@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} } @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 {cFernandezc, title = {Linking Media: adopting Semantic Technologies for multimodal media connection}, booktitle = {International Semantic Web Conference - ISWC (Industry Track)}, year = {2018}, month = {08/2018}, address = {Monterey, CA, USA}, abstract = {

Today{\textquoteright}s media and news organizations are constantly generating large amounts of multimedia content, majorly delivered online. As the online media market grows, the management and delivery of contents is becoming a challenge.\ Computational approaches can help to overcome this challenge by governing different applications such as content creation, production, search, and its promotion and distribution to different audiences.\ In this abstract we present a success story of the adoption of semantic technologies on the aforementioned applications, which\ \ are built on top of a semantic tagging framework, based on a Knowledge Graph (KG).\ The presented pipeline combines multimodal inputs into a contextual entity linking module, which indexes documents and links them to trends and stories developing on the news.\ \ We will describe how documents are linked and provided to media producers through Vilynx{\textquoteright}s platform, which is currently indexing over 20k media documents a day.

}, keywords = {Knowledge graph; Linked data; Multimedia; Semantic web Linked data; Computational approach; Content creation; Knowledge graphs; Multimedia; Multimedia contents; Multimodal inputs; Semantic tagging; Semantic technologies; Semantic Web}, url = {http://ceur-ws.org/Vol-2180/}, author = {Fern{\`a}ndez, D{\`e}lia and Bou-Balust, Elisenda and Xavier Gir{\'o}-i-Nieto} } @unpublished {xFernandezb, title = {Multimodal Knowledge Base Population from News Streams for Media Applications}, year = {2018}, month = {07/2018}, type = {Phd thesis proposal}, abstract = {

Media producers publish large amounts of multimedia content online - both text, audio\ and video. As the online media market grows, the management and delivery of contents\ is becoming a challenge. Semantic and Linking technologies can be used to organize and\ exploit this contents. This dissertation addresses the problem of integrating Semantic Web\ technologies and linking data technologies into Vilynx{\textquoteright}s platform, a system used by media\ producers to manage and explode its contents. For that purpose, Knowledge Graphs (KG)\ and its maintenance through multimodal Knowledge Base Population (KBP) from online\ data extracted from the Web is studied. The Web is a very large unstructured data source\ with millions of text, images, videos and audio. This thesis is willing to generate solutions\ to facilitate automatic learning from these multimodal data and use it in real product applications\ for media.

This thesis is going to be structured in three parts. The first part of the thesis will cover\ the construction of a multimodal KG, which will be the core of the system for knowledge\ extraction, standardization and contextualization.

The\ second part will consist on the construction of the tools that will be used for KBP. For\ that we will construct a multimodal semantic tagging framework, based on the previously\ mentioned KG. This block addresses some typical challenges of KBP and data mining, like:\ name entity recognition (NER), entity linking (EL), context set construction (CSC), structured\ data creation, standardization, entity matching and data fusion.

The third part will focus on the extraction of knowledge from the Web to populate the knowledge\ base. As the KG domain is media, we will populate the KG using events detected from\ news streams using a multilmodal perspective. To detect events we will construct a news\ aggregator system. This part will deal with the problems of Topic Detection and Tracking\ (TDT), Topic Modeling (TM) and multi-document summarization. From these data we will\ learn relations between world entities, that will populate our KG, dealing with the automatic\ detection and update of concepts and relations. Also social media information will be\ analyzed to understand trendiness and world interests.

}, keywords = {Entity Detection, Entity Linking, Knowledge Base Population, Knowledge Graph, Linked Technologies, Multi-document Summarization, multimedia, Multimodal Systems, Natural Language Processing, Semantic Web, Topic Detection and Tracking, Topic Modeling}, author = {Fern{\`a}ndez, D{\`e}lia and Bou-Balust, Elisenda and Xavier Gir{\'o}-i-Nieto} }