Finding Patterns in a Knowledge Base using Keywords to Compose Table Answers

TL;DR

This paper presents methods to identify relevant patterns in knowledge bases for keyword queries, enabling the composition of table answers by efficiently matching query keywords to structured graph patterns.

Contribution

It introduces algorithms and indexing techniques for finding relevant patterns in knowledge graphs to generate table answers for keyword queries, addressing computational challenges.

Findings

Path-based indexes are memory-efficient.

Two query-processing algorithms outperform naive methods.

The proposed methods handle both small and large queries effectively.

Abstract

We aim to provide table answers to keyword queries against knowledge bases. For queries referring to multiple entities, like "Washington cities population" and "Mel Gibson movies", it is better to represent each relevant answer as a table which aggregates a set of entities or entity-joins within the same table scheme or pattern. In this paper, we study how to find highly relevant patterns in a knowledge base for user-given keyword queries to compose table answers. A knowledge base can be modeled as a directed graph called knowledge graph, where nodes represent entities in the knowledge base and edges represent the relationships among them. Each node/edge is labeled with type and text. A pattern is an aggregation of subtrees which contain all keywords in the texts and have the same structure and types on node/edges. We propose efficient algorithms to find patterns that are relevant to the query for a class of scoring functions. We show the hardness of the problem in theory, and propose path-based indexes that are affordable in memory. Two query-processing algorithms are proposed: one is fast in practice for small queries (with small patterns as answers) by utilizing the indexes; and the other one is better in theory, with running time linear in the sizes of indexes and answers, which can handle large queries better. We also conduct extensive experimental study to compare our approaches with a naive adaption of known techniques.