scholarly journals A knowledge graph based question answering method for medical domain

2021 ◽  
Vol 7 ◽  
pp. e667
Author(s):  
Xiaofeng Huang ◽  
Jixin Zhang ◽  
Zisang Xu ◽  
Lu Ou ◽  
Jianbin Tong
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Information Gain ◽  
Real Life ◽  
Medical Knowledge ◽  
Disease Diagnosis ◽  
Knowledge Graph ◽  
Named Entities ◽  
Human Labor ◽  
Medical Domain

Question answering (QA) is a hot field of research in Natural Language Processing. A big challenge in this field is to answer questions from knowledge-dependable domain. Since traditional QA hardly satisfies some knowledge-dependable situations, such as disease diagnosis, drug recommendation, etc. In recent years, researches focus on knowledge-based question answering (KBQA). However, there still exist some problems in KBQA, traditional KBQA is limited by a range of historical cases and takes too much human labor. To address the problems, in this paper, we propose an approach of knowledge graph based question answering (KGQA) method for medical domain, which firstly constructs a medical knowledge graph by extracting named entities and relations between the entities from medical documents. Then, in order to understand a question, it extracts the key information in the question according to the named entities, and meanwhile, it recognizes the questions’ intentions by adopting information gain. The next an inference method based on weighted path ranking on the knowledge graph is proposed to score the related entities according to the key information and intention of a given question. Finally, it extracts the inferred candidate entities to construct answers. Our approach can understand questions, connect the questions to the knowledge graph and inference the answers on the knowledge graph. Theoretical analysis and real-life experimental results show the efficiency of our approach.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

scholarly journals Legal Knowledge Extraction for Knowledge Graph Based Question-Answering

2020 ◽  
Author(s):  
Francesco Sovrano ◽  
Monica Palmirani ◽  
Fabio Vitali
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Design Patterns ◽  
Question Answering ◽  
Knowledge Extraction ◽  
Knowledge Graph ◽  
Legal Knowledge ◽  
Legal Information ◽  
Event Time ◽  
Legal Domain

This paper presents the Open Knowledge Extraction (OKE) tools combined with natural language analysis of the sentence in order to enrich the semantic of the legal knowledge extracted from legal text. In particular the use case is on international private law with specific regard to the Rome I Regulation EC 593/2008, Rome II Regulation EC 864/2007, and Brussels I bis Regulation EU 1215/2012. A Knowledge Graph (KG) is built using OKE and Natural Language Processing (NLP) methods jointly with the main ontology design patterns defined for the legal domain (e.g., event, time, role, agent, right, obligations, jurisdiction). Using critical questions, underlined by legal experts in the domain, we have built a question answering tool capable to support the information retrieval and to answer to these queries. The system should help the legal expert to retrieve the relevant legal information connected with topics, concepts, entities, normative references in order to integrate his/her searching activities.

scholarly journals Medical Knowledge Graph in Chinese Using Deep Semantic Mobile Computation Based on IoT and WoT

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wanheng Liu ◽  
Ling Yin ◽  
Cong Wang ◽  
Fulin Liu ◽  
Zhiyu Ni
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Medical Knowledge ◽  
Disease Diagnosis ◽  
Knowledge Graph ◽  
Great Success ◽  
Smart Healthcare ◽  
Made In

In this paper, a novel medical knowledge graph in Chinese approach applied in smart healthcare based on IoT and WoT is presented, using deep neural networks combined with self-attention to generate medical knowledge graph to make it more convenient for performing disease diagnosis and providing treatment advisement. Although great success has been made in the medical knowledge graph in recent studies, the issue of comprehensive medical knowledge graph in Chinese appropriate for telemedicine or mobile devices have been ignored. In our study, it is a working theory which is based on semantic mobile computing and deep learning. When several experiments have been carried out, it is demonstrated that it has better performance in generating various types of medical knowledge graph in Chinese, which is similar to that of the state-of-the-art. Also, it works well in the accuracy and comprehensive, which is much higher and highly consisted with the predictions of the theoretical model. It proves to be inspiring and encouraging that our work involving studies of medical knowledge graph in Chinese, which can stimulate the smart healthcare development.

ALBERT-QM: An ALBERT Based Method for Chinese Health Related Question Matching (Preprint)

2020 ◽  
Author(s):  
Feihong Yang ◽  
Jiao Li
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Binary Classification ◽  
Medical Knowledge ◽  
Related Question ◽  
Text Similarity ◽  
Web Based ◽  
Health Related ◽  
Health Question

BACKGROUND Question answering (QA) system is widely used in web-based health-care applications. Health consumers likely asked similar questions in various natural language expression due to the lack of medical knowledge. It’s challenging to match a new question to previous similar questions for answering. In health QA system development, question matching (QM) is a task to judge whether a pair of questions express the same meaning and is used to map the answer of matched question in the given question-answering database. BERT (i.e. Bidirectional Encoder Representations from Transformers) is proved to be state-of- the-art model in natural language processing (NLP) tasks, such as binary classification and sentence matching. As a light model of BERT, ALBERT is proposed to address the huge parameters and low training speed problems of BERT. Both of BERT and ALBERT can be used to address the QM problem. OBJECTIVE In this study, we aim to develop an ALBERT based method for Chinese health related question matching. METHODS Our proposed method, named as ALBERT-QM, consists of three components. (1)Data augmenting. Similar health question pairs were augmented for training preparation. (2)ALBERT model training. Given the augmented training pairs, three ALBERT models were trained and fine-tuned. (3)Similarity combining. Health question similarity score were calculated by combining ALBRT model outputs with text similarity. To evaluate our ALBERT-QM performance on similar question identification, we used an open dataset with 20,000 labeled Chinese health question pairs. RESULTS Our ALBERT-QM is able to identify similar Chinese health questions, achieving the precision of 86.69%, recall of 86.70% and F1 of 86.69%. Comparing with baseline method (text similarity algorithm), ALBERT-QM enhanced the F1-score by 20.73%. Comparing with other BERT series models, our ALBERT-QM is much lighter with the files size of 64.8MB which is 1/6 times that other BERT models. We made our ALBERT-QM open accessible at https://github.com/trueto/albert_question_match. CONCLUSIONS In this study, we developed an open source algorithm, ALBERT-QM, contributing to similar Chinese health questions identification in a health QA system. Our ALBERT-QM achieved better performance in question matching with lower memory usage, which is beneficial to the web-based or mobile-based QA applications.

scholarly journals Domain-Transferable Method for Named Entity Recognition Task

2020 ◽  
Author(s):  
Vladislav Mikhailov ◽  
Tatiana Shavrina
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Neural Models ◽  
Named Entities ◽  
Named Entity ◽  
Domain Specific ◽  
Human Effort

Named Entity Recognition (NER) is a fundamental task in the fields of natural language processing and information extraction. NER has been widely used as a standalone tool or an essential component in a variety of applications such as question answering, dialogue assistants and knowledge graphs development. However, training reliable NER models requires a large amount of labelled data which is expensive to obtain, particularly in specialized domains. This paper describes a method to learn a domain-specific NER model for an arbitrary set of named entities when domain-specific supervision is not available. We assume that the supervision can be obtained with no human effort, and neural models can learn from each other. The code, data and models are publicly available.

scholarly journals KVQA: Knowledge-Aware Visual Question Answering

2019 ◽  
Vol 33 ◽  
pp. 8876-8884 ◽  
Author(s):  
Sanket Shah ◽  
Anand Mishra ◽  
Naganand Yadati ◽  
Partha Pratim Talukdar
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
World Knowledge ◽  
Named Entities ◽  
Commonsense Knowledge ◽  
White House ◽  
Visual Question Answering ◽  
Knowledge Graphs ◽  
Answering Questions

Visual Question Answering (VQA) has emerged as an important problem spanning Computer Vision, Natural Language Processing and Artificial Intelligence (AI). In conventional VQA, one may ask questions about an image which can be answered purely based on its content. For example, given an image with people in it, a typical VQA question may inquire about the number of people in the image. More recently, there is growing interest in answering questions which require commonsense knowledge involving common nouns (e.g., cats, dogs, microphones) present in the image. In spite of this progress, the important problem of answering questions requiring world knowledge about named entities (e.g., Barack Obama, White House, United Nations) in the image has not been addressed in prior research. We address this gap in this paper, and introduce KVQA – the first dataset for the task of (world) knowledge-aware VQA. KVQA consists of 183K question-answer pairs involving more than 18K named entities and 24K images. Questions in this dataset require multi-entity, multi-relation, and multi-hop reasoning over large Knowledge Graphs (KG) to arrive at an answer. To the best of our knowledge, KVQA is the largest dataset for exploring VQA over KG. Further, we also provide baseline performances using state-of-the-art methods on KVQA.

scholarly journals Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 31
Author(s):  
Ayiguli Halike ◽  
Kahaerjiang Abiderexiti ◽  
Tuergen Yibulayin
Keyword(s):  
Language Processing ◽  
Extraction Method ◽  
Question Answering ◽  
Conditional Random Field ◽  
Weighted Average ◽  
Relation Extraction ◽  
System Construction ◽  
Named Entities ◽  
Named Entity ◽  
Conditional Random Field Model

Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines a conditional random field model for making suggestions regarding annotation based on extracted relations with a set of rules applied by human annotators to rapidly increase the size of the Uyghur corpus. We integrate our relation extraction method into an existing annotation tool, and, with the help of human correction, we implement Uyghur relation extraction and expand the existing corpus. The effectiveness of our proposed approach is demonstrated based on experimental results by using an existing Uyghur corpus, and our method achieves a maximum weighted average between precision and recall of 61.34%. The method we proposed achieves state-of-the-art results on entity and relation extraction tasks in Uyghur.

scholarly journals Research on Automatic Question Answering of Generative Knowledge Graph Based on Pointer Network

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 136
Author(s):  
Shuang Liu ◽  
Nannan Tan ◽  
Yaqian Ge ◽  
Niko Lukač
Keyword(s):  
Knowledge Base ◽  
Word Frequency ◽  
Language Processing ◽  
Question Answering ◽  
Language Model ◽  
Word List ◽  
Superior Performance ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Question Answering Systems

Question-answering systems based on knowledge graphs are extremely challenging tasks in the field of natural language processing. Most of the existing Chinese Knowledge Base Question Answering(KBQA) can only return the knowledge stored in the knowledge base by extractive methods. Nevertheless, this processing does not conform to the reading habits and cannot solve the Out-of-vocabulary(OOV) problem. In this paper, a new generative question answering method based on knowledge graph is proposed, including three parts of knowledge vocabulary construction, data pre-processing, and answer generation. In the word list construction, BiLSTM-CRF is used to identify the entity in the source text, finding the triples contained in the entity, counting the word frequency, and constructing it. In the part of data pre-processing, a pre-trained language model BERT combining word frequency semantic features is adopted to obtain word vectors. In the answer generation part, one combination of a vocabulary constructed by the knowledge graph and a pointer generator network(PGN) is proposed to point to the corresponding entity for generating answer. The experimental results show that the proposed method can achieve superior performance on WebQA datasets than other methods.

scholarly journals Causal Reasoning Model Based on Medical Knowledge Graph for Disease Diagnosis

2021 ◽  
Author(s):  
Ze Xu ◽  
Huazhen Wang ◽  
Xiaocong Liu ◽  
Ting He ◽  
Jin Gou
Keyword(s):  
Causal Reasoning ◽  
Medical Knowledge ◽  
Threshold Value ◽  
Disease Diagnosis ◽  
Knowledge Graph ◽  
Dynamic Threshold ◽  
Model Based ◽  
Knowledge Reasoning ◽  
Patient Symptom ◽  
Proposed Model

In view of the non-interpretability of disease diagnosis models based on deep learning, a knowledge reasoning model based on medical knowledge graph for intelligent diagnosis is proposed. Given the patient symptom set, the co-occurrence of the patient and the disease is calculated, then the patient suffering from one disease is calculated. Based on the dynamic threshold value, the final disease diagnosis result of the patient is outputted. According to the symptoms of patients and the symptoms in the knowledge graph, the causal reasoning of the disease diagnosis is interpretable. Experiments on 145,712 pediatric electronic medical records in Chinese show that the proposed model can predict diseases with interpretability, and the accuracy reaches-82.12%.

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