论文标题
这样走！实体步行和财产步行RDF2VEC
Walk this Way! Entity Walks and Property Walks for RDF2vec
论文作者
论文摘要
RDF2VEC是一种知识图嵌入机制，它首先通过执行随机步行来从知识图中提取序列，然后将这些序列馈入嵌入算法Word2Vec的单词，以计算实体的向量表示。在这张海报中，我们介绍了两个新的步行提取式电子步行和P-walks的新口味，这些口味分别重点放在实体的结构或邻里，从而允许创建以相似性或相关性的形式创建嵌入。通过将步行策略与订单感知和经典的RDF2VEC以及CBOW和SKIP-GROW WORD2VEC嵌入结合在一起，我们进行了初步评估，共有12个RDF2VEC变体。
RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from knowledge graphs by performing random walks, then feeds those into the word embedding algorithm word2vec for computing vector representations for entities. In this poster, we introduce two new flavors of walk extraction coined e-walks and p-walks, which put an emphasis on the structure or the neighborhood of an entity respectively, and thereby allow for creating embeddings which focus on similarity or relatedness. By combining the walk strategies with order-aware and classic RDF2vec, as well as CBOW and skip-gram word2vec embeddings, we conduct a preliminary evaluation with a total of 12 RDF2vec variants.
