Knowledge Extraction in Web Media: At The Frontier of NLP, Machine Learning and Semantics

摘要: The Web offers a vast amount of structured and unstructured content from which more and more advanced techniques are developed for extracting entities and relations between entities, one of the key elements for feeding the various knowledge graphs that are being developed by major Web companies as part of their product offerings. Most of the knowledge available on the Web is present as natural language text enclosed in Web documents aimed at human consumption. A common approach for obtaining programmatic access to such a knowledge uses information extraction techniques. It reduces texts written in natural languages to machine readable structures, from which it is possible to retrieve entities and relations, for instance obtaining answers to database-style queries. This thesis aims to contribute to the emerging idea that entities should be a first class citizen on the Web. A common research line consists in annotating texts such as users’ posts, item descriptions, video subtitles, with entities that are uniquely identified in some knowledge bases as part of the Global Giant Graph. The Natural Language Processing (NLP) community has been addressing this crucial task for the past few decades. As a result, the community has established gold standards and metrics to evaluate the performance of algorithms in important tasks such as co-reference Resolution, Named Entity Recognition, Entity Linking and Relationship Extraction, just to mention few examples. Some of these topics overlap with research that the Database Systems and more recently the Knowledge Engineering communities have been addressing also for decades, such as …