多知识库问答的ILP联合模型：解耦与提升效率

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"随着知识库（KBs）的快速增长，多知识库问答（Multiple Knowledge Base QA）问题逐渐受到关注。与单一知识库问答的主要区别在于，多知识库QA需要处理不同KB之间的关联性或映射。传统的解决方案通常采用管道策略，首先独立构建KB之间的对应关系（即对齐），然后利用这些对齐信息构造查询。然而，这个过程并非易事，对齐的不精确可能会对后续的查询构建产生负面影响，因为错误的对齐会直接影响到问题解答的质量。现有的方法往往将对齐和查询构造视为两个分离的步骤，但我们的研究指出，这两个步骤实际上是相互依赖且相辅相成的。为了克服这个问题，我们提出了一种基于整数线性规划（Integer Linear Programming，ILP）的联合模型。该模型将对齐和查询构造融合在一个统一的框架内，通过联合优化来减少潜在的噪声和提高效率。我们的模型设计旨在通过ILP的有效求解，找到最佳的对齐方案，同时考虑到查询的有效构建，以确保答案的准确性。这种联合方法能够动态调整对齐和查询之间的交互，以适应多知识库环境中的复杂性。实验结果显示，与单独处理对齐和查询的传统方法相比，我们的模型在准确性和效率上都有显著提升，尤其是在处理复杂问题时，能够更有效地整合来自不同KB的信息，从而提高问答系统的整体性能。本研究不仅深化了我们对多知识库问答系统理解，还提供了一个创新的解决策略，有助于推动该领域的发展，并为实际应用中的知识检索和理解提供了有力支持。"

A Joint Model for Question Answering over Multiple Knowledge Bases

Yuanzhe Zhang, Shizhu He, Kang Liu, Jun Zhao

National Laboratory of Pattern Recognition (NLPR)

Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

{yzzhang, shizhu.he, kliu, jzhao}@nlpr.ia.ac.cn

Abstract

As the amount of knowledge bases (KBs) grows rapidly, the

problem of question answering (QA) over multiple KBs has

drawn more attention. The most signiﬁcant distinction be-

tween multiple KB-QA and single KB-QA is that the former

must consider the alignments between KBs. The pipeline s-

trategy ﬁrst constructs the alignments independently, and then

uses the obtained alignments to construct queries. However,

alignment construction is not a trivial task, and the introduced

noises would be passed on to query construction. By contrast,

we notice that alignment construction and query construction

are interactive steps, and jointly considering them would be

beneﬁcial. To this end, we present a novel joint model based

on integer linear programming (ILP), uniting these two pro-

cedures into a uniform framework. The experimental results

demonstrate that the proposed approach outperforms state-

of-the-art systems, and is able to improve the performance of

both alignment construction and query construction.

Introduction

With the continued growth of knowledge bases (KBs) on the

web, how to access such precious intellectual resources be-

comes increasingly important (Unger, Freitas, and Cimiano

2014). Knowledge base based question answering (KB-QA)

just focuses on this problem and is able to use natural lan-

guage as query language. Therefore, it has received more

attention in recent years.

The key problem in KB-QA is to convert natural lan-

guage questions into structured queries, such as SPARQL

queries. There are many researches that focus on this prob-

lem, and most of them are single KB-QA (Frank et al. 2007;

Zettlemoyer and Collins 2005; 2007; 2009; Kwiatkowski et

al. 2011; 2013). They often assume that the answers could

be acquired from a single KB. However, it is almost un-

practical that using a single KB could cover all questions.

A plenty of KBs exist on the web and they could focus on

different domains. It is not rare that a natural language ques-

tion involves many aspects, and each aspect is covered by

a relevant KB. Such question would be answered by using

multiple KBs. We name this task as multiple KB-QA, which

is seldom investigated before, except for (Lopez et al. 2012;

 2016, Association for the Advancement of Artiﬁcial

Music

General

Movie

mue:I_Dreamed

_a_Dream

mur:performer

mue:Anne_

Hathaway

gee:Anne_Hathaway gee:New_York

ger:birthPlace

moe:Anne_Hathaway

moe:Valentines

_Day(2010)

mor:starring

owl:sameAs

Figure 1: Three KBs should be used to answer the ques-

tion “Which songs are performed by person who was born

in New York and played a role in Valentine’s Day?”.

Shekarpour et al. 2014; Fader, Zettlemoyer, and Etzion-

i 2014).

This is a challenging task. For example, consider the fol-

lowing question:

Which songs are performed by person who was born in

New York and played a role in Valentine’s Day?

As illustrated in Figure 1, the answer to “songs performed

by” is in a music domain KB, and the answer to “born

in New York” is in a general domain KB, and answering

“played a role in Valentine’s Day” should turn to a movie

domain KB. The ﬁnal structured query is generated by unit-

ing different fragments as follows:

SELECT ?v1 WHERE {

h?v1, mur:perfomer, ?v2i

h?v2, owl:sameAs, ?v3i

h?v3, mor:starring, moe:Valentines Day(2010)i

h?v3, owl:sameAs, ?v4i

h?v4, ger:birthPlace, gee:New Yorki }

From this example, we can see that the most signiﬁcan-

t difference between multiple KB-QA and single KB-QA

is that the former needs to consider the interconnection-

s between heterogeneous KBs, such as h?v2, owl:sameAs,

This is a real case in Chinese QA scenario, and there is no such

a Chinese KB could answer it alone.

This triple pattern means that ?v2 is the performer of ?v1. The

ﬁrst two letters of the preﬁx represent the source KB (mo: movie,

mu: music and ge: general), and the last letter represents the type

(e: entity, c: class and r: relation). E.g., mur means the resource is

from music KB and is a relation.

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