Knowledge Graph Embedding: A Survey of Approaches and Applications

Wang, Quan; Mao, Zhendong; Wang, Bin; Guo, Li

doi:10.1109/tkde.2017.2754499

Cited by 1,886 publications

(1,095 citation statements)

References 74 publications

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“…Our novel embedding approach is inspired by the OWL2Vec (Holter et al, 2019) as well as the Walking RDF & OWL (Alshahrani et al, 2017) methods which first convert ontologies into a graph based on syntactic patterns within the ontology axioms, and then apply a knowledge graph embedding (Wang et al, 2017b) on the resulting graph. However, our method extends OWL2Vec and Walking RDF & OWL to incorporate more complex forms of axioms into the generated graph so that the complexity of the axioms in a cross-species phenotype ontology such as PhenomeNET (Rodríguez-García et al, 2017) can be utilized.…”

Section: Embedding Graph-based Representations Of Ontologiesmentioning

confidence: 99%

Predicting candidate genes from phenotypes, functions, and anatomical site of expression

Chen

Althagafi

Hoehndorf

2020

Preprint

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Motivation: Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine learning models. Results: We developed a novel graph-based machine learning method for biomedical ontologies which is able to exploit axioms in ontologies and other graph-structured data. Using our machine learning method, we embed genes based on their associated phenotypes, functions of the gene products, and anatomical location of gene expression. We then develop a machine learning model to predict gene-disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state of the art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes which are associated with phenotypes, functions, or site of expression. Availability: Software and data are available at https://github. com/bio-ontology-research-group/DL2Vec.

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Section: Embedding Graph-based Representations Of Ontologiesmentioning

confidence: 99%

Predicting candidate genes from phenotypes, functions, and anatomical site of expression

Chen

Althagafi

Hoehndorf

2020

Preprint

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“…A knowledge graph is a structured knowledge base consisting of the triple "entity-property-entity," which is used to describe entities, concepts, and the relationships between them in the physical world [24]. The superiority of the knowledge graph is that it can implement concept retrieval through inference and provide users with structured knowledge directly, while the traditional search engine is based on string matching the return links of webpages.…”

Section: Knowledge Graphs In Scientific Entities Managementmentioning

confidence: 99%

Knowledge Graph in Smart Education: A Case Study of Entrepreneurship Scientific Publication Management

et al. 2018

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Abstract:In recent years, with the rapid growth of science and innovation, plenty of constantly-updated scientific achievements containing innovative knowledge can be acquired and used to solve problems. However, most undergraduate students and non-researchers cannot use them efficiently. In traditional teacher-centric education, education for sustainability is often marginalized and the interdisciplinary demand is neglected. Additionally, it fails to provide education for learners to connect abstract knowledge with actual world problems. This paper presents the design of a scientific publication management model to integrate scientific metadata based on the knowledge graph and data analysis technologies. Based on this model, an interdisciplinary transregional multiple application platform could be realized for scientific resource retrieval and analysis, the purpose of which is to enhance scientific retrieval efficiency and reduce learning difficulty in the scientific domains and encourage non-researchers to utilize scientific resources in their study and work. Finally, to evaluate this model, the use of the case of an entrepreneurship scientific publication management prototype system was implemented. This work not only favors student's learning for sustainability through analysis and knowledge management functions, but also promotes their awareness, comprehensive thinking, and the skills to deal with the issues of sustainability in their future work.

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“…In the recent years, knowledge graph embedding models, i.e., projections of knowledge graphs into lower-dimensional, dense vector spaces, have received a lot of attention [34]. Several different models have been developed for the knowledge graph completion problem and have brought improvements in performance.…”

Section: Related Workmentioning

confidence: 99%

Detection of Relation Assertion Errors in Knowledge Graphs

Melo

Paulheim

2017

Proceedings of the Knowledge Capture Conference

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Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention. In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error detection method which relies on path and type features used by a classifier for every relation in the graph exploiting local feature selection. We perform an extensive evaluation on a variety of datasets, backed by a manual evaluation on DBpedia and NELL, and we propose and evaluate heuristics for the selection of relevant graph paths to be used as features in our method.

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Knowledge Graph Embedding: A Survey of Approaches and Applications

Cited by 1,886 publications

References 74 publications

Predicting candidate genes from phenotypes, functions, and anatomical site of expression

Predicting candidate genes from phenotypes, functions, and anatomical site of expression

Knowledge Graph in Smart Education: A Case Study of Entrepreneurship Scientific Publication Management

Detection of Relation Assertion Errors in Knowledge Graphs

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