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

Entity matching, a core data integration problem, is the task of decidingwhether two data tuples refer to the same real-world entity. Recent advances indeep learning methods, using pre-trained language models, were proposed forresolving entity matching. Although demonstrating unprecedented results, thesesolutions suffer from a major drawback as they require large amounts of labeleddata for training, and, as such, are inadequate to be applied to low resourceentity matching problems. To overcome the challenge of obtaining sufficientlabeled data we offer a new active learning approach, focusing on a selectionmechanism that exploits unique properties of entity matching. We argue that adistributed representation of a tuple pair indicates its informativeness whenconsidered among other pairs. This is used consequently in our approach thatiteratively utilizes space-aware considerations. Bringing it all together, wetreat the low resource entity matching problem as a Battleship game, huntingindicative samples, focusing on positive ones, through awareness of the latentspace along with careful planning of next sampling iterations. An extensiveexperimental analysis shows that the proposed algorithm outperformsstate-of-the-art active learning solutions to low resource entity matching, andalthough using less samples, can be as successful as state-of-the-art fullytrained known algorithms.

The Battleship Approach to the Low Resource Entity Matching Problem