Research on the Method of Extracting Domain Knowledge From the Freebase RDF Dumps

Chen, Deyan; Zhao, Hong

doi:10.1109/access.2018.2868516

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…However, extracting domain knowledge from the Freebase RDF dumps on a particular domain, such as the medicine domain, will face many difficulties. With the complete understanding of Freebase-related concepts, Freebase's knowledge representation model, and the structural features of the Freebase RDF dumps, a method for extracting domain knowledge from the Freebase RDF dumps is proposed and implemented [56]. The method ensures a fast, precious, complete knowledge extraction over one or more domains from the Freebase RDF dumps.…”

Section: Domain Semantic Knowledge Base Constructionmentioning

confidence: 99%

Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases

2024

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The health care domain is a knowledge-intensive domain. The quality of clinical diagnosis relies mainly on the medical knowledge and experience held by doctors. However, the ability of a single doctor is very limited, so the quality of clinical diagnosis is currently not high. In this paper, an aided diagnosis method based on domain semantic knowledge bases is proposed. Firstly, a domain semantic knowledge base is established by extracting and refining the knowledge of the medicine subject matter domain from the Freebase RDF dumps. Then, based on the semantic knowledge base, the algorithms for calculating the weights of the symptoms in the knowledge base, the relative weights of the diseases related to the input symptom set from a patient, and the related symptom set related to the input symptom set from the patient are proposed. Finally, the clinical medical record data of several common diseases are selected to make an evaluation on the proposed method. For each medical record, the symptom information is extracted from the chief complaint as the patient's input symptom set. Based on the input symptom set, the method of this paper is used to obtain the list of related diseases and the ranking of disease relative weights. From the disease relevance rankings, the Top 1 (first diagnosis) and Top-3 (first 3 diagnoses) are compared with the doctor's diagnoses in the medical records. Among them, ovarian cyst has the highest Top-1 and Top-3 hit rates of 67.3% and 89.1%, respectively. Followed by acute upper respiratory tract infection, Top-1 and Top-3 hit rates are 56.6% and 85.2%, respectively. The average Top-1 and Top-3 hit rates are 47.9% and 79.7%, respectively. Compared with the relevant methods, the method of this paper is better. The evaluation results show that based on the domain semantic knowledge base and the aided diagnosis method of diseases constructed in this paper, it is possible to provide aided diagnosis services of a large number of common diseases for general practitioners (especially inexperienced doctors) at the grassroots level as well as selfdiagnosis services of diseases for patients.

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Section: Domain Semantic Knowledge Base Constructionmentioning

confidence: 99%

Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases

2024

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“…The knowledge extraction methodology is proposed to retrieve the domain knowledge from the freebase RDF dumps, this methodology addressed the challenges while retrieving the triples from freebase triples. The main drawback of this methodology is that the intelligent technique is not utilized for the extraction of objects from triples [16]. The proposed methodology tries to remove semantic, syntactic, and structural heterogeneity of homogeneous domain databases using a data turn and query turn scheme [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

Shanmurthy

Poongodi

Balusamy

et al. 2022

IJEECS

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Semantic web technology is adapted to the internet of things (IoT) for web - based applications to globally connect the services. Web ontology language (OWL) domain ontology is a powerful machine - readable language for domain knowledge representation. The developer stored the IoT application relevant ontology in a repository or catalogue. Hence, IoT application - related ontology files are available for reus e, but many of the IoT application - relevant ontology files are publicly not available or inaccessible. The proposed idea is to extract the contextual knowledge of IoT applications that contain inaccessible ontology files. The context - wise specific domain I oT applications are not obtainable, hence respective ontology - based research papers are identified and their frequent terms are computed. The selected contextual dominant frequent terms from the transport domain are passed into the skip - gram flavour of wor d2vector modelled n atural language processing ( NLP ) corpus which produces most similar terms. The domain experts select the appropriate terms to annotate in OWL ontology for contextual knowledge augmentation. Finally, 1422 contextual terms were generated b ased on dominant terms of selected IoT applications.

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SEGAR: Knowledge Graph Augmented Session-Based Recommendation

Tang

2021

Knowledge Science, Engineering and Management

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Research on the Method of Extracting Domain Knowledge From the Freebase RDF Dumps

Cited by 5 publications

References 12 publications

Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases

Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

SEGAR: Knowledge Graph Augmented Session-Based Recommendation

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