Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs

Steenwinckel, Bram; Vandewiele, Gilles; Bonte, Pieter; Weyns, Michael; Paulheim, Heiko; Ristoski, Petar; Turck, Filip De; Ongenae, Femke

doi:10.1007/978-3-030-87101-7_8

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…In future work, we will implement new parameters to allow these walks. Furthermore, we will incorporate other walk flavors [24], [25].…”

Section: Parameter Analysis and Discussionmentioning

confidence: 99%

“…Additionally, they proposed similarity-oriented and relevance-oriented walk methods and identified the advantages of each method [24]. Steenwinckel et al [25] showed that the WL graph kernel offers little improvements in the context of a single KG with respect to walk embeddings, and proposed five alternative walking strategies.…”

Section: Related Work a Knowledge Graph Embeddings For Rdfmentioning

confidence: 99%

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RDF-star2Vec: RDF-star Graph Embeddings for Data Mining

Egami,

Ugai,

Oota

et al. 2023

IEEE Access

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Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (). Knowledge graph embedding (KGE) is crucial in machine learning applications, specifically in node classification and link prediction tasks. KGE remains a vital research topic within the semantic web community. RDF-star introduces the concept of a quoted triple (QT), a specific form of triple employed either as the subject or object within another triple. Moreover, RDF-star permits a QT to act as compositional entities within another QT, thereby enabling the representation of recursive, hyper-relational KGs with nested structures. However, existing KGE models fail to adequately learn the semantics of QTs and entities, primarily because they do not account for RDF-star graphs containing multi-leveled nested QTs and QT-QT relationships. This study introduces RDF-star2Vec, a novel KGE model specifically designed for RDF-star graphs. RDF-star2Vec introduces graph walk techniques that enable probabilistic transitions between a QT and its compositional entities. Feature vectors for QTs, entities, and relations are derived from generated sequences through the structured skip-gram model. Additionally, we provide a dataset and a benchmarking framework for data mining tasks focused on complex RDF-star graphs. Evaluative experiments demonstrated that RDF-star2Vec yielded superior performance compared to recent extensions of RDF2Vec in various tasks including classification, clustering, entity relatedness, and QT similarity.

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“…In future work, we will implement new parameters to allow these walks. Furthermore, we will incorporate other walk flavors [24], [25].…”

Section: Parameter Analysis and Discussionmentioning

confidence: 99%

Section: Related Work a Knowledge Graph Embeddings For Rdfmentioning

confidence: 99%

RDF-star2Vec: RDF-star Graph Embeddings for Data Mining

Egami,

Ugai,

Oota

et al. 2023

IEEE Access

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“…In our approach, we choose the RDF2vec [9,10], an unsupervised technique for feature extraction (embeddings), for nodes as well for edges, that has been proposed in several domains (e.g., Semantic Web, Bioinformatics, Social Net-works) and for various purposes (e.g., Semantic Similarity, Link Prediction, Recommendation Systems, Entity Classification) for RDF graphs. Embeddings aim to encode the semantic information mainly for entities; however, in our case, RDF2Vec is applied for properties too.…”

Section: Weight Assignment and Node Selectionmentioning

confidence: 99%

“…• We use RDF2Vec [9,10], a prominent approach for efficiently generating embeddings for the nodes and edges of a KG. We explore several walking strategies for generating embeddings (Random, Anonymous, Walklet, HALK, N-Grams) to identify the optimal one for our problem.…”

Section: Introductionmentioning

confidence: 99%

Constructing Semantic Summaries Using Embeddings

Trouli,

Papadakis,

Kondylakis

2024

Information

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The increase in the size and complexity of large knowledge graphs now available online has resulted in the emergence of many approaches focusing on enabling the quick exploration of the content of those data sources. Structural non-quotient semantic summaries have been proposed in this direction that involve first selecting the most important nodes and then linking them, trying to extract the most useful subgraph out of the original graph. However, the current state of the art systems use costly centrality measures for identifying the most important nodes, whereas even costlier procedures have been devised for linking the selected nodes. In this paper, we address both those deficiencies by first exploiting embeddings for node selection, and then by meticulously selecting approximate algorithms for node linking. Experiments performed over two real-world big KGs demonstrate that the summaries constructed using our method enjoy better quality. Specifically, the coverage scores obtained were 0.8, 0.81, and 0.81 for DBpedia v3.9 and 0.94 for Wikidata dump 2018, across 20%, 25%, and 30% summary sizes, respectively. Additionally, our method can compute orders of magnitude faster than the state of the art.

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“…While alternatives to random walks have been proposed as well, the result with those are not yet very conclusive [6,27], which is why we decided to stick to random walks as a sequence extraction technique. In particular, we compare two flavors of random walks:…”

Section: Training Rdf2vec Embedding Vectorsmentioning

confidence: 99%

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

Heilig¹,

Kirchhoff²,

Stumpe³

et al. 2022

Preprint

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Medical diagnosis is the process of making a prediction of the disease a patient is likely to have, given a set of symptoms and observations. This requires extensive expert knowledge, in particular when covering a large variety of diseases. Such knowledge can be coded in a knowledge graph -encompassing diseases, symptoms, and diagnosis paths. Since both the knowledge itself and its encoding can be incomplete, refining the knowledge graph with additional information helps physicians making better predictions. At the same time, for deployment in a hospital, the diagnosis must be explainable and transparent. In this paper, we present an approach using diagnosis paths in a medical knowledge graph. We show that those graphs can be refined using latent representations with RDF2vec, while the final diagnosis is still made in an explainable way. Using both an intrinsic as well as an expert-based evaluation, we show that the embedding-based prediction approach is beneficial for refining the graph with additional valid conditions.

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Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs

Cited by 11 publications

References 20 publications

RDF-star2Vec: RDF-star Graph Embeddings for Data Mining

RDF-star2Vec: RDF-star Graph Embeddings for Data Mining

Constructing Semantic Summaries Using Embeddings

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

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