Graduate Thesis Or Dissertation


Knowledge Base Completion Leveraging Natural Language Data Public Deposited

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  • Information about named entities (real-world objects) is usually harvested from different sources and organized as a multiple relational directed graph in Knowledge Bases (KBs). KBs play essential roles in many NLP modules including question answering, fact-checking, search engines, etc. KBs are big but still incomplete: relational information among entities is missing. This phenomenon leads to the degradation of the performance for the consumers of a KB. In this work, we aim to mitigate the incompleteness issue by proposing models in two different paradigms of work: i) Extracting information from unstructured/unlabeled textual modality and integrating them into a KB ii) Improving expressiveness of current link prediction models developed on an observed relational graph. For the first paradigm, we propose 1) A novel global Named Entity Disambiguation (NED) model that formulates NED as a structure prediction problem in a limited discrepancy search framework. This model starts searching from a complete solution constructed by a local model and conducts a search in the space of few possible corrections leading to improved assignments from a global point of view. 2) A novel local NED model based on a state-of-the-art language model to efficiently leverage contextual cues for disambiguation. 3) A novel approach for relation extraction model with distant supervision to predicts relation types based on right reasons leading to improved transparency and robustness. 4) A novel mechanism for unsupervised relation learning. 5) Improved fusion model for integrating relations extracted from text into observed KB. For the second paradigm, we propose a novel approach to enforce logical rules into state-of-the-art link prediction models that is important for the transparency of such models.
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