4,880 research outputs found
MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
Knowledge Graph (KG) contains entities and the relations between entities.
Due to its representation ability, KG has been successfully applied to support
many medical/healthcare tasks. However, in the medical domain, knowledge holds
under certain conditions. For example, symptom \emph{runny nose} highly
indicates the existence of disease \emph{whooping cough} when the patient is a
baby rather than the people at other ages. Such conditions for medical
knowledge are crucial for decision-making in various medical applications,
which is missing in existing medical KGs. In this paper, we aim to discovery
medical knowledge conditions from texts to enrich KGs.
Electronic Medical Records (EMRs) are systematized collection of clinical
data and contain detailed information about patients, thus EMRs can be a good
resource to discover medical knowledge conditions. Unfortunately, the amount of
available EMRs is limited due to reasons such as regularization. Meanwhile, a
large amount of medical question answering (QA) data is available, which can
greatly help the studied task. However, the quality of medical QA data is quite
diverse, which may degrade the quality of the discovered medical knowledge
conditions. In the light of these challenges, we propose a new truth discovery
method, MedTruth, for medical knowledge condition discovery, which incorporates
prior source quality information into the source reliability estimation
procedure, and also utilizes the knowledge triple information for trustworthy
information computation. We conduct series of experiments on real-world medical
datasets to demonstrate that the proposed method can discover meaningful and
accurate conditions for medical knowledge by leveraging both EMR and QA data.
Further, the proposed method is tested on synthetic datasets to validate its
effectiveness under various scenarios.Comment: Accepted as CIKM2019 long pape
Knowledge Graph based Question and Answer System for Cosmetic Domain
With the development of E-commerce, the requirements of customers for products become more detailed, and the workload of customer service consultants will increase massively. However, the manufacturer is not obliged to provide specific product ingredients on the website. Therefore, it is necessary to construct a KBQA system to relieve the pressure of online customer service and effectively help customers to find suitable skincare production. For the cosmetic filed, the different basic cosmetics may have varied effects depending on its ingredients. In this paper, we utilize CosDNA website and online cosmetic websites to construct a cosmetic product knowledge graph to broaden the relationship between cosmetics, ingredients, skin type, and effects. Besides, we build the question answering system based on the cosmetic knowledge graph to allow users to understand product details directly and make the decision quickly
Model Dan Metoda Arsitektur Pada Sistem Tanya Jawab Medis
Pada makalah ini, akan dilakukan survey beberapa penelitian yang membahas mengenai sistem tanya jawab dengan domain pada bidang medis (medical question answering = MedQuAn). Sistem MedQuAn mengolah pertanyaan yang diajukan dalam bentuk teks bahasa alami dan kemudian sistem akan memberikan jawaban yang relevan. Makalah ini mencoba menelaah modul konseptual MedQuAn, bahwa sistem tanya jawab terdiri dari tiga komponen inti yang berbeda beserta metoda/ pendekatan yang digunakan. Ketiga komponen inti tersebut adalah klasifikasi pertanyaan, pencarian dokumen, dan ekstraksi jawaban. Hasil akhir dari survey ini adalah sebuah kontribusi untuk pengembangan penelitian di masa mendatang di domain MedQuAn khususnya untuk sistem tanya jawab medis dengan menggunakan bahasa Indonesia
Model dan Metoda Arsitektur pada Sistem Tanya Jawab Medis
Pada makalah ini, akan dilakukan survey beberapa penelitian yang membahas mengenai sistem tanya jawab dengan domain pada bidang medis (medical question answering = MedQuAn). Sistem MedQuAn mengolah pertanyaan yang diajukan dalam bentuk teks bahasa alami dan kemudian sistem akan memberikan jawaban yang relevan. Makalah ini mencoba menelaah modul konseptual MedQuAn, bahwa sistem tanya jawab terdiri dari tiga komponen inti yang berbeda beserta metoda/ pendekatan yang digunakan. Ketiga komponen inti tersebut adalah klasifikasi pertanyaan, pencarian dokumen, dan ekstraksi jawaban. Hasil akhir dari survey ini adalah sebuah kontribusi untuk pengembangan penelitian di masa mendatang di domain MedQuAn khususnya untuk sistem tanya jawab medis dengan menggunakan bahasa Indonesia
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