English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Question answering over temporal knowledge graphs (KGs) efficiently usesfacts contained in a temporal KG, which records entity relations and when theyoccur in time, to answer natural language questions (e.g., "Who was thepresident of the US before Obama?"). These questions often involve threetime-related challenges that previous work fail to adequately address: 1)questions often do not specify exact timestamps of interest (e.g., "Obama"instead of 2000); 2) subtle lexical differences in time relations (e.g.,"before" vs "after"); 3) off-the-shelf temporal KG embeddings that previouswork builds on ignore the temporal order of timestamps, which is crucial foranswering temporal-order related questions. In this paper, we propose atime-sensitive question answering (TSQA) framework to tackle these problems.TSQA features a timestamp estimation module to infer the unwritten timestampfrom the question. We also employ a time-sensitive KG encoder to injectordering information into the temporal KG embeddings that TSQA is based on.With the help of techniques to reduce the search space for potential answers,TSQA significantly outperforms the previous state of the art on a new benchmarkfor question answering over temporal KGs, especially achieving a 32% (absolute)error reduction on complex questions that require multiple steps of reasoningover facts in the temporal KG.

Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs