@inproceedings{christodoulopoulos-2018-knowledge,
    title = "Knowledge Representation and Extraction at Scale",
    author = "Christodoulopoulos, Christos",
    editor = "Anke, Luis Espinosa  and
      Gromann, Dagmar  and
      Declerck, Thierry",
    booktitle = "Proceedings of the Third Workshop on Semantic Deep Learning",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/W18-4007/",
    pages = "46",
    abstract = "These days, most general knowledge question-answering systems rely on large-scale knowledge bases comprising billions of facts about millions of entities. Having a structured source of semantic knowledge means that we can answer questions involving single static facts (e.g. {\textquotedblleft}Who was the 8th president of the US?{\textquotedblright}) or dynamically generated ones (e.g. {\textquotedblleft}How old is Donald Trump?{\textquotedblright}). More importantly, we can answer questions involving multiple inference steps ({\textquotedblleft}Is the queen older than the president of the US?{\textquotedblright}). In this talk, I{'}m going to be discussing some of the unique challenges that are involved with building and maintaining a consistent knowledge base for Alexa, extending it with new facts and using it to serve answers in multiple languages. I will focus on three recent projects from our group. First, a way of measuring the completeness of a knowledge base, that is based on usage patterns. The definition of the usage of the KB is done in terms of the relation distribution of entities seen in question-answer logs. Instead of directly estimating the relation distribution of individual entities, it is generalized to the {\textquotedblleft}class signature{\textquotedblright} of each entity. For example, users ask for baseball players' height, age, and batting average, so a knowledge base is complete (with respect to baseball players) if every entity has facts for those three relations. Second, an investigation into fact extraction from unstructured text. I will present a method for creating distant (weak) supervision labels for training a large-scale relation extraction system. I will also discuss the effectiveness of neural network approaches by decoupling the model architecture from the feature design of a state-of-the-art neural network system. Surprisingly, a much simpler classifier trained on similar features performs on par with the highly complex neural network system (at 75x reduction to the training time), suggesting that the features are a bigger contributor to the final performance. Finally, I will present the Fact Extraction and VERification (FEVER) dataset and challenge. The dataset comprises more than 185,000 human-generated claims extracted from Wikipedia pages. False claims were generated by mutating true claims in a variety of ways, some of which were meaningaltering. During the verification step, annotators were required to label a claim for its validity and also supply full-sentence textual evidence from (potentially multiple) Wikipedia articles for the label. With FEVER, we aim to help create a new generation of transparent and interprable knowledge extraction systems."
}