ExCut: Explainable Embedding-Based Clustering over Knowledge Graphs

Gad-Elrab, Mohamed H.; Stepanova, Daria; Tran, Trung-Kien; Adel, Heike; Weikum, Gerhard

doi:10.1007/978-3-030-62419-4_13

Cited by 16 publications

(9 citation statements)

References 25 publications

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“…The score of the rules in IterE depends directly on the embeddings of the relations that it uses. ExCut [7] also employs KGEs and rule mining, however, with the purpose of explaining clusterings of entities.…”

Section: Related Workmentioning

confidence: 99%

On the Effectiveness of Knowledge Graph Embeddings: a Rule Mining Approach

Jøsang¹,

Guimarães²,

Ozaki³

2022

Preprint

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We study the effectiveness of Knowledge Graph Embeddings (KGE) for knowledge graph (KG) completion with rule mining. More specifically, we mine rules from KGs before and after they have been completed by a KGE to compare possible differences in the rules extracted. We apply this method to classical KGEs approaches, in particular, TransE, DistMult and ComplEx. Our experiments indicate that there can be huge differences between the extracted rules, depending on the KGE approach for KG completion. In particular, after the TransE completion, several spurious rules were extracted.

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Section: Related Workmentioning

confidence: 99%

On the Effectiveness of Knowledge Graph Embeddings: a Rule Mining Approach

Jøsang¹,

Guimarães²,

Ozaki³

2022

Preprint

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“…Clustering over (explainable) knowledge graph embeddings [14], [15] could also serve a similar purpose. However, the limited sample size available in our scenario probably is limiting the achievable quality of embeddings.…”

Section: Related Workmentioning

confidence: 99%

Reliability-Based Aggregation of Heterogeneous Knowledge to Assist Operators in Manufacturing

Nordsieck

Heider

Hoffmann

et al. 2022

2022 IEEE 16th International Conference on Semantic Computing (ICSC)

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In socio-technical systems, such as manufacturing processes, human operators are often entrusted with cognitive tasks that rely on tacit knowledge. Extracting the operators' tacit knowledge is beneficial to facilitate knowledge transfer and enable semantic machine learning. We improve upon an existing methodology that relies on operators' insights into influences on their decision making processes to extract tacit knowledge. By introducing a data-based weighting of the operators' information, we are able to control varying degrees of worker reliability and other individual biases, increasing the quality of the aggregated knowledge. We evaluate several methods to weigh and aggregate knowledge on a real-world dataset collected in the domain of fused deposition modelling (FDM) showing an improvement of 34% over a previously published baseline applied to our data. Applicability of the approach in the same domain is demonstrated by a case study, where the aggregated knowledge is utilised to shorten the time required for parametrisation.

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“…In [16] introduce ExCut, an approach for computing explainable clusters, which combines embeddingbased clustering with symbolic rule learning to produce human-understandable explanations for the resulting clusters. The method is designed for knowledge graphs, and its goal is to cluster semantically similar entities as denoted by the underlying background knowledge component.…”

Section: Explainable Clusteringmentioning

confidence: 99%

“…Prior knowledge Explanation granularity [35] statistical summary, visualization model-agnostic no single instance [52] example-based model-specific no single instance [12] rules,trees model-specific no single instance [20] feature importance model-agnostic no global [15] rules model-specific no single instance [28][23], [4] [13] rules, trees model-specific no single-instance [16] rules, ontologies model-specific yes single instance [30] rules model-agnostic no single instance [27] rules model-agnostic no global [22] knowledge graph model-specific no global…”

Section: Reference Explanation Form Explanation Mechanismmentioning

confidence: 99%

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KnAC: an approach for enhancing cluster analysis with background knowledge and explanations

Bobek¹,

Kuk²,

Brzegowski³

et al. 2021

Preprint

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Pattern discovery in multidimensional data sets has been a subject of research since decades. There exists a wide spectrum of clustering algorithms that can be used for that purpose. However, their practical applications share in common the post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be a bottleneck of the process, especially in the cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters, but also a conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KNAC), which main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution does not depend on any ready clustering algorithm, nor introduce one. Instead KNAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and model-agnostic. We demonstrate the feasibility of our method on artificially, reproducible examples and on a real life use case scenario.

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ExCut: Explainable Embedding-Based Clustering over Knowledge Graphs

Cited by 16 publications

References 25 publications

On the Effectiveness of Knowledge Graph Embeddings: a Rule Mining Approach

On the Effectiveness of Knowledge Graph Embeddings: a Rule Mining Approach

Reliability-Based Aggregation of Heterogeneous Knowledge to Assist Operators in Manufacturing

KnAC: an approach for enhancing cluster analysis with background knowledge and explanations

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