Supporting search engines with knowledge and context
- URL: http://arxiv.org/abs/2102.06762v1
- Date: Fri, 12 Feb 2021 20:28:25 GMT
- Title: Supporting search engines with knowledge and context
- Authors: Nikos Voskarides
- Abstract summary: In the first part of this thesis, we study how to make structured knowledge more accessible to the user.
In the second part of this thesis, we study how to improve interactive knowledge gathering.
In the final part of this thesis, we focus on search engine support for professional writers in the news domain.
- Score: 1.0152838128195467
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Search engines leverage knowledge to improve information access. In order to
effectively leverage knowledge, search engines should account for context,
i.e., information about the user and query. In this thesis, we aim to support
search engines in leveraging knowledge while accounting for context. In the
first part of this thesis, we study how to make structured knowledge more
accessible to the user when the search engine proactively provides such
knowledge as context to enrich search results. As a first task, we study how to
retrieve descriptions of knowledge facts from a text corpus. Next, we study how
to automatically generate knowledge fact descriptions. And finally, we study
how to contextualize knowledge facts, that is, to automatically find facts
related to a query fact. In the second part of this thesis, we study how to
improve interactive knowledge gathering. We focus on conversational search,
where the user interacts with the search engine to gather knowledge over large
unstructured knowledge repositories. We focus on multi-turn passage retrieval
as an instance of conversational search. We propose to model query resolution
as a term classification task and propose a method to address it. In the final
part of this thesis, we focus on search engine support for professional writers
in the news domain. We study how to support such writers create
event-narratives by exploring knowledge from a corpus of news articles. We
propose a dataset construction procedure for this task that relies on existing
news articles to simulate incomplete narratives and relevant articles. We study
the performance of multiple rankers, lexical and semantic, and provide insights
into the characteristics of this task.
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