Augmenting Ontology Alignment by Semantic Embedding and Distant Supervision

Chen, Jiaoyan; Jiménez-Ruiz, Ernesto; Horrocks, Ian; Antonyrajah, Denvar; Hadian, Ali; Lee, Jun Ho

doi:10.1007/978-3-030-77385-4_23

Cited by 26 publications

(24 citation statements)

References 20 publications

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“…This will be further studied in the future. Meanwhile, we plan to expand PRASE with other reasoning-based systems (e.g., LogMap) and enhance the interaction between the PR and SE modules by, e.g., injecting prior knowledge defined by the KGs' ontologies [Chen et al, 2021]. We will also utilize the alignment of KGs to address KG refinement problems such as error detection and correction .…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

Zhang

Chen³

et al. 2021

Preprint

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Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embedding-based models and the conventional reasoning and lexical matching based systems. The former compute the similarity of entities via their cross-KG embeddings, but they usually rely on an ideal supervised learning setting for good performance and lack appropriate reasoning to avoid logically wrong mappings; while the latter address the reasoning issue but are poor at utilizing the KG graph structures and the entity contexts. In this study, we aim at combining the above two solutions and thus propose an iterative framework named PRASE that is based on probabilistic reasoning and semantic embedding. It learns the KG embeddings via entity mappings from a probabilistic reasoning system named PARIS, and feeds the resultant entity mappings and embeddings back into PARIS for augmentation. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

Zhang

Chen³

et al. 2021

Preprint

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“…The subgraph of TERA used for prediction is available alongside the chemical effect prediction models in our GitHub repository. 14 of maximum ∼ 0.6%. The resulting endpoint from an experiment is categorised in one of a plethora of predefined endpoints (see Table 2 above).…”

Section: Tera Knowledge Graphmentioning

confidence: 95%

“…Each result is composed by Table 5 Example triples from the TERA knowledge graph. For space reasons, we have added the full id or label for some of the entities using footnote marks where 1 inchikey:MMOXZBCLCQITDF-UHFFFAOYSA-N, 2 Pimephales, 3 Cyprinidae, 4 Headwater, 5 Benzamides, 6 Insect Repellents, 7 CHRNA3, 8 CHRNB4, 9 DETA-20, 10 DETA Epichlorohydrin, 11 Has component, 12 Triclocarban, 13 Trichlorocarbanilide-containing product, 14 Similar to, 15 3-Chloromethyl-N,N-diethylbenzamide. an endpoint, an effect, and a concentration (with a unit) at which the endpoint and effect are recorded.…”

Section: Effects Sub-kg Constructionmentioning

confidence: 99%

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Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings

Myklebust,

Jiménez-Ruiz,

Chen

et al. 2021

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We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.

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“…Recall that our approach is supervised since gold clusters are computed from preexisting alignments. Hence, testing our approach on different knowledge graphs of the LOD would require such preexisting alignments or using ontology alignment systems in a distant supervision process [8]. In this setting, merging the different graphs into one and learning a "global" embedding, as we did, may provide positive results but may pose additional scalability issues.…”

Section: Generalization To Other Knowledge Graphsmentioning

confidence: 99%

Discovering alignment relations with Graph Convolutional Networks: A biomedical case study

Monnin

Raïssi

Napoli

et al. 2022

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Knowledge graphs are freely aggregated, published, and edited in the Web of data, and thus may overlap. Hence, a key task resides in aligning (or matching) their content. This task encompasses the identification, within an aggregated knowledge graph, of nodes that are equivalent, more specific, or weakly related. In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster. We conducted experiments with this approach on the real world application of aligning knowledge in the field of pharmacogenomics, which motivated our study. We particularly investigated the interplay between domain knowledge and GCN models with the two following focuses. First, we applied inference rules associated with domain knowledge, independently or combined, before learning node embeddings, and we measured the improvements in matching results. Second, while our GCN model is agnostic to the exact alignment relations (e.g., equivalence, weak similarity), we observed that distances in the embedding space are coherent with the “strength” of these different relations (e.g., smaller distances for equivalences), letting us considering clustering and distances in the embedding space as a means to suggest alignment relations in our case study.

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Augmenting Ontology Alignment by Semantic Embedding and Distant Supervision

Cited by 26 publications

References 20 publications

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings

Discovering alignment relations with Graph Convolutional Networks: A biomedical case study

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