KnowLife: A knowledge graph for health and life sciences

Erñst, Patrick; Meng, Cynthia; Siu, Amy; Weikum, Gerhard

doi:10.1109/icde.2014.6816754

Cited by 59 publications

(37 citation statements)

References 10 publications

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“…Another related study is KnowLife [5], which constructed a KB from life science publications and health-related social media content. Luo et al [6] proposed an algorithm that translates free-text sentences from pathology reports into a graph representation, where the nodes of the graph represent the concepts (e.g., genes and proteins) and the edges indicate the syntactic dependency links between these concepts.…”

Section: Related Workmentioning

confidence: 99%

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

Lossio-Ventura

Hogan

Modave

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Obesity has been linked to several types of cancer. Access to adequate health information activates people’s participation in managing their own health, which ultimately improves their health outcomes. Nevertheless, the existing online information about the relationship between obesity and cancer is heterogeneous and poorly organized. A formal knowledge representation can help better organize and deliver quality health information. Currently, there are several efforts in the biomedical domain to convert unstructured data to structured data and store them in Semantic Web knowledge bases (KB). In this demo paper, we present, OC-2-KB (Obesity and Cancer to Knowledge Base), a system that is tailored to guide the automatic KB construction for managing obesity and cancer knowledge from free-text scientific literature (i.e., PubMed abstracts) in a systematic way. OC-2-KB has two important modules which perform the acquisition of entities and the extraction then classification of relationships among these entities. We tested the OC-2-KB system on a data set with 23 manually annotated obesity and cancer PubMed abstracts and created a preliminary KB with 765 triples. We conducted a preliminary evaluation on this sample of triples and reported our evaluation results.

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Section: Related Workmentioning

confidence: 99%

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

Lossio-Ventura

Hogan

Modave

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…It is a disease symptom database developed from expert sources ([10], [15], [16]) where each disease is associated with 5 parameters (S, T, I, O, D) of RA data structure. E.g.…”

Section: Disease Symptom Databasementioning

confidence: 99%

“…As database fetched matrices are verified as true ([10], [15], [16]), values present in these matrices are considered as 1, and others are 0. If matrix size is not same for user query data matrix (Dq) and DB fetched data matrix, the empty rows are considered as complete mismatch.…”

Section: Sim (Mat_i Mat_ J) = Q / (Q+r +S) ----------------------(I)mentioning

confidence: 99%

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Automated Disease Prediction System (ADPS): A User Input-based Reliable Architecture for Disease Prediction

Laskar¹,

Hossain²,

Kamal³

et al. 2016

IJCA

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Rapid proliferation of Internet technology and handheld devices has opened up new avenues for online healthcare system. There are instances where online medical help or healthcare advice is easier or faster to grasp than real world help. People often feel reluctant to go to hospital or physician on minor symptoms. However, in many cases, these minor symptoms may trigger major health hazards. As online health advice is easily reachable, it can be a great head start for users. Moreover, existing online health care systems suffer from lack of reliability and accuracy. Herein, we propose an automated disease prediction system (ADPS) that relies on guided (to be described later) user input. The system takes input from the user and provides a list (topmost diseases have greater likelihood of occurrence) of probable diseases. The accuracy of ADPS has been evaluated extensively. It ensured an average of 14.35% higher accuracy in comparison with the existing solution.

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“…Taking China as an example, according to the survey published by CNNIC, the number of blog users in China is 109 million in 2014 with a 21 million increase from the user number in 2013. 1 However, as shown by the example in Fig.1, most blog pages contain irrelevant contents such as pop-up ads, decorative images, navigation links etc. 2 Eliminating these unrelated contents can help users better browse information they are interested in, especially for people with visual impairment, who access web contents via screen readers.…”

Section: Introductionmentioning

confidence: 99%

Effective Blog Pages Extractor for Better UGC Accessing

Kui¹,

Wang²,

Xia³

et al. 2016

2016 3rd International Conference on Information Science and Control Engineering (ICISCE)

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Blog is becoming an increasingly popular media for information publishing. Besides the main content, most of blog pages nowadays also contain noisy information such as advertisements etc. Removing these unrelated elements can improves user experience, but also can better adapt the content to various devices such as mobile phones. Though template-based extractors are highly accurate, they may incur expensive cost in that a large number of template need to be developed and they will fail once the template is updated. To address these issues, we present a novel template-independent content extractor for blog pages. First, we convert a blog page into a DOM-Tree, where all elements including the title and body blocks in a page correspond to subtrees. Then we construct subtree candidate set for the title and the body blocks respectively, and extract both spatial and content features for elements contained in the subtree. SVM classifiers for the title and the body blocks are trained using these features. Finally, the classifiers are used to extract the main content from blog pages. We test our extractor on 2,250 blog pages crawled from nine blog sites with obviously different styles and templates. Experimental results verify the effectiveness of our extractor.Comment: 2016 3rd International Conference on Information Science and Control Engineering (ICISCE

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KnowLife: A knowledge graph for health and life sciences

Cited by 59 publications

References 10 publications

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

Automated Disease Prediction System (ADPS): A User Input-based Reliable Architecture for Disease Prediction

Effective Blog Pages Extractor for Better UGC Accessing

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