Negative sampling and rule mining for explainable link prediction in knowledge graphs

Islam, Kamrul; Aridhi, Sabeur; Smaïl-Tabbone, Malika

doi:10.1016/j.knosys.2022.109083

Cited by 8 publications

(7 citation statements)

References 25 publications

(45 reference statements)

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“…Although the literature often presents RNS as an ineffective sampling strategy for the generic link prediction task [8,23], we have shown it can be successfully used in a recommendation framework when combined with a specialization training procedure. In particular, S-TCNS sometimes improves Hits@K at the expense of the model understanding of the semantic profile of the target relation.…”

Section: Discussionmentioning

confidence: 98%

“…Resulting triples are called negative samples and they constitute the basis on which embedding learning is performed: embedding models iteratively learn to assign higher ranks to true triples than to negative ones. Hence, the way these models learn is significantly influenced by negative sampling methods, which therefore received much attention recently [7,8,12].…”

Section: Introductionmentioning

confidence: 99%

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New Strategies for Learning Knowledge Graph Embeddings: The Recommendation Case

Hubert

Monnin

Brun

et al. 2022

Lecture Notes in Computer Science

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Knowledge graph embedding models encode elements of a graph into a low-dimensional space that supports several downstream tasks. This work is concerned with the recommendation task, which we approach as a link prediction task on a single target relation performed in the embedding space. Training an embedding model requires negative sampling, which consists in corrupting the head or the tail of positive triples to generate negative ones. Although knowledge graph embedding models and negative sampling have extensively been investigated for link prediction, their combined use for performing recommendations over knowledge graphs remains largely unexplored in the literature. In this work, we propose two specialization strategies for training embedding models and performing knowledge graph-based recommendations. Both strategies first train an embedding model on the whole knowledge graph. Then, during a specialization phase, a dedicated negative sampling scheme is applied to refine the pre-trained model. Experimental results on two public datasets demonstrate that a simple strategy which refines a pre-trained model by sampling random negative tails for the target relation proves to be very effective. This strategy significantly improves performance with respect to traditional rank-based evaluation metrics as well as a newly introduced metric that reflects the semantic validity of the top-ranked candidate entities.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

New Strategies for Learning Knowledge Graph Embeddings: The Recommendation Case

Hubert

Monnin

Brun

et al. 2022

Lecture Notes in Computer Science

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“… Here, is the hinge function. We refer to Islam et al 32 for the architecture of a classical KG embedding method with the SNS negative sampling method.…”

Section: Methodsmentioning

confidence: 99%

“…Using our neuro-symbolic method 32 , we mine a set of rules from DRKG and use them for generating plausible explanation(s) for the predictions. A rule consists of a rule Body and a rule Head in the following form: where are entity variables, are relations from DRKG.…”

Section: Methodsmentioning

confidence: 99%

“…The top-100 predictions are evaluated based on two groups of methods: (1) cross-matching with in-trial drugs for COVID-19 and (2) molecular evaluation based on compound and protein structures. Beside these evaluations, we learn high quality rules from DRKG and provide possible explanations of predictions 32 .

Figure 1 Overall study workflow; the major steps are numbered.…”

Section: Introductionmentioning

confidence: 99%

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Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding

Islam

Amaya-Ramirez

Maigret

et al. 2023

Sci Rep

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The search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns “ensemble embeddings” of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.

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