# Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs

arXiv: https://arxiv.org/abs/1708.04828 · HTML: https://mlsanity.com/paper/1708.04828/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2017.

## Built on by (most cited first, 14 of 14)

- [Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models](https://mlsanity.com/paper/2312.06323/) (2023)
- [Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers](https://mlsanity.com/paper/2305.18256/) (2023)
- [Applications of knowledge graphs for food science and industry](https://mlsanity.com/paper/2107.05869/) (2021)
- [Augmenting Knowledge Graphs for Better Link Prediction](https://mlsanity.com/paper/2203.13965/) (2022)
- [LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals](https://mlsanity.com/paper/2304.14742/) (2023)
- [BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs](https://mlsanity.com/paper/2306.03606/) (2023)
- [Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data](https://mlsanity.com/paper/2201.08580/) (2022)
- [Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning](https://mlsanity.com/paper/2309.03219/) (2023)
- [End-to-End Learning on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2309.01169/) (2023)
- [Numerical Literals in Link Prediction: A Critical Examination of Models and Datasets](https://mlsanity.com/paper/2407.18241/) (2024)
- [HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling](https://mlsanity.com/paper/2408.14812/) (2024)
- [NumCoKE: Ordinal-Aware Numerical Reasoning over Knowledge Graphs with Mixture-of-Experts and Contrastive Learning](https://mlsanity.com/paper/2411.12950/) (2024)
- [CHAINSFORMER: Numerical Reasoning on Knowledge Graphs from a Chain Perspective](https://mlsanity.com/paper/2504.14282/) (2025)
- [Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs](https://mlsanity.com/paper/2608.26729/) (2026)

## Abstract (arXiv)

Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete attributes for each entity. Intuitively, these attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and attributes that encode information about both tasks. Moreover, such attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and attribute value prediction.