This paper addresses the task of conversational question answering (ConvQA)
over knowledge graphs (KGs). The majority of existing ConvQA methods rely on
full supervision signals with a strict assumption of the availability of gold
logical forms of queries to extract answers from the KG. However, creating such
a gold logical form is not viable for each potential question in a real-world
scenario. Hence, in the case of missing gold logical forms, the existing
information retrieval-based approaches use weak supervision via heuristics or
reinforcement learning, formulating ConvQA as a KG path ranking problem.
Despite missing gold logical forms, an abundance of conversational contexts,
such as entire dialog history with fluent responses and domain information, can
be incorporated to effectively reach the correct KG path. This work proposes a
contrastive representation learning-based approach to rank KG paths
effectively. Our approach solves two key challenges. Firstly, it allows weak
supervision-based learning that omits the necessity of gold annotations.
Second, it incorporates the conversational context (entire dialog history and
domain information) to jointly learn its homogeneous representation with KG
paths to improve contrastive representations for effective path ranking. We
evaluate our approach on standard datasets for ConvQA, on which it
significantly outperforms existing baselines on all domains and overall.
Specifically, in some cases, the Mean Reciprocal Rank (MRR) and Hit@5 ranking
metrics improve by absolute 10 and 18 points, respectively, compared to the
state-of-the-art performance.

通过对话历史和领域信息进行对比表示学习，本文提出了一种基于对比表示学习的方法，用于无需黄金注释的弱监督下进行知识图谱路径排名。 实验结果表明，该方法在所有领域和整体上显着优于现有基线，排名指标相对于现有技术表现提高了 10 到 18 个百分点。

知识图谱对话问答中的对比表示学习

Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs

Nowadays Knowledge Graphs constitute a mainstream approach for the
representation of relational information on big heterogeneous data, however,
they may contain a big amount of imputed noise when constructed automatically.
To address this problem, different error detection methodologies have been
proposed, mainly focusing on path ranking and representation learning. This
work presents various mainstream approaches and proposes a hybrid and modular
methodology for the task. We compare different methods on two benchmarks and
one real-world biomedical publications dataset, showcasing the potential of our
approach and providing insights on graph embeddings when dealing with noisy
Knowledge Graphs.

本文研究知识图谱中可能存在的噪声问题，并提出混合模块化的方法进行错误检测和关系表示，对两个基准测试和一个实际的生物医学出版物数据集进行比较分析，展示了处理噪声知识图谱时图形嵌入技术的潜力和见解。

使用路径排名方法指导图嵌入，用于在嘈杂的知识图谱中进行错误检测

Guiding Graph Embeddings using Path-Ranking Methods for Error Detection  innoisy Knowledge Graphs

Reasoning over paths in large scale knowledge graphs is an important problem
for many applications. In this paper we discuss a simple approach to
automatically build and rank paths between a source and target entity pair with
learned embeddings using a knowledge base completion model (KBC). We assembled
a knowledge graph by mining the available biomedical scientific literature and
extracted a set of high frequency paths to use for validation. We demonstrate
that our method is able to effectively rank a list of known paths between a
pair of entities and also come up with plausible paths that are not present in
the knowledge graph. For a given entity pair we are able to reconstruct the
highest ranking path 60% of the time within the the top 10 ranked paths and
achieve 49% mean average precision. Our approach is compositional since any KBC
model that can produce vector representations of entities can be used.

本论文探讨了一种简单的方法，使用知识库完成模型（KBC）和学习的嵌入向量，自动构建和排序源实体和目标实体对之间的路径。我们使用获取的生物医学科学文献挖掘了一个知识图，并提取了一组高频路径用于验证。实验结果表明，我们的方法能够有效地排列实体对之间的已知路径，并提出了未在知识图中存在的合理路径。对于给定的实体对，我们能够在排名前 10 的路径中 60% 的时间内重构出排名最高的路径，并达到 49％的平均精度。由于任何能够产生实体向量表示的 KBC 模型都可以使用，因此我们的方法具有组合性