Ensemble-Based Fact Classification with Knowledge Graph Embeddings

Joshi, Unmesh; Urbani, Jacopo

doi:10.1007/978-3-031-06981-9_9

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…Embeddings are used in various downstream tasks, such as disambiguation [80], ontology learning [71], handling concept drift in streams [13], question answering [54] or fact classification [43]. Over the years multiple survey papers were published on the topic of KB embeddings [10,14,16,91,92].…”

Section: Knowledge Base Embeddingsmentioning

confidence: 99%

Reason-able embeddings: Learning concept embeddings with a transferable neural reasoner

Adamski

Potoniec

2024

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We present a novel approach for learning embeddings of ALC knowledge base concepts. The embeddings reflect the semantics of the concepts in such a way that it is possible to compute an embedding of a complex concept from the embeddings of its parts by using appropriate neural constructors. Embeddings for different knowledge bases are vectors in a shared vector space, shaped in such a way that approximate subsumption checking for arbitrarily complex concepts can be done by the same neural network, called a reasoner head, for all the knowledge bases. To underline this unique property of enabling reasoning directly on embeddings, we call them reason-able embeddings. We report the results of experimental evaluation showing that the difference in reasoning performance between training a separate reasoner head for each ontology and using a shared reasoner head, is negligible.

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Section: Knowledge Base Embeddingsmentioning

confidence: 99%

Reason-able embeddings: Learning concept embeddings with a transferable neural reasoner

Adamski

Potoniec

2024

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“…DuEL [18] exploits embedding models for classifying facts to be either true or false, rather than ranking them. Starting from a tail query (ℎ, 𝑟, ?…”

Section: A3 Related Work: Combination Between Machine Learning Models...mentioning

confidence: 99%

Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models

Gregucci

Nayyeri

Hernández

et al. 2023

Proceedings of the ACM Web Conference 2023

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Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scoring them according to geometric criteria. Relations in the graph may follow patterns that can be learned, e.g., some relations might be symmetric and others might be hierarchical. However, the learning capability of different embedding models varies for each pattern and, so far, no single model can learn all patterns equally well. In this paper, we combine the query representations from several models in a unified one to incorporate patterns that are independently captured by each model. Our combination uses attention to select the most suitable model to answer each query. The models are also mapped onto a non-Euclidean manifold, the Poincaré ball, to capture structural patterns, such as hierarchies, besides relational patterns, such as symmetry. We prove that our combination provides a higher expressiveness and inference power than each model on its own. As a result, the combined model can learn relational and structural patterns. We conduct extensive experimental analysis with various link prediction benchmarks showing that the combined model outperforms individual models, including state-of-the-art approaches.

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“…Both Ciampaglia et al [9] and Shiralkar et al [50] use entity paths in DBpedia to verify triples extracted from text. Other approaches based on KG embeddings associate the likelihood of a claim being true to that of it belonging as a new triple to a KG [4,19].…”

Section: Afc On Kgsmentioning

confidence: 99%

ProVe: A pipeline for automated provenance verification of knowledge graphs against textual sources

Amaral,

Rodrigues,

Simperl

2023

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Knowledge Graphs are repositories of information that gather data from a multitude of domains and sources in the form of semantic triples, serving as a source of structured data for various crucial applications in the modern web landscape, from Wikipedia infoboxes to search engines. Such graphs mainly serve as secondary sources of information and depend on well-documented and verifiable provenance to ensure their trustworthiness and usability. However, their ability to systematically assess and assure the quality of this provenance, most crucially whether it properly supports the graph’s information, relies mainly on manual processes that do not scale with size. ProVe aims at remedying this, consisting of a pipelined approach that automatically verifies whether a Knowledge Graph triple is supported by text extracted from its documented provenance. ProVe is intended to assist information curators and consists of four main steps involving rule-based methods and machine learning models: text extraction, triple verbalisation, sentence selection, and claim verification. ProVe is evaluated on a Wikidata dataset, achieving promising results overall and excellent performance on the binary classification task of detecting support from provenance, with 87.5 % accuracy and 82.9 % F1-macro on text-rich sources. The evaluation data and scripts used in this paper are available in GitHub and Figshare.

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Ensemble-Based Fact Classification with Knowledge Graph Embeddings

Cited by 4 publications

References 29 publications

Reason-able embeddings: Learning concept embeddings with a transferable neural reasoner

Reason-able embeddings: Learning concept embeddings with a transferable neural reasoner

Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models

ProVe: A pipeline for automated provenance verification of knowledge graphs against textual sources

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