Broccoli: Semantic Full-Text Search at your Fingertips

TL;DR

Broccoli is a novel search engine that combines full-text and ontology search, enabling users to construct complex semantic queries through an intuitive interface, with fast query processing demonstrated on Wikipedia and YAGO data.

Contribution

This paper introduces Broccoli, a new semantic full-text search engine with a novel query language, indexing method, and user interface for efficient and intuitive semantic searches.

Findings

Fast query processing on large datasets

High quality of search results

Effective user-guided query construction

Abstract

We present Broccoli, a fast and easy-to-use search engine for what we call semantic full-text search. Semantic full-text search combines the capabilities of standard full-text search and ontology search. The search operates on four kinds of objects: ordinary words (e.g., edible), classes (e.g., plants), instances (e.g., Broccoli), and relations (e.g., occurs-with or native-to). Queries are trees, where nodes are arbitrary bags of these objects, and arcs are relations. The user interface guides the user in incrementally constructing such trees by instant (search-as-you-type) suggestions of words, classes, instances, or relations that lead to good hits. Both standard full-text search and pure ontology search are included as special cases. In this paper, we describe the query language of Broccoli, the main idea behind a new kind of index that enables fast processing of queries from that language as well as fast query suggestion, the natural language processing required, and the user interface. We evaluated query times and result quality on the full version of the English Wikipedia (40 GB XML dump) combined with the YAGO ontology (26 million facts). We have implemented a fully functional prototype based on our ideas and provide a web application to reproduce our quality experiments. Both are accessible via http://broccoli.informatik.uni-freiburg.de/repro-corr/ .