Knowledge Graph Augmentation (KGA) is the task of adding facts to an incomplete knowledge graph by extracting then primarily from textual sources on the Web, to improve the effectiveness of the knowledge graph in applications such as search and question answering. State-of-the-art KGA methods rely on information extraction from running ...

Modern Knowledge Bases are used by millions on a daily basis in applications such as search and question answering, recommender systems, among others. Moreover, open knowledge bases like DBpedia and Wikidata are the fabric that keeps the Linked Open Data ecosystem alive, enabling scholarly research across all disciplines. Nevertheless, high-quality ...

Knowledge Bases have always been central to both the theory and practice of Artificial Intelligence. Today there are massive knowledge bases used by millions on a daily basis in applications such as search and question answering. Open knowledge bases like DBpedia and Wikidata are the fabric that keeps the Linked ...

Neural Fine-grained Entity Type Classification is the task of assigning a fine-grained type (e.g., athlete or artist instead of just person) to an entity mentioned in text. Fine-grained types have been shown superior to the standard generic types for most NLP tasks, including information extraction which is our main ...

Two crucial tasks in natural language understanding have to do with named entities, which are the persons, organizations, locations, etc. that are explicitly mentioned in text using proper nouns: Named Entity Recognition (NER) which is finding mentions to entities in the text; and Named Entity Disambiguation (NED), also known as ...

Cyberbullying is a a real and worrisome problem affecting many school children across the world. Sadly, it is becoming customary to hear tragic stories in the news about cases of cyberbullying leading to self harm or worse. Although these extreme cases are a minority, a large number of children go ...