# Open Question Answering with Weakly Supervised Embedding Models

arXiv: https://arxiv.org/abs/1404.4326 · HTML: https://mlsanity.com/paper/1404.4326/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2014.

## Built on by (most cited first, 26 of 26)

- [Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://mlsanity.com/paper/2404.17723/) (2024)
- [Complex Knowledge Base Question Answering: A Survey](https://mlsanity.com/paper/2108.06688/) (2021)
- [Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts](https://mlsanity.com/paper/2103.08115/) (2021)
- [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://mlsanity.com/paper/2101.01775/) (2021)
- [Towards Continual Knowledge Graph Embedding via Incremental Distillation](https://mlsanity.com/paper/2405.04453/) (2024)
- [Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2204.10448/) (2022)
- [Fast and Continual Knowledge Graph Embedding via Incremental LoRA](https://mlsanity.com/paper/2407.05705/) (2024)
- [Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network](https://mlsanity.com/paper/2106.06555/) (2021)
- [TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors](https://mlsanity.com/paper/2209.08271/) (2022)
- [Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems](https://mlsanity.com/paper/2312.10967/) (2023)
- [Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes](https://mlsanity.com/paper/2108.00513/) (2021)
- [PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning](https://mlsanity.com/paper/2204.13957/) (2022)
- [A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning](https://mlsanity.com/paper/2308.07889/) (2023)
- [A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution](https://mlsanity.com/paper/2108.13024/) (2021)
- [ModulE: Module Embedding for Knowledge Graphs](https://mlsanity.com/paper/2203.04702/) (2022)
- [KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods](https://mlsanity.com/paper/2106.14233/) (2021)
- [A Probabilistic Framework for Knowledge Graph Data Augmentation](https://mlsanity.com/paper/2110.13205/) (2021)
- [Spatio-Temporal Relation Learning for Video Anomaly Detection](https://mlsanity.com/paper/2209.13116/) (2022)
- [HEISIR: Hierarchical Expansion of Inverted Semantic Indexing for Training-free Retrieval of Conversational Data using LLMs](https://mlsanity.com/paper/2503.04141/) (2025)
- [A Knowledge-based Approach for Answering Complex Questions in Persian](https://mlsanity.com/paper/2107.02040/) (2021)
- [Knowledge Modelling and Active Learning in Manufacturing](https://mlsanity.com/paper/2107.02298/) (2021)
- [Attacks against Ranking Algorithms with Text Embeddings: a Case Study on Recruitment Algorithms](https://mlsanity.com/paper/2108.05490/) (2021)
- [Recent Advances in Automated Question Answering In Biomedical Domain](https://mlsanity.com/paper/2111.05937/) (2021)
- [Knowledgebra: An Algebraic Learning Framework for Knowledge Graph](https://mlsanity.com/paper/2204.07328/) (2022)
- [Improving rule mining via embedding-based link prediction](https://mlsanity.com/paper/2406.10144/) (2024)
- [Unlearning of Knowledge Graph Embedding via Preference Optimization](https://mlsanity.com/paper/2507.20566/) (2025)

## Abstract (arXiv)

Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of its schema, without requiring any grammar or lexicon. Our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine-tuning step using the weak supervision provided by blending automatically and collaboratively generated resources. We empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over paralex, the only existing method able to be trained on similar weakly labeled data.