Query Interpretations from Entity-Linked Segmentations

TL;DR

This paper presents a method for interpreting ambiguous web search queries by segmenting them and linking parts to entities in a knowledge base, improving accuracy and efficiency.

Contribution

It introduces a segmentation-based approach for deriving multiple entity-linked interpretations of queries, enhancing both effectiveness and response time.

Findings

Outperforms previous methods in interpretation accuracy.

Achieves faster response times suitable for web search.

Effective on a combined dataset of existing query entity linking benchmarks.

Abstract

Web search queries can be ambiguous: is "source of the nile" meant to find information on the actual river or on a board game of that name? We tackle this problem by deriving entity-based query interpretations: given some query, the task is to derive all reasonable ways of linking suitable parts of the query to semantically compatible entities in a background knowledge base. Our suggested approach focuses on effectiveness but also on efficiency since web search response times should not exceed some hundreds of milliseconds. In our approach, we use query segmentation as a pre-processing step that finds promising segment-based "interpretation skeletons". The individual segments from these skeletons are then linked to entities from a knowledge base and the reasonable combinations are ranked in a final step. An experimental comparison on a combined corpus of all existing query entity linking datasets shows our approach to have a better interpretation accuracy at a better run time than the previously most effective methods.