Multilinguality in knowledge graphs

Abstract

Content on the web is predominantly in English, which makes it inaccessible to individuals who exclusively speak other languages. Knowledge graphs can store multilingual information, facilitate the creation of multilingual applications, and make these accessible to more language communities. In this thesis, we present studies to assess and improve the state of labels and languages in knowledge graphs and apply multilingual information. We propose ways to use multilingual knowledge graphs to reduce gaps in coverage between languages.
We explore the current state of language distribution in knowledge graphs by developing a framework - based on existing standards, frameworks, and guidelines - to measure label and language distribution in knowledge graphs. We apply this framework to a dataset representing the web of data, and to Wikidata. We find that there is a lack of labelling on the web of data, and a bias towards a small set of languages. Due to its multilingual editors, Wikidata has a better distribution of languages in labels. We explore how this knowledge about labels and languages can be used in the domain of question answering. We show that we can apply our framework to the task of ranking and selecting knowledge graphs for a set of user questions A way of overcoming the lack of multilingual information in knowledge graphs is to transliterate and translate knowledge graph labels and aliases. We propose the automatic classification of labels into transliteration or translation in order to train a model for each task. Classification before generation improves results compared to using either a translation- or transliteration-based model on their own. A use case of multilingual labels is the generation of article placeholders for Wikipedia using neural text generation in lower-resourced languages. On the basis of surveys and semi-structured interviews, we show that Wikipedia community members find the placeholder pages, and especially the generated summaries, helpful, and are highly likely to accept and reuse the generated text.

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Identifiers

ORCID for Lucie-Aimée Kaffee: orcid.org/0000-0002-1514-8505

ORCID for Leslie Carr: orcid.org/0000-0002-2113-9680

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