Entity oriented action recommendations for actionable knowledge graph generation
Entity oriented action recommendations for actionable knowledge graph generation
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DOI：
10.1145/3106426.3106546
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Md. Mostafizur Rahman;A. Takasu
中科院分区：
文献类型：
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作者：
Md. Mostafizur Rahman;A. Takasu
流行的搜索引擎最近利用知识图（KGs）的力量，以直接的方式为查询提供特定的答案。期望搜索引擎结果页（serp）为满足语义的查询提供事实响应。这鼓励研究人员提出更有影响力的知识图谱生成技术。为了实现和推进与可操作的知识图谱表示相关的技术，创建行动建议（ARs）是一个必要的步骤，也是一个相对较新的研究方向，以促进生成优化的知识图谱，以促进实体的行动。动作代表一个实体的身体或精神活动。例如，对于实体“Donald J. Trump”，典型的潜在行动可能是“赢得美国总统大选”或“针对美国记者”。在本文中，我们描述了基于实体实例和实体类型的相关操作建议的生成。我们提出了采用不同方法的两个模型。我们的第一个模型利用了半监督学习，我们引入了实体上下文向量（ECV）作为实体的区分特征，用于捕获实体的上下文，以揭示实体之间的相似性，以著名的word2vec模型为基础。第二个模型是基于朴素贝叶斯定理的概率方法。我们广泛地评估我们提出的模型。我们的第一个模型明显优于基于概率和监督学习的模型。
Popular search engines have recently utilized the power of knowledge graphs (KGs) to provide specific answers to queries in a direct way. Search engine result pages (SERPs) are expected to provide facts in response to queries that satisfy semantic meaning. This encourages researchers to propose more influential knowledge graph generation techniques. To achieve and advance the technologies related to actionable knowledge graph presentation, creating action recommendations (ARs) is an essential step and a relatively new research direction to nurture research on generating KGs that are optimized for facilitating an entity's actions. An action represents the physical or mental activity of an entity. For example, for the entity "Donald J. Trump", typical potential actions could be "won the US presidential election" or "targets US journalists". In this paper, we describe the generation of relevant action recommendations based on entity instance and entity type. We propose two models that employ different approaches. Our first model exploits semisupervised learning and we introduce entity context vector (ECV) as an entity's distinguishing features for capturing the context of entities to reveal the similarity between entities, grounded on the prominent word2vec model. The second model is a probabilistic approach based on the Naive Bayes Theorem. We extensively evaluate our proposed models. Our first model significantly outperforms probabilistic and supervised learning-based models.