標題: Titlebook: Embedding Knowledge Graphs with RDF2vec; Heiko Paulheim,Petar Ristoski,Jan Portisch Book 2023 The Editor(s) (if applicable) and The Author [打印本頁] 作者: 螺絲刀 時間: 2025-3-21 18:31
書目名稱Embedding Knowledge Graphs with RDF2vec影響因子(影響力)
書目名稱Embedding Knowledge Graphs with RDF2vec影響因子(影響力)學(xué)科排名
書目名稱Embedding Knowledge Graphs with RDF2vec網(wǎng)絡(luò)公開度
書目名稱Embedding Knowledge Graphs with RDF2vec網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Embedding Knowledge Graphs with RDF2vec被引頻次
書目名稱Embedding Knowledge Graphs with RDF2vec被引頻次學(xué)科排名
書目名稱Embedding Knowledge Graphs with RDF2vec年度引用
書目名稱Embedding Knowledge Graphs with RDF2vec年度引用學(xué)科排名
書目名稱Embedding Knowledge Graphs with RDF2vec讀者反饋
書目名稱Embedding Knowledge Graphs with RDF2vec讀者反饋學(xué)科排名
作者: mucous-membrane 時間: 2025-3-21 23:47
From Word Embeddings to Knowledge Graph Embeddings,n 2013 – and embeddings have gathered a tremendous uptake in the natural language processing community since then. Since RDF2vec is based on word2vec, we take a closer look at word2vec in this chapter. We explain how word2vec has been developed to represent words as vectors, and we discuss how this 作者: Nibble 時間: 2025-3-22 04:26
Benchmarking Knowledge Graph Embeddings,er introduces a few datasets and three common benchmarks for embedding methods—i.e., SW4ML, GEval, and DLCC—and shows how to use them for comparing different variants of RDF2vec. The novel DLCC benchmark allows us to take a closer look at what RDF2vec vectors actually represent, and to analyze what 作者: 樂章 時間: 2025-3-22 08:09
Tweaking RDF2vec,weaks encompass various steps of the pipeline: reasoners have been used to preprocess the knowledge graph and add implicit knowledge. Different strategies for changing the walk strategy have been proposed, starting from injecting edge weights to biasing the walks towards higher or lower degree nodes作者: maroon 時間: 2025-3-22 08:53 作者: 殺菌劑 時間: 2025-3-22 15:46
Link Prediction in Knowledge Graphs (and its Relation to RDF2vec),ification, which we have considered so far). In this chapter, we give a very brief overview of the main embedding techniques for link prediction and flesh out the main differences between the well-known link prediction technique TransE and RDF2vec. Moreover, we show how RDF2vec can be used for link 作者: 殺菌劑 時間: 2025-3-22 17:58 作者: otic-capsule 時間: 2025-3-22 23:34 作者: Reservation 時間: 2025-3-23 02:23
Sushil Kumar,Priyanka,Upendra Kumaring tasks, and we show classic feature extraction or propositionalization techniques, which are the historical predecessor of knowledge graph embeddings, and we show how these techniques are used for basic node classification tasks.作者: 強有力 時間: 2025-3-23 05:59 作者: 等待 時間: 2025-3-23 10:02 作者: 鑲嵌細工 時間: 2025-3-23 16:43 作者: freight 時間: 2025-3-23 20:30 作者: Amorous 時間: 2025-3-24 00:08
Talking About Talking Microbes,ification, which we have considered so far). In this chapter, we give a very brief overview of the main embedding techniques for link prediction and flesh out the main differences between the well-known link prediction technique TransE and RDF2vec. Moreover, we show how RDF2vec can be used for link 作者: 就職 時間: 2025-3-24 03:43 作者: 供過于求 時間: 2025-3-24 07:42
https://doi.org/10.1007/978-3-030-16190-3re are the handling of literal values (which are currently not used by RDF2vec), the handling of dynamic knowledge graphs, and the generation of are explanations for systems using RDF2vec (which are currently black box models).作者: Melatonin 時間: 2025-3-24 13:25
Heiko Paulheim,Petar Ristoski,Jan PortischExplains what are knowledge graph embeddings are and how they can be computed.Demonstrates how RDF2vec is used as a building block in AI applications.Discusses which variants of RDF2vec exist and when作者: Offbeat 時間: 2025-3-24 17:34
Synthesis Lectures on Data, Semantics, and Knowledgehttp://image.papertrans.cn/e/image/307983.jpg作者: malign 時間: 2025-3-24 22:59 作者: tenuous 時間: 2025-3-25 02:44
Poornima Singh,Mohit Sharma,Rashmi Rawater introduces a few datasets and three common benchmarks for embedding methods—i.e., SW4ML, GEval, and DLCC—and shows how to use them for comparing different variants of RDF2vec. The novel DLCC benchmark allows us to take a closer look at what RDF2vec vectors actually represent, and to analyze what proximity in the vector space means for them.作者: beta-cells 時間: 2025-3-25 05:09
Ultrastructure of bacterial envelopes,with large knowledge graphs. First, we look at a knowledge graph embedding server called KGvec2go, which serves pre-trained embedding vectors for well-known knowledge graphs such as DBpedia as a service. Second, we look at how we can train partial RDF2vec models only for instances of interest with RDF2vec Light.作者: 推延 時間: 2025-3-25 10:38
Talking About Talking Microbes,ification, which we have considered so far). In this chapter, we give a very brief overview of the main embedding techniques for link prediction and flesh out the main differences between the well-known link prediction technique TransE and RDF2vec. Moreover, we show how RDF2vec can be used for link prediction.作者: mechanical 時間: 2025-3-25 12:31
Ann C. Kennedy,Virginia L. Gewinrget (with a few exceptions), RDF2vec is more versatile. In this chapter, we show examples of works that describe the use of RDF2vec for other purposes, such as recommender systems, relation extraction, ontology learning, or knowledge graph matching.作者: 細頸瓶 時間: 2025-3-25 16:33 作者: 高深莫測 時間: 2025-3-25 20:27 作者: 震驚 時間: 2025-3-26 02:40 作者: 十字架 時間: 2025-3-26 07:57 作者: Strength 時間: 2025-3-26 09:02 作者: 合唱團 時間: 2025-3-26 15:56
Example Applications Beyond Node Classification,rget (with a few exceptions), RDF2vec is more versatile. In this chapter, we show examples of works that describe the use of RDF2vec for other purposes, such as recommender systems, relation extraction, ontology learning, or knowledge graph matching.作者: reserve 時間: 2025-3-26 19:16
Future Directions for RDF2vec,re are the handling of literal values (which are currently not used by RDF2vec), the handling of dynamic knowledge graphs, and the generation of are explanations for systems using RDF2vec (which are currently black box models).作者: inchoate 時間: 2025-3-26 23:18
https://doi.org/10.1007/978-3-031-30387-6Data mining; knowledge representation in AI; Knowledge Graph Embeddings; dynamic knowledge graphs; ontol作者: Incisor 時間: 2025-3-27 04:15 作者: 細微的差異 時間: 2025-3-27 09:00 作者: ESPY 時間: 2025-3-27 11:19
2691-2023 lications.Discusses which variants of RDF2vec exist and when.This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reus作者: 帽子 時間: 2025-3-27 16:30
Book 2023red vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode..作者: Misgiving 時間: 2025-3-27 20:13 作者: champaign 時間: 2025-3-28 01:34 作者: 小丑 時間: 2025-3-28 05:15
From Word Embeddings to Knowledge Graph Embeddings,approach can be adapted to knowledge graphs by performing random graph walks, yielding the basic version of RDF2vec. We explain the CBOW and SkipGram variants of basic RDF2vec, revisiting the node classification tasks used in Chap.?..
