Collaboratively built semi-structured content and Artificial Intelligence: The story so far

  • Authors:

  • Eduard Hovy;Roberto Navigli;Simone Paolo Ponzetto

  • Affiliations:

  • Information Sciences Institute, University of Southern California, United States;Dipartimento di Informatica, Sapienza University of Rome, Italy;Dipartimento di Informatica, Sapienza University of Rome, Italy

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2013

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Abstract

Recent years have seen a great deal of work that exploits collaborative, semi-structured content for Artificial Intelligence (AI) and Natural Language Processing (NLP). This special issue of the Artificial Intelligence Journal presents a variety of state-of-the-art contributions, each of which illustrates the substantial impact that work on leveraging semi-structured content is having on AI and NLP as it continuously fosters new directions of cutting-edge research. We contextualize the papers collected in this special issue by providing a detailed overview of previous work on collaborative, semi-structured resources. The survey is made up of two main logical parts: in the first part, we present the main characteristics of collaborative resources that make them attractive for AI and NLP research; in the second part, we present an overview of how these features have been exploited to tackle a variety of long-standing issues in the two fields, in particular the acquisition of large amounts of machine-readable knowledge, and its application to a wide range of tasks. The overall picture shows that not only are semi-structured resources enabling a renaissance of knowledge-rich AI techniques, but also that significant advances in high-end applications that require deep understanding capabilities can be achieved by synergistically exploiting large amounts of machine-readable structured knowledge in combination with sound statistical AI and NLP techniques.