A Practical Guide to Hybrid Natural Language Processing Combining Neural Models and Knowledge Graphs for NLP

This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs. To this end, it first introduces the main building blocks and then describes how they can be integrated to support the effective implementation of real-world NLP applications. To illustrate the ideas described, the book also includes a comprehensive set of experiments and exercises involving different algorithms over a selection of domains and corpora in various NLP tasks.

Throughout, the authors show how to leverage complementary representations stemming from the analysis of unstructured text corpora as well as the entities and relations described explicitly in a knowledge graph, how to integrate such representations, and how to use the resulting features to effectively solve NLP tasks in a range of domains. In addition, the book offers access to executable code with examples, exercises and real-world applications in key domains, like disinformation analysis and machine reading comprehension of scientific literature. All the examples and exercises proposed in the book are available as executable Jupyter notebooks in a GitHub repository. They are all ready to be run on Google Colaboratory or, if preferred, in a local environment.

A valuable resource for anyone interested in the interplay between neural and knowledge-based approaches to NLP, this book is a useful guide for readers with a background in structured knowledge representations as well as those whose main approach to AI is fundamentally based on logic. Further, it will appeal to those whose main background is in the areas of machine and deep learning who are looking for ways to leverage structured knowledge bases to optimize results along the NLP downstream.

Part I: Preliminaries and Building Blocks.- Hybrid Natural Language Processing: An Introduction.- Word, Sense, and Graph Embeddings.- UnderstandingWord Embeddings and Language Models.- Capturing Meaning from Text asWord Embeddings.- Capturing Knowledge Graph Embeddings.- Part II: Combining Neural Architectures and Knowledge Graphs.- Building Hybrid Representations from Text Corpora, Knowledge Graphs, and Language Models.- Quality Evaluation.- Capturing Lexical, Grammatical, and Semantic Information with Vecsigrafo.- Aligning Embedding Spaces and Applications for Knowledge Graphs.- Part III: Applications.- A Hybrid Approach to Disinformation Analysis.- Jointly Learning Text and Visual Information in the Scientific Domain.- Looking into the Future of Natural Language Processing.

Jose Manuel Gomez-Perez leads the Cogito Research Lab at Expert System in Madrid, Spain, where he focuses on the combination of neural and knowledge-based approaches to enable reading comprehension in machines. His work lies at the intersection of several areas of artificial intelligence, including natural language processing, knowledge graphs and deep learning. He also consults for organizations like the European Space Agency and is the co-founder of ROHub.org, a platform for the intelligent management of scientific information. A former Marie Curie fellow, José Manuel holds a Ph.D. in Computer Science and Artificial Intelligence from Universidad Politécnica de Madrid. He regularly publishes in top scientific conferences and journals and his views have appeared in magazines like Nature and Scientific American, as well as newspapers like El País.

Ronald Denaux is a senior researcher scientist at Expert System. Ronald obtained his MSc in Computer Science from the Technical University Eindhoven, The Netherlands. After a couple of years working in industry as a software developer for a large IT company in The Netherlands, Ronald decided to go back to academia. He obtained a Ph.D., again in Computer Science, from the University of Leeds, UK. Ronald’s research interests have revolved around making semantic web technologies more usable for end users, which has required research into the areas of ontology authoring and reasoning, natural language interfaces, dialogue systems, intelligent user interfaces and user modelling.

Andres Garcia-Silva is a senior research scientist at Expert System, where he works on a variety of fields related to knowledge management and artificial intelligence including semantic technologies, natural language processing, information extraction and retrieval, and machine learning. Andrés holds a Ph.D. and a Master degree in Artificial Intelligence from Universidad Politécnica de Madrid. He has worked as a visiting researcher at the University of Southampton, the Free University of Berlin, and the University of Southern California. Andrés regularly publishes and reviews papers for conferences and workshops in the semantic web research community.