ReliK: A Reliability Measure for Knowledge Graph Embeddings
Keywords: Knowledge Graph Embeddings, Reliability, Knowledge Graphs, Data quality
TL;DR: We propose ReliK, a task- method- and dataset-agnostic measure to assess the reliability of Knowledge Graph Embeddings
Abstract: _Can we assess a priori how well a knowledge graph embedding will perform on a specific downstream task and in a specific part of the knowledge graph?_ Knowledge graph embeddings (KGEs) represent entities (e.g., “da Vinci,” “Mona Lisa”) and relationships (e.g., “painted”) of a knowledge graph (KG) as vectors. KGEs are generated by optimizing an _embedding score_, which assesses whether a triple (e.g., “da Vinci,” “painted,” “Mona Lisa”) exists in the graph. KGEs have been proven effective in a variety of web-related downstream tasks, including, for instance, predicting relationship(s) among entities. However, the problem of anticipating the performance of a given KGE in a certain downstream task and locally to a specific individual triple, has not been tackled so far.
In this paper, we fill this gap with ReliK, a **Reli**ability measure for **K**GEs. ReliK relies solely on KGE embedding scores, is task- and KGE-agnostic, and requires no further KGE training. As such, it is particularly appealing for semantic web applications which call for testing multiple KGE methods on various parts of the KG and on each individual downstream task. Through extensive experiments, we attest that ReliK correlates well with both common downstream tasks, such as tail/relation prediction and triple classification, as well as advanced downstream tasks, such as rule mining and question answering, while preserving locality.
Track: Semantics and Knowledge
Submission Guidelines Scope: Yes
Submission Guidelines Blind: Yes
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Submission Guidelines Limit: Yes
Submission Guidelines Authorship: Yes
Student Author: Yes
Submission Number: 660
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