Archive for the ‘Broccoli’ Category

Broccoli: Semantic Full-Text Search at your Fingertips

Friday, July 13th, 2012

Broccoli: Semantic Full-Text Search at your Fingertips by Hannah Bast, Florian Bäurle, Björn Buchhold, and Elmar Haussmann.

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, 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 EnglishWikipedia (32 GB XML dump) combined with the YAGO ontology (26 million facts). We have implemented a fully-functional prototype based on our ideas, see this http URL

It’s good to see CS projects work so hard to find unambiguous names. That won’t be confused with far more common uses of the same names. 😉

For all that, on quick review it does look like a clever, if annoyingly named, project.

Hmmm, doesn’t like the “-” (hyphen) character. “graph-theoretical tree” returns 0 results, “graph theoretical tree” returns 1 (the expected one).

Definitely worth a close read.

One puzzle though. There are a number of projects that use Wikipedia data dumps. The problem is most of the documents I am interested in searching aren’t in Wikipedia data dumps. Like the Enron emails.

Techniques that work well with clean data may work less well with documents composed of the vagaries of human communication. Or attempts at communication.