# Question Answering Over Biological Knowledge Graph via Amazon Alexa

arXiv: https://arxiv.org/abs/2210.06040 · HTML: https://mlsanity.com/paper/2210.06040/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Explicit Knowledge-based Reasoning for Visual Question Answering](https://mlsanity.com/paper/1511.02570/) (2015)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [Building a PubMed knowledge graph](https://mlsanity.com/paper/2005.04308/) (2020)
- [Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network](https://mlsanity.com/paper/1908.01288/) (2019)
- [Neuro-symbolic representation learning on biological knowledge graphs](https://mlsanity.com/paper/1612.04256/) (2016)
- [Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text](https://mlsanity.com/paper/1908.07721/) (2019)
- [Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation](https://mlsanity.com/paper/1904.13196/) (2019)

## Built on by (most cited first, 4 of 4)

- [Explainable AI for Bioinformatics: Methods, Tools, and Applications](https://mlsanity.com/paper/2212.13261/) (2022)
- [Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness](https://mlsanity.com/paper/2307.15076/) (2023)
- [A Biomedical Knowledge Graph for Biomarker Discovery in Cancer](https://mlsanity.com/paper/2302.04737/) (2023)
- [A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI](https://mlsanity.com/paper/2401.06256/) (2024)

## Abstract (arXiv)

Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A knowledge graph (KG) can be constructed by integrating such facts and data and be used for data integration, exploration, and federated queries. However, exploration and querying large-scale KGs is tedious for certain groups of users due to a lack of knowledge about underlying data assets or semantic technologies. A question-answering (QA) system allows the answer of natural language questions over KGs automatically using triples contained in a KG. Recently, the use and adaption of digital assistants are getting wider owing to their capability at enabling users to voice commands to control smart systems or devices. This paper is about using Amazon Alexa's voice-enabled interface for QA over KGs. As a proof-of-concept, we use the well-known DisgeNET KG, which contains knowledge covering 1.13 million gene-disease associations between 21,671 genes and 30,170 diseases, disorders, and clinical or abnormal human phenotypes. Our study shows how Alex could be of help to find facts about certain biological entities from large-scale knowledge bases.