3.1 Knowledge Graph Definition 3.2 Knowledge Prompt 3.3 KG-LLM Framework 4 Experiments 4.1 Experimental Setup 4.2 Multi-hop Link Prediction without In-Context Learning 4.3 Multi-hop Link Prediction with In-Context Learning 4.4 Multi-hop Relation Prediction without In-Context Learning 4.5 Multi-hop Rela...
Knowledge Graph Embedding for Link Prediction: A Comparative Analysis 作者:Andrea Rossi、Donatella Firmani、Antonio Matinata、Paolo Merialdo、Denilson Barbosa 论文链接:https://arxiv.org/pdf/2002.00819.pdf 摘要:知识图谱(Knowledge graph, KGs)在工业和学术领域有很多应用,这反过来又推动了从各种来源大规模提...
Link Prediction.The link prediction (LP) task, one of the commonly researched knowledge graph completion tasks, attempts to predict the missing head entity (h) or tail entity (t) of a triple (h, r, t) given a KG G = (E, R), where {h, t} ∈ E is the set of all entities and...
(3)Graphlets and path-based methods 最后,正如我们对节点级特性的讨论一样,在图上定义特性的一个有效而强大的策略是简单地计算不同小子图结构(在此上下文中通常称为graphlets)的出现次数。在形式上,graphlet内核涉及枚举特定大小的所有可能的图形结构,并计算它们在整个图形中出现的次数。这种方法的挑战在于,尽管已...
Knowledge graphsKnowledge graph embeddingsLink predictionLink Prediction (LP) on Knowledge Graphs (KGs) has recently become a sparkling research topic, benefiting from the explosion of machine learning techniques. Several relation-learning models are published every year, mostly relying on KG embeddings. ...
现在的知识图谱可以包含数以亿计的事实(fact),但是知识图谱不可能包含所有实际中存在的事实。因此,链路预测(link prediction)/知识图谱补全(knowledge base completion)成为了研究的一个方向。即如何根据已有的事实,预测可能存在的事实。 受到词嵌入的启发,知识图谱嵌入(knowgraph graph embedding)——将知识图谱映射到离...
Knowledge Graph Embedding for Link Prediction:A Comparative Analysis 原因: 知识图谱(KGs)已在工业和学术界找到了许多应用,这反过来又促使了相当多的研究努力,从各种来源大规模提取信息,即使是最先进的KG也会出现不完整的情况。链接预测(LP)旨在解决KG不完整的问题,链接预测的不断发展,使得越来越多的模型出现,但是...
DHGE: Dual-view Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing DHGE:用于链接预测和实体分类的双视图超关系知识图嵌入 In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute ...
A common approach in link prediction and knowledge graph completion is viaembedding into vector spacesto learn representations of entities and relations and embedding vectors of entities and relations can then be updated bymaximizing the global plausibility (最大化全局合理性). Embedding methods...
More specifically, the task of finding new causal relations in an incomplete causal network is mapped to the task of knowledge graph link prediction. The use of knowledge graphs to represent causal relations enables the integration of external domain knowledge; and as an added complexity, the ...