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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Knowledge graphs and vector space models are robust knowledge representationtechniques with individual strengths and weaknesses. Vector space models excelat determining similarity between concepts, but are severely constrained whenevaluating complex dependency relations and other logic-based operations thatare a strength of knowledge graphs. We describe the VKG structure that helpsunify knowledge graphs and vector representation of entities, and enablespowerful inference methods and search capabilities that combine theircomplementary strengths. We analogize this to thinking `fast' in vector spacealong with thinking 'slow' and `deeply' by reasoning over the knowledge graph.We have created a query processing engine that takes complex queries anddecomposes them into subqueries optimized to run on the respective knowledgegraph or vector view of a VKG. We show that the VKG structure can processspecific queries that are not efficiently handled by vector spaces or knowledgegraphs alone. We also demonstrate and evaluate the VKG structure and the queryprocessing engine by developing a system called Cyber-All-Intel for knowledgeextraction, representation and querying in an end-to-end pipeline grounded inthe cybersecurity informatics domain.

Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs