# Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives

## TL;DR

This paper explores using semantic analysis of URLs in large Web archives to efficiently generate initial annotations, focusing on named entity recognition to improve access to vast, long-term Web data.

## Contribution

It demonstrates that named entity recognition can be effectively applied to URLs, providing a scalable method for annotating large Web archives.

## Key findings

- Named entity recognition achieves high precision on URLs
- A significant proportion of URLs contain extractable semantic information
- URLs can serve as efficient proxies for document annotation

## Abstract

Long-term Web archives comprise Web documents gathered over longer time periods and can easily reach hundreds of terabytes in size. Semantic annotations such as named entities can facilitate intelligent access to the Web archive data. However, the annotation of the entire archive content on this scale is often infeasible. The most efficient way to access the documents within Web archives is provided through their URLs, which are typically stored in dedicated index files.The URLs of the archived Web documents can contain semantic information and can offer an efficient way to obtain initial semantic annotations for the archived documents. In this paper, we analyse the applicability of semantic analysis techniques such as named entity extraction to the URLs in a Web archive. We evaluate the precision of the named entity extraction from the URLs in the Popular German Web dataset and analyse the proportion of the archived URLs from 1,444 popular domains in the time interval from 2000 to 2012 to which these techniques are applicable. Our results demonstrate that named entity recognition can be successfully applied to a large number of URLs in our Web archive and provide a good starting point to efficiently annotate large scale collections of Web documents.