Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints

TL;DR

This paper introduces a novel approach for query answering on knowledge graphs that incorporates soft, context-dependent constraints, enhancing flexibility and user interaction without sacrificing performance.

Contribution

The paper formalizes query answering with soft constraints and proposes two efficient methods that adjust answer scores while preserving original rankings.

Findings

Methods effectively incorporate soft constraints into query answering.

Approaches maintain high performance with minimal computational overhead.

Extended benchmarks demonstrate robustness and flexibility of the proposed methods.

Abstract

Methods for query answering over incomplete knowledge graphs retrieve entities that are \emph{likely} to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However, existing approaches have focused on queries formalized using first-order-logic. In practice, many real-world queries involve constraints that are inherently vague or context-dependent, such as preferences for attributes or related categories. Addressing this gap, we introduce the problem of query answering with soft constraints. We formalize the problem and introduce two efficient methods designed to adjust query answer scores by incorporating soft constraints without disrupting the original answers to a query. These methods are lightweight, requiring tuning only two parameters or a small neural network trained to capture soft constraints while maintaining the original ranking structure. To evaluate the task, we extend existing QA benchmarks by generating datasets with soft constraints. Our experiments demonstrate that our methods can capture soft constraints while maintaining robust query answering performance and adding very little overhead. With our work, we explore a new and flexible way to interact with graph databases that allows users to specify their preferences by providing examples interactively.