The Knowledge Graph Track at OAEI

Hertling, Sven; Paulheim, Heiko

doi:10.1007/978-3-030-49461-2_20

Cited by 18 publications

(7 citation statements)

References 18 publications

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“…It is worth noting that Ontology Alignment Evaluation Initiatives 1 has been organizing a KG track since 2018 (Hertling and Paulheim, 2020). The benchmarks used are those KGs extracted from several different Wikis from Fandom; 2 for example, starwars-swg is a benchmark with mappings between two KGs from Star Wars Wiki and Star Wars Galaxies Wiki.…”

Section: Benchmarksmentioning

confidence: 99%

An Industry Evaluation of Embedding-based Entity Alignment

Zhang¹,

Liu²,

Chen³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

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Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage. In this study, we evaluate those state-of-the-art methods in an industrial context, where the impact of seed mappings with different sizes and different biases is explored. Besides the popular benchmarks from DBpedia and Wikidata, we contribute and evaluate a new industrial benchmark that is extracted from two heterogeneous knowledge graphs (KGs) under deployment for medical applications. The experimental results enable the analysis of the advantages and disadvantages of these alignment methods and the further discussion of suitable strategies for their industrial deployment.

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Section: Benchmarksmentioning

confidence: 99%

An Industry Evaluation of Embedding-based Entity Alignment

Zhang¹,

Liu²,

Chen³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

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“…We evaluate our approach on the anatomy, biodiv, and commonkg tracks of OAEI 6 . Moreover, we show results on the Knowledge Graph track [7], where only class correspondences are considered. For all tracks, we compare OLaLa against the three best-performing systems in the different OEAI tracks in the 2022 edition of the OAEI [21].…”

Section: Discussionmentioning

confidence: 99%

OLaLa: Ontology Matching with Large Language Models

Hertling,

Paulheim

2023

Proceedings of the 12th Knowledge Capture Conference 2023

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Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process. With the rise of Large Language Models, it is possible to incorporate this knowledge in a better way into the matching pipeline. A number of decisions still need to be taken, e.g., how to generate a prompt that is useful to the model, how information in the KG can be formulated in prompts, which Large Language Model to choose, how to provide existing correspondences to the model, how to generate candidates, etc. In this paper, we present a prototype that explores these questions by applying zero-shot and few-shot prompting with multiple open Large Language Models to different tasks of the Ontology Alignment Evaluation Initiative (OAEI). We show that with only a handful of examples and a well-designed prompt, it is possible to achieve results that are en par with supervised matching systems which use a much larger portion of the ground truth.

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“…Traditional ontology alignment systems e.g., LogMap often rely on defined logic constraints to correct (usually eliminate) those mappings that lead to inconsistency. Recently they are also being extended to entity alignment for large KBs [21]. In the future work, we will evaluate our entity alignment correction solution on KBs with a richer schema (e.g., hierarchical classes and multiple properties) by analyzing the impact of constraint mining and comparing the results with the above ontology alignment systems.…”

Section: Discussion and Outlookmentioning

confidence: 99%

An assertion and alignment correction framework for large scale knowledge bases

Chen

Jiménez-Ruiz

Horrocks

et al. 2022

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Various knowledge bases (KBs) have been constructed via information extraction from encyclopedias, text and tables, as well as alignment of multiple sources. Their usefulness and usability is often limited by quality issues. One common issue is the presence of erroneous assertions and alignments, often caused by lexical or semantic confusion. We study the problem of correcting such assertions and alignments, and present a general correction framework which combines lexical matching, context-aware sub-KB extraction, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated with one set of literal assertions from DBpedia, one set of entity assertions from an enterprise medical KB, and one set of mapping assertions from a music KB constructed by integrating Wikidata, Discogs and MusicBrainz. It has achieved promising results, with a correction rate (i.e., the ratio of the target assertions/alignments that are corrected with right substitutes) of 70.1 %, 60.9 % and 71.8 %, respectively.

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The Knowledge Graph Track at OAEI

Cited by 18 publications

References 18 publications

An Industry Evaluation of Embedding-based Entity Alignment

An Industry Evaluation of Embedding-based Entity Alignment

OLaLa: Ontology Matching with Large Language Models

An assertion and alignment correction framework for large scale knowledge bases

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