This project will develop and empirically evaluate methods for creating subjective knowledge bases: databases of opinions and viewpoints as they are asserted by individuals in books, web forums, and social media.
While most knowledge base research seeks to extract real-world truth from text, many factual assertions are either about inherently subjective propositions (such as "apples are delicious") or are non-subjective assertions that happen to contradict other belief holders or even consensus reality (such as "the Earth is flat").
This project pioneers new methods to automatically extract expressions of opinions and viewpoints from a textual corpus and use those assertions to build a subjective knowledge base that can accommodate contradictory and conflicting statements from different authors. Such a subjective knowledge base will help researchers answer a range of questions: What contradictory claims are being made in historical books, or contemporary social media? What propositions does a particular ideological community hold, and are they compatible with, or contradictory to, those held by other communities?
We aim to lay the foundation for understanding a broad range of phenomena that can be seen as conflicts between coherent viewpoints. The resulting computational models will lay the groundwork for intelligent systems that are robust with respect to the way in which propositions are used in the real world; as applications in artificial intelligence are being deployed more and more in social contexts, this research will inform these methods with more nuanced information about the diversity of human viewpoints.
Finally, this work will also include a substantial educational component, incorporating human context into algorithm design in undergraduate STEM education and broadening the use of natural language processing and machine learning across a range of disciplines.
This material is based upon work supported by the National Science Foundation for the project "III: Small: Collaborative Research: Building subjective knowledge bases by modeling viewpoints," IIS-1814955 (O'Connor) and IIS-1813470 (Bamman). Expected duration: 2018-2021.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Last updated July 1, 2019. Contact: firstname.lastname@example.org and email@example.com