Filip Ilievski – författare

The digital era has generated a huge amount of data on the identities (profiles) of people, organizations and other entities in a digital format, largely consisting of textual documents such as news articles, encyclopedias, personal websites, books, and social media. Identity has thus been transformed from a philosophical to a societal issue, one requiring robust computational tools to determine entity identity in text.Computational systems developed to establish identity in text often struggle with long-tail cases. This book investigates how Natural Language Processing (NLP) techniques for establishing the identity of long-tail entities – which are all infrequent in communication, hardly represented in knowledge bases, and potentially very ambiguous – can be improved through the use of background knowledge. Topics covered include: distinguishing tail entities from head entities; assessing whether current evaluation datasets and metrics are representative for long-tail cases; improving evaluation of long-tail cases; accessing and enriching knowledge on long-tail entities in the Linked Open Data cloud; and investigating the added value of background knowledge (“profiling”) models for establishing the identity of NIL entities.Providing novel insights into an under-explored and difficult NLP challenge, the book will be of interest to all those working in the field of entity identification in text.

This book enables readers to understand the challenges and opportunities of developing human-centered AI with commonsense reasoning abilities. Despite apparent accuracy improvements brought by large neural models across task benchmarks, common sense is still lacking. The lack of common sense affects many tasks, including story understanding, decision-making, and question answering. Commonsense knowledge and reasoning have long been considered the “black matter” of AI, raising concerns about the trustworthiness and applicability of AI methods in both autonomous and hybrid applications. This book describes how to design a more robust, collaborative, explainable, and responsible AI through incorporating neuro-symbolic commonsense reasoning. In addition, the book provides examples of how these properties of AI can facilitate a wide range of social-good applications in digital democracy, traffic monitoring, education, and robotics. What makes commonsense reasoning such a unique and impactful challenge? What can we learn from cognitive research when designing and developing AI systems? How can we approach building responsible, robust, collaborative, and explainable AI with common sense? And finally, what is the impact of this work on human-AI teaming? This book provides an accessible introduction and exploration of these topics.