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EARL:知识图上问答的联合实体和关系链接
EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs |
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| 课程网址: |
http://videolectures.net/iswc2018_dubey_earl_joint_entity/
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| 主讲教师: |
Mohnish Dubey
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| 开课单位: |
波恩-莱茵-西格应用科学大学计算机科学系
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| 开课时间: |
2018-11-22
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| 课程语种: |
英语
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| 中文简介: |
知识图上的许多问答系统依赖于实体和关系链接组件，以便将自然语言输入连接到基础知识图。传统上，实体链接和关系链接要么作为依赖的、连续的任务执行，要么作为独立的、并行的任务执行。在本文中，我们提出了一个名为EARL的框架，它将实体链接和关系链接作为一项联合任务来执行。EARL实施了两种不同的解决策略，我们在本文中对此进行了比较分析：第一种策略是将联合实体和关系链接任务形式化，作为广义旅行推销员问题（GTSP）的一个实例。为了在计算上可行，我们使用近似GTSP解算器。第二种策略使用机器学习来利用知识图中节点之间的连接密度。它依靠三个基本特征和重新排序步骤来预测实体和关系。我们比较了这些策略，并在一个有5000个问题的数据集上对它们进行了评估。这两种策略显著优于当前最先进的实体和关系链接方法。
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| 课程简介: |
Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph. Traditionally, entity linking and relation linking has been performed either as a dependent, sequential tasks or as independent, parallel tasks. In this paper, we propose a framework called EARL, which performs entity linking and relation linking as a joint task. EARL implements two different solution strategies for which we provide a comparative analysis in this paper: The first strategy is a formalization of the joint entity and relation linking tasks as an instance of the Generalised Travelling Salesman Problem (GTSP). In order to be computationally feasible, we employ approximate GTSP solvers. The second strategy uses machine learning in order to exploit the connection density between nodes in the knowledge graph. It relies on three base features and re-ranking steps in order to predict entities and relations. We compare the strategies and evaluate them on a dataset with 5000 questions. Both strategies significantly outperform the current state-of-the-art approaches for entity and relation linking.
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| 关 键 词: |
许多问答系统依赖于实体; 自然语言输入连接; 广义旅行推销员问题; 使用机器学习
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| 课程来源: |
视频讲座网
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| 数据采集: |
2023-01-16：cyh
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| 最后编审: |
2023-01-16：cyh
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| 阅读次数: |
32
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