# Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval

arXiv: https://arxiv.org/abs/1805.07591 · HTML: https://mlsanity.com/paper/1805.07591/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2018.

## Built on by (most cited first, 30 of 33)

- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [CPM-2: Large-scale Cost-effective Pre-trained Language Models](https://mlsanity.com/paper/2106.10715/) (2021)
- [Temporal Knowledge Graph Reasoning with Historical Contrastive Learning](https://mlsanity.com/paper/2211.10904/) (2022)
- [Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity](https://mlsanity.com/paper/2111.08366/) (2021)
- [A Semantic Search Engine for Mathlib4](https://mlsanity.com/paper/2403.13310/) (2024)
- [A Survey of Knowledge Graph Embedding and Their Applications](https://mlsanity.com/paper/2107.07842/) (2021)
- [Neural ranking models for document retrieval](https://mlsanity.com/paper/2102.11903/) (2021)
- [Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2201.04831/) (2022)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning](https://mlsanity.com/paper/2209.01416/) (2022)
- [Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction](https://mlsanity.com/paper/2304.04717/) (2023)
- [Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement](https://mlsanity.com/paper/2112.13510/) (2021)
- [DREQ: Document Re-Ranking Using Entity-based Query Understanding](https://mlsanity.com/paper/2401.05939/) (2024)
- [Optimal Transport Guided Correlation Assignment for Multimodal Entity Linking](https://mlsanity.com/paper/2406.01934/) (2024)
- [DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding](https://mlsanity.com/paper/2212.04205/) (2022)
- [Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning](https://mlsanity.com/paper/2509.01166/) (2025)
- [DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach](https://mlsanity.com/paper/2410.22631/) (2024)
- [Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space](https://mlsanity.com/paper/2106.15217/) (2021)
- [Bending the Future: Autoregressive Modeling of Temporal Knowledge Graphs in Curvature-Variable Hyperbolic Spaces](https://mlsanity.com/paper/2209.05635/) (2022)
- [Do as I can, not as I get](https://mlsanity.com/paper/2306.10345/) (2023)
- [QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking](https://mlsanity.com/paper/2510.11589/) (2025)
- [Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking](https://mlsanity.com/paper/2606.15998/) (2026)
- [Profiling and Evolution of Intellectual Property](https://mlsanity.com/paper/2204.09333/) (2022)
- [Learning Attention-based Representations from Multiple Patterns for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/2206.04801/) (2022)
- [Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation](https://mlsanity.com/paper/2308.15002/) (2023)
- [Knowledge-enhanced Multi-perspective Video Representation Learning for Scene Recognition](https://mlsanity.com/paper/2401.04354/) (2024)
- [Dynamic Injection of Entity Knowledge into Dense Retrievers](https://mlsanity.com/paper/2507.03922/) (2025)
- [REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking](https://mlsanity.com/paper/2510.11592/) (2025)
- [TA-KAND: Two-stage Attention Triple Enhancement and U-KAN based Diffusion For Few-shot Knowledge Graph Completion](https://mlsanity.com/paper/2512.12182/) (2025)

## Abstract (arXiv)

This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words and entity annotations. The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks. The two components are learned end-to-end, making EDRM a natural combination of entity-oriented search and neural information retrieval. Our experiments on a commercial search log demonstrate the effectiveness of EDRM. Our analyses reveal that knowledge graph semantics significantly improve the generalization ability of neural ranking models.