Link Prediction in Knowledge Graphs with Concepts of Nearest Neighbours

Ferré, Sébastien

doi:10.1007/978-3-030-21348-0_6

Cited by 7 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, we can find a number of fact checking proposals that produce models to classify a triple as correct or incorrect, following approaches such as rule-based reasoning [10,14,19,23], neural tensor networks [26], embedded spaces [7,25,27], tensor factorization [22], concepts of nearest neighbors [9] and random walks [11,18]. However, when it comes to candidate filtering, all current approaches are encompassed within a specific fact checking technique, and they are very basic.…”

Section: Missing Factsmentioning

confidence: 99%

Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques

Borrego

Ayala

Hernández

et al. 2019

Proceedings of the 10th International Conference on Knowledge Capture

View full text Add to dashboard Cite

Knowledge Graphs (KGs) contain large amounts of structured information. Due to their inherent incompleteness, a process known as KG completion is often carried out to find the missing triples in a KG, usually by training a fact checking model that is able to discern between correct and incorrect knowledge. After the fact checking model has been trained and evaluated, it has to be applied to a set of candidate triples, and those that are considered correct are added to the KG as new knowledge. However, this process needs a set of candidate triples of a reasonable size that represents possible new knowledge, in order to be evaluated by the fact checking task and, if considered to be correct, added to the KG, enriching it. Current approaches for selecting candidate triples for their correctness checking either use the full set possible missing candidate triples (and thus provide no filtering) or apply very basic rules to filter out unlikely candidates, which may have a negative effect on the completion performance as very few candidate triples are filtered out. In this paper we present CHAI, a method for producing more complex rules that are able to filter candidate triples by combining a set of criteria to optimize a fitness function. Our experiments show that CHAI is able to generate rules that, when applied, yield smaller candidate sets than similar proposals while still including promising candidate triples. •

show abstract

Section: Missing Factsmentioning

confidence: 99%

Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques

Borrego

Ayala

Hernández

et al. 2019

Proceedings of the 10th International Conference on Knowledge Capture

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Specifically, they are applied to various systems, such as question answering (QA) systems, recommendation systems, and knowledge inference systems. Knowledge graphs can be used to combine and manage knowledge data from various sources [5]. Furthermore, knowledge graphs provide a method for reconstructing new knowledge through link predictions using inference for entity-relation.…”

Section: Introductionmentioning

confidence: 99%

Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary

Choi¹

2022

Sustainability

View full text Add to dashboard Cite

Knowledge bases built in the knowledge processing field have a problem in that experts have to add rules or update them through modifications. To solve this problem, research has been conducted on knowledge graph expansion methods using deep learning technology, and in recent years, many studies have been conducted on methods of generating knowledge bases by embedding the knowledge graph’s triple information in a continuous vector space. In this paper, using a research literature summary, we propose a domain-specific knowledge graph expansion method based on graph embedding. To this end, we perform pre-processing and process and text summarization with the collected research literature data. Furthermore, we propose a method of generating a knowledge graph by extracting the entity and relation information and a method of expanding the knowledge graph using web data. To this end, we summarize research literature using the Bidirectional Encoder Representations from Transformers for Summarization (BERTSUM) model based on domain-specific research literature data and design a Research-BERT (RE-BERT) model that extracts entities and relation information, which are components of the knowledge graph, from the summarized research literature. Moreover, we proposed a method of expanding related entities based on Google news after extracting related entities through the web for the entities in the generated knowledge graph. In the experiment, we measured the performance of summarizing research literature using the BERTSUM model and the accuracy of the knowledge graph relation extraction model. In the experiment of removing unnecessary sentences from the research literature text and summarizing them in key sentences, the result shows that the BERTSUM Classifier model’s ROUGE-1 precision is 57.86%. The knowledge graph extraction performance was measured using the mean reciprocal rank (MRR), mean rank (MR), and HIT@N rank-based evaluation metric. The knowledge graph extraction method using summarized text showed superior performance in terms of speed and knowledge graph quality.

show abstract

“…In this paper, we report on extensive experimental results about a novel approach to knowledge graph completion that is based on Concepts of Nearest Neighbours (C-NN), which were introduced in [7], and applied to query relaxation in [8]. This paper is an extended version of [9] that reports on first results about the application of C-NNs to link prediction. In particular, the extension includes an analogical form of inference, and an extensive and deeper experimental evaluation.…”

Section: Introductionmentioning

confidence: 99%

Application of concepts of neighbours to knowledge graph completion1

Ferré

2021

Self Cite

View full text Add to dashboard Cite

The open nature of Knowledge Graphs (KG) often implies that they are incomplete. Knowledge graph completion (a.k.a. link prediction) consists in inferring new relationships between the entities of a KG based on existing relationships. Most existing approaches rely on the learning of latent feature vectors for the encoding of entities and relations. In general however, latent features cannot be easily interpreted. Rule-based approaches offer interpretability but a distinct ruleset must be learned for each relation. In both latent- and rule-based approaches, the training phase has to be run again when the KG is updated. We propose a new approach that does not need a training phase, and that can provide interpretable explanations for each inference. It relies on the computation of Concepts of Nearest Neighbours (C-NN) to identify clusters of similar entities based on common graph patterns. Different rules are then derived from those graph patterns, and combined to predict new relationships. We evaluate our approach on standard benchmarks for link prediction, where it gets competitive performance compared to existing approaches.

show abstract

Link Prediction in Knowledge Graphs with Concepts of Nearest Neighbours

Cited by 7 publications

References 14 publications

Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques

Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques

Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary

Application of concepts of neighbours to knowledge graph completion1

Contact Info

Product

Resources

About