# ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages Source: [ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:16:13.371Z Authors: Colin Lockard, Prashant Shiralkar, Xin Luna Dong, Hannaneh Hajishirzi Published: 2020-05-14T16:15:58.000Z ## Abstract In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for "zero-shot" open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31% F1 gain over a baseline for zero-shot extraction in a new subject vertical. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Open Information Extraction on SWDE University](): Open Information Extraction; SWDE University; Pair F1: 50. - [Open Information Extraction on SWDE Movie](): Open Information Extraction; SWDE Movie; Pair F1: 0.5. ## Original sources and implementation [Original paper]() [PDF]()