journalism: who, what, when and where.\  This part of the thesis deals with the problems of Topic Detection and Tracking (TDT), topic modeling and event representation.

The third part of the thesis builds on top of the models developed in the two previous parts to populate a knowledge graph from aggregated news.
The system is completed with an emerging entity detection module, which detects mentions of novel people appearing on the news and creates new knowledge graph entities from them. Finally, data validation and triple classification tools are added to increase the quality of the knowledge graph population.

This dissertation addresses many general knowledge graph and information extraction problems, like knowledge dynamicity, self-learning, and quality assessment. Moreover, as an industrial work, we provide solutions that were deployed in production and verify our methods with real customers.

}, keywords = {Entity Linking, Information Extraction, Knowledge Graph Population, Named Entity Disambiguation, Named Entity Recognition, Natural Language Processing, Relation Extraction, Topic Detection and Tracking, Topic Modeling, Triple Validation}, author = {Fern{\`a}ndez, D{\`e}lia and Marqu{\'e}s, F. and Xavier Gir{\'o}-i-Nieto and Bou-Balust, Elisenda} } @article {aGene-Molaa, title = {KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data}, journal = {Data in Brief}, year = {2019}, month = {07/2019}, abstract = {

This article contains data related to the research article entitle {\textquotedblleft}Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities{\textquotedblright} [1]. The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. RGB-D sensors have shown potential for fruit detection and localization since they provide 3D information with color data. However, the lack of substantial datasets is a barrier for exploiting the use of these sensors. This article presents the KFuji RGB-DS database which is composed by 967 multi-modal images of Fuji apples on trees captured using Microsoft Kinect v2 (Microsoft, Redmond, WA, USA). Each image contains information from 3 different modalities: color (RGB), depth (D) and range corrected IR intensity (S). Ground truth fruit locations were manually annotated, labeling a total of 12,839 apples in all the dataset. The current dataset is publicly available at http://www.grap.udl.cat/publicacions/datasets.html.

}, keywords = {Depth cameras; RGB-D, Fruit detection, Fruit reflectance, Fuji apple, Multi-modal dataset}, doi = {10.1016/j.dib.2019.104289}, author = {Gen{\'e}-Mola, Jordi and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @mastersthesis {xRamos-Caballero15, title = {Keyframe-based Video Summarization Designer}, year = {2015}, abstract = {

Advisors: Xavier Gir{\'o}-i-Nieto (UPC) and Horst Eidenberger (TU Wien)

Studies: Bachelor Degree inAudiovisual Systems Engineeringat Telecom BCN-ETSETB from the Technical University of Catalonia (UPC)

Grade: B (8.7/10)

This Final Degree Work extends two previous projects and consists in carrying out an improvement of the video keyframe extraction module from one of them called Designer Master, by integrating the algorithms that were developed in the other, Object Maps.

Firstly the proposed solution is explained, which consists in a shot detection method, where the input video is sampled uniformly and afterwards, cumulative pixel-to-pixel difference is applied and a classifier decides which frames are keyframes or not.

Last, to validate our approach we conducted a user study in which both applications were compared. Users were asked to complete a survey regarding to different summaries created by means of the original application and with the one developed in this project. The results obtained were analyzed and they showed that the improvement done in the keyframes extraction module improves slightly the application performance and the quality of the generated summaries.

Keyframe-based Video Summarization Designer from Xavier Giro
}, author = {Ramos-Caballero, Carlos}, editor = {Eidenberger, Horst and Xavier Gir{\'o}-i-Nieto} }