ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training Article Swipe
Jiayang Wu
,
Wensheng Gan
,
Jiahao Zhang
,
Philip S. Yu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3748321
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3748321
In the current development of large language models (LLMs), it is important to ensure the accuracy and reliability of the underlying data sources. LLMs are critical for various applications, but they often suffer from hallucinations and inaccuracies due to knowledge gaps in the training data. Knowledge graphs (KGs), as a powerful structural tool, could serve as a vital external information source to mitigate the aforementioned issues. By providing a structured and comprehensive understanding of real-world data, KGs enhance the performance and reliability of LLMs. However, it is common that errors exist in KGs while extracting triplets from unstructured data to construct KGs. This could lead to degraded performance in downstream tasks such as question-answering and recommender systems. Therefore, anomaly detection in KGs is essential to identify and correct these errors. This article presents an anomaly detection algorithm in knowledge graphs with dual-channel learning (ADKGD). ADKGD leverages a dual-channel learning approach to enhance representation learning from both the entity-view and triplet-view perspectives. Furthermore, using a cross-layer approach, our framework integrates internal information aggregation and context information aggregation. We introduce a kullback-leibler (KL)-loss component to improve the accuracy of the scoring function between the dual channels. To evaluate ADKGD’s performance, we conduct empirical studies on three real-world KGs: WN18RR, FB15K, and NELL-995. Experimental results demonstrate that ADKGD outperforms state-of-the-art anomaly detection algorithms. The source code and datasets are publicly available at https://github.com/csjywu1/ADKGD .
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3748321
- https://dl.acm.org/doi/pdf/10.1145/3748321
- OA Status
- bronze
- Cited By
- 1
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413941680
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413941680Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3748321Digital Object Identifier
- Title
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ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel TrainingWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-03Full publication date if available
- Authors
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Jiayang Wu, Wensheng Gan, Jiahao Zhang, Philip S. YuList of authors in order
- Landing page
-
https://doi.org/10.1145/3748321Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3748321Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3748321Direct OA link when available
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Dual (grammatical number), Anomaly detection, Training (meteorology), Anomaly (physics), Computer science, Artificial intelligence, Physics, Linguistics, Philosophy, Meteorology, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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16Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learning | 142, 148, 153 |
| abstract_inverted_index.mitigate | 62 |
| abstract_inverted_index.powerful | 50 |
| abstract_inverted_index.presents | 132 |
| abstract_inverted_index.publicly | 226 |
| abstract_inverted_index.sources. | 22 |
| abstract_inverted_index.systems. | 116 |
| abstract_inverted_index.training | 43 |
| abstract_inverted_index.triplets | 95 |
| abstract_inverted_index.(KL)-loss | 180 |
| abstract_inverted_index.ADKGD’s | 196 |
| abstract_inverted_index.Knowledge | 45 |
| abstract_inverted_index.NELL-995. | 209 |
| abstract_inverted_index.algorithm | 136 |
| abstract_inverted_index.approach, | 165 |
| abstract_inverted_index.available | 227 |
| abstract_inverted_index.channels. | 193 |
| abstract_inverted_index.component | 181 |
| abstract_inverted_index.construct | 100 |
| abstract_inverted_index.detection | 119, 135, 218 |
| abstract_inverted_index.empirical | 200 |
| abstract_inverted_index.essential | 123 |
| abstract_inverted_index.framework | 167 |
| abstract_inverted_index.important | 11 |
| abstract_inverted_index.introduce | 177 |
| abstract_inverted_index.knowledge | 39, 138 |
| abstract_inverted_index.leverages | 145 |
| abstract_inverted_index.providing | 67 |
| abstract_inverted_index.Therefore, | 117 |
| abstract_inverted_index.downstream | 109 |
| abstract_inverted_index.extracting | 94 |
| abstract_inverted_index.integrates | 168 |
| abstract_inverted_index.real-world | 74, 204 |
| abstract_inverted_index.structural | 51 |
| abstract_inverted_index.structured | 69 |
| abstract_inverted_index.underlying | 20 |
| abstract_inverted_index.aggregation | 171 |
| abstract_inverted_index.algorithms. | 219 |
| abstract_inverted_index.cross-layer | 164 |
| abstract_inverted_index.demonstrate | 212 |
| abstract_inverted_index.development | 3 |
| abstract_inverted_index.entity-view | 157 |
| abstract_inverted_index.information | 59, 170, 174 |
| abstract_inverted_index.outperforms | 215 |
| abstract_inverted_index.performance | 79, 107 |
| abstract_inverted_index.recommender | 115 |
| abstract_inverted_index.reliability | 17, 81 |
| abstract_inverted_index.Experimental | 210 |
| abstract_inverted_index.Furthermore, | 161 |
| abstract_inverted_index.aggregation. | 175 |
| abstract_inverted_index.dual-channel | 141, 147 |
| abstract_inverted_index.inaccuracies | 36 |
| abstract_inverted_index.performance, | 197 |
| abstract_inverted_index.triplet-view | 159 |
| abstract_inverted_index.unstructured | 97 |
| abstract_inverted_index.applications, | 28 |
| abstract_inverted_index.comprehensive | 71 |
| abstract_inverted_index.perspectives. | 160 |
| abstract_inverted_index.understanding | 72 |
| abstract_inverted_index.aforementioned | 64 |
| abstract_inverted_index.hallucinations | 34 |
| abstract_inverted_index.representation | 152 |
| abstract_inverted_index.kullback-leibler | 179 |
| abstract_inverted_index.state-of-the-art | 216 |
| abstract_inverted_index.question-answering | 113 |
| abstract_inverted_index.https://github.com/csjywu1/ADKGD | 229 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.95711073 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |