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首页> 外文会议>Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications >Embedding Dynamic Knowledge Graphs based on Observational Ontologies in Semantic Vector Spaces
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Embedding Dynamic Knowledge Graphs based on Observational Ontologies in Semantic Vector Spaces

机译：基于语义向量空间中的观测性本体嵌入动态知识图

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Knowledge graphs (KGs) provide a useful representation format for capturing complex knowledge about an information domain, with rich logical descriptions available for defining the relationships between entities. Separately, semantic vector spaces (SVSs) capture the relative meanings of terms based on their actual usage within a dataset and allow useful operations for exploring the relationships between these terms. Combining KGs and SVSs via knowledge graph embedding (KGE) enables further analysis tasks to leverage learned semantic vectors to gain additional insights. Therefore, KGE represents an interesting and potentially powerful tool for identifying emergent or unexpected behavior, or for seeking previously unaccounted for relationships, event, and groups. In this work, we report on the state-of-the-art in KGE. We describe the operational benefits that can be gained from this approach and the considerations that apply for observational ontologies that describe a complex, untrusted, time-sensitive, and rapidly-evolving environment. We suggest several promising avenues for future research in this context.
机译：知识图（kgs）提供了一种有用的表示格式，用于捕获有关信息域的复杂知识，具有用于定义实体之间的关系的丰富逻辑描述。单独地，语义矢量空间（SVSS）基于数据集内的实际使用捕获术语的相对含义，并允许有用的操作来探索这些术语之间的关系。通过知识图形嵌入（KGE）结合KGS和SVSS，可以进一步分析任务来利用学习的语义向量来获得额外的见解。因此，KGE代表了识别紧急或意外行为的有趣和潜在的强大工具，或者寻求以前未计算的关系，事件和群体。在这项工作中，我们在KGE中报告了最先进的。我们描述了可以从这种方法中获得的运营效益以及适用于描述复杂，不受信任，时间敏感和快速发展的环境的观察本体的考虑因素。我们建议在这方面的未来研究中提出了几个有希望的途径。

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