mlsanityData to 14 Oct 2026

Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale

arXiv 2204.07309 ↗First posted 15 Apr 2022cs.DB cs.AI cs.CLcited by 19

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Abstract (arXiv)

We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremental design to continuously integrate billions of facts about real-world entities and construct a central knowledge graph that supports multiple production use cases with diverse requirements around data freshness, accuracy, and availability. In this paper, we discuss the unique challenges associated with knowledge graph construction at industrial scale, and review the main components of Saga and how they address these challenges. Finally, we share lessons-learned from a wide array of production use cases powered by Saga.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 28 of its 94 references that are in the map. First posted on arXiv 15 Apr 2022.

Builds on

28 of 94 references.

References · 28

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
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  10. 2016Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignmentcited by 128
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16 more
  1. 2020DGL-KE: Training Knowledge Graph Embeddings at Scalecited by 67
  2. 2020Machine Knowledge: Creation and Curation of Comprehensive Knowledge Basescited by 55
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  4. 2019HoloDetect: Few-Shot Learning for Error Detectioncited by 31
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  6. 2020Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguationcited by 24
  7. 2020Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Modelscited by 23
  8. 2017Iris: A Conversational Agent for Complex Taskscited by 22
  9. 2015Knowledge-Based Trust: Estimating the Trustworthiness of Web Sourcescited by 21
  10. 2021WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Datasetcited by 20
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  16. 2020The Effects of Different JSON Representations on Querying Knowledge Graphscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 8; 2024: 4; 2025: 3; 2026: 3.

19 papers.

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  1. 2023Towards Foundation Models for Knowledge Graph Reasoningcited by 86
  2. 2023Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databasescited by 35
  3. 2023FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledgecited by 25
  4. 2023High-Throughput Vector Similarity Search in Knowledge Graphscited by 19
  5. 2024Time Sensitive Knowledge Editing through Efficient Finetuningcited by 14
  6. 2023Construction of Knowledge Graphs: State and Challengescited by 12
  7. 2024Incremental IVF Index Maintenance for Streaming Vector Searchcited by 7
  8. 2025DBAIOps: A Reasoning LLM-Enhanced Database Operation and Maintenance System using Knowledge Graphscited by 7
  9. 2024Uncertainty Management in the Construction of Knowledge Graphs: a Surveycited by 5
  10. 2025ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMscited by 4
  11. 2023Growing and Serving Large Open-domain Knowledge Graphscited by 3
  12. 2025KGpipe: Generation and Evaluation of Pipelines for Data Integration into Knowledge Graphscited by 2
7 more
  1. 2022Unsupervised Term Extraction for Highly Technical Domainscited by 1
  2. 2023Towards a GML-Enabled Knowledge Graph Platformcited by 1
  3. 2023Open Domain Knowledge Extraction for Knowledge Graphscited by 1
  4. 2026Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Modelscited by 1
  5. 2024SAKA: An Intelligent Platform for Semi-automated Knowledge Graph Construction and Applicationcited by 0
  6. 2026MQ-GNN: A Multi-Queue Pipelined Architecture for Scalable and Efficient GNN Trainingcited by 0
  7. 2026MERIT: Scaling KG Foundation Models with Relation Transformerscited by 0