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

arXiv: https://arxiv.org/abs/2204.07309 · HTML: https://mlsanity.com/paper/2204.07309/

## 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 (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [AliGraph: A Comprehensive Graph Neural Network Platform](https://mlsanity.com/paper/1902.08730/) (2019)
- [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015)
- [Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment](https://mlsanity.com/paper/1611.03954/) (2016)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://mlsanity.com/paper/1903.12287/) (2019)
- [HoloClean: Holistic Data Repairs with Probabilistic Inference](https://mlsanity.com/paper/1702.00820/) (2017)
- [DGL-KE: Training Knowledge Graph Embeddings at Scale](https://mlsanity.com/paper/2004.08532/) (2020)
- [Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases](https://mlsanity.com/paper/2009.11564/) (2020)
- [A Survey on Truth Discovery](https://mlsanity.com/paper/1505.02463/) (2015)
- [HoloDetect: Few-Shot Learning for Error Detection](https://mlsanity.com/paper/1904.02285/) (2019)
- [Marius: Learning Massive Graph Embeddings on a Single Machine](https://mlsanity.com/paper/2101.08358/) (2021)
- [Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation](https://mlsanity.com/paper/2010.10363/) (2020)
- [Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models](https://mlsanity.com/paper/2008.05190/) (2020)
- [Iris: A Conversational Agent for Complex Tasks](https://mlsanity.com/paper/1707.05015/) (2017)
- [Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources](https://mlsanity.com/paper/1502.03519/) (2015)
- [WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset](https://mlsanity.com/paper/2107.09556/) (2021)
- [Parallel Correlation Clustering on Big Graphs](https://mlsanity.com/paper/1507.05086/) (2015)
- [Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems](https://mlsanity.com/paper/2104.09088/) (2021)
- [A Comparison of Blocking Methods for Record Linkage](https://mlsanity.com/paper/1407.3191/) (2014)
- [SLiMFast: Guaranteed Results for Data Fusion and Source Reliability](https://mlsanity.com/paper/1512.06474/) (2015)
- [Record fusion: A learning approach](https://mlsanity.com/paper/2006.10208/) (2020)
- [The Effects of Different JSON Representations on Querying Knowledge Graphs](https://mlsanity.com/paper/2004.04286/) (2020)

## Built on by (most cited first, 19 of 19)

- [Towards Foundation Models for Knowledge Graph Reasoning](https://mlsanity.com/paper/2310.04562/) (2023)
- [Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases](https://mlsanity.com/paper/2303.14617/) (2023)
- [FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge](https://mlsanity.com/paper/2310.17119/) (2023)
- [High-Throughput Vector Similarity Search in Knowledge Graphs](https://mlsanity.com/paper/2304.01926/) (2023)
- [Time Sensitive Knowledge Editing through Efficient Finetuning](https://mlsanity.com/paper/2406.04496/) (2024)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Incremental IVF Index Maintenance for Streaming Vector Search](https://mlsanity.com/paper/2411.00970/) (2024)
- [DBAIOps: A Reasoning LLM-Enhanced Database Operation and Maintenance System using Knowledge Graphs](https://mlsanity.com/paper/2508.01136/) (2025)
- [Uncertainty Management in the Construction of Knowledge Graphs: a Survey](https://mlsanity.com/paper/2405.16929/) (2024)
- [ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs](https://mlsanity.com/paper/2509.04696/) (2025)
- [Growing and Serving Large Open-domain Knowledge Graphs](https://mlsanity.com/paper/2305.09464/) (2023)
- [KGpipe: Generation and Evaluation of Pipelines for Data Integration into Knowledge Graphs](https://mlsanity.com/paper/2511.18364/) (2025)
- [Unsupervised Term Extraction for Highly Technical Domains](https://mlsanity.com/paper/2210.13118/) (2022)
- [Towards a GML-Enabled Knowledge Graph Platform](https://mlsanity.com/paper/2303.02166/) (2023)
- [Open Domain Knowledge Extraction for Knowledge Graphs](https://mlsanity.com/paper/2312.09424/) (2023)
- [Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models](https://mlsanity.com/paper/2606.18001/) (2026)
- [SAKA: An Intelligent Platform for Semi-automated Knowledge Graph Construction and Application](https://mlsanity.com/paper/2410.08094/) (2024)
- [MQ-GNN: A Multi-Queue Pipelined Architecture for Scalable and Efficient GNN Training](https://mlsanity.com/paper/2601.04707/) (2026)
- [MERIT: Scaling KG Foundation Models with Relation Transformers](https://mlsanity.com/paper/2610.15638/) (2026)

## 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.