Researchers propose PatchScout for thematic web data collection
A new arXiv paper introduces PatchScout, a multi-agent framework designed to solve the problem of thematic web data collection. Unlike standard search queries that target specific answers, this task involves assembling reusable corpora of documents that share a common theme but are scattered across disconnected parts of the web. The authors argue that existing methods, such as traversal-based…
Key points
- PatchScout is a multi-agent framework for collecting thematic web data across disconnected sources.
- It uses patch-switching policies to balance local exploitation and exploring new regions.
- In a social science test, it found 102 previously unidentified entities in an existing dataset.
The paper frames this challenge as a long-horizon information foraging problem over query-induced semantic patches. The core difficulty is determining when to continue exploiting a local area versus when to switch to a new region under a fixed budget. PatchScout implements a class of patch-switching policies to balance these actions. Experiments on four live-web topics indicate that the framework significantly improves yield and domain coverage compared to baseline methods.
In a specific application within social science, the system was tested against an incomplete prior collection. It successfully identified 102 previously unidentified entities, demonstrating its capacity to expand existing thematic datasets. This approach offers a structured way to gather broad, semantically relevant data from heterogeneous sources.
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