Fine-grained Type Inference in Knowledge Graphs via Probabilistic and Tensor Factorization Methods

Moniruzzaman, A B M; Nayak, Richi; Tang, Maolin; Balasubramaniam, Thirunavukarasu

doi:10.1145/3308558.3313597

Cited by 9 publications

(2 citation statements)

References 41 publications

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“…In another example with text processing methods, combining logical and text-based evidence was performed by Du et al [43]. Along with the methods mentioned for the verification process, other approaches deal with data mining, and natural language processing [44].…”

Section: B Open-world Assumption Methodsmentioning

confidence: 99%

Continuous Knowledge Graph Refinement With Confidence Propagation

Huseynli

Akçayol

2023

IEEE Access

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Although Knowledge Graphs (KGs) are widely used, they suffer from hosting false information. In the literature, many studies have been carried out to eliminate this deficiency. These studies correct triples, relations, relation types, and literal values or enrich the KG by generating new triples and relations. The proposed methods can be grouped as closed-world approaches that take into account the KG itself or open-world approaches using external resources. The recent studies also considered the confidence of triples in the refinement process. The confidence values calculated in these studies affect either the triple itself or the ground rule for rule-based models. In this study, a propagation approach based on the confidence of triples has been proposed for the refinement process. This method ensures that the effect of confidence spreads over the KG without being limited to a single triple. This makes the KG continuously more stable by strengthening strong relationships and eliminating weak ones. Another limitation of the existing studies is that they handle refinement as a one-time operation and do not give due importance to process performance. However, realworld KGs are live, dynamic, and constantly evolving systems. Therefore, the proposed approach should support continuous refinement. To measure this, experiments were carried out with varying data sizes and rates of false triples. The experiments have been performed using the FB15K, NELL, WN18, and YAGO3-10 datasets, which are commonly used in refinement studies. Despite the increase in data size and false information rate, an average accuracy of 90% and an average precision of 98% have been achieved across all datasets.

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Section: B Open-world Assumption Methodsmentioning

confidence: 99%

Continuous Knowledge Graph Refinement With Confidence Propagation

Huseynli

Akçayol

2023

IEEE Access

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“…Second, the paper is the first to investigate the role of Wikidata in automatically enriching Wikipedia categories. In contrast, previous methods for open-domain information extraction mostly rely on unstructured or semi-structured text (Sun et al, 2018), Wikipedia (Tsurel et al, 2017) and repositories other than Wikidata (Hoffart et al, 2013;Qu et al, 2018;Moniruzzaman et al, 2019) with few exceptions (Chisholm et al, 2017). Third, in experiments over the gold set of Wikipedia categories, in comparison to a previous method (Paşca, 2017), the proposed method automatically annotates constituent modifiers as semantically-anchored properties and topics, rather than mere strings.…”

Section: Introductionmentioning

confidence: 99%

Interpreting Open-Domain Modifiers: Decomposition of Wikipedia Categories into Disambiguated Property-Value Pairs

Paşca

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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This paper proposes an open-domain method for automatically annotating modifier constituents ("20th-century") within Wikipedia categories ("20th-century male writers") with properties ("date of birth"). The annotations offer a semantically-anchored understanding of the role of the constituents in defining the underlying meaning of the categories. In experiments over an evaluation set of Wikipedia categories, the proposed method annotates constituent modifiers as semanticallyanchored properties, rather than as mere strings in a previous method. It does so at a better trade-off between precision and recall.

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Modelling and Factorizing Large-Scale Knowledge Graph (DBPedia) for Fine-Grained Entity Type Inference

Moniruzzaman

2021

Lecture Notes in Computer Science

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Fine-grained Type Inference in Knowledge Graphs via Probabilistic and Tensor Factorization Methods

Cited by 9 publications

References 41 publications

Continuous Knowledge Graph Refinement With Confidence Propagation

Continuous Knowledge Graph Refinement With Confidence Propagation

Interpreting Open-Domain Modifiers: Decomposition of Wikipedia Categories into Disambiguated Property-Value Pairs

Modelling and Factorizing Large-Scale Knowledge Graph (DBPedia) for Fine-Grained Entity Type Inference

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