1,720,974 research outputs found
Construction of Disease-Symptom Knowledge Graph from Web-Board Documents
No full textThe research aim is to construct a disease-symptom knowledge graph (DSKG) as a cause-effect knowledge graph containing disease-symptom relations as a cause-effect relation type determined from downloaded documents on medical web-board resources. Each disease-symptom relation connects a disease-name concept node (a causative-concept node) to a corresponding node having a group of correlated symptom-concept/effect-concept features as common symptom-concept/effect-concept features among some disease-name concepts. The DSKG benefits non-professionals in preliminary diagnosis through a recommender web-board. There are three main problems: how to determine symptom concepts from sentences without annotation on the documents having disease-name concepts as the documents’ topic-names; how to determine the disease-symptom relations from the documents with/without complications; and how to construct the DSKG involving high dimensional symptom-concept features after union of the correlated symptom-concept groups. Therefore, we apply a word co-occurrence pattern including medical-symptom expressions from Wikipedia including MeSH and the Lexitron Dictionary to determine the symptom concepts. The Cartesian product is applied for automatic-supervised machine learning to determine the disease-symptom relation. We propose using Principal Component Analysis for constructing the DSKG by dimensionality reduction in the symptom-concept features with minimized information loss. In contrast to previous works, the proposed approach enables the DSKG construction with precise and concise representation scores of 7.8 and 9, respectively
Herbal Medicinal-Property Knowledge Extraction from Text for Knowledge Base Construction
No full textComparative Study of Using Word Co-occurrence to Extract Disease Symptoms from Web Documents
No full text
Causal Pathway Extraction from Web-Board Documents
No full textThis research aim is to extract causal pathways, particularly disease causal pathways, through cause-effect relation (CErel) extraction from web-board documents. The causal pathways benefit people with a comprehensible representation approach to disease complication. A causative/effect-concept expression is based on a verb phrase of an elementary discourse unit (EDU) or a simple sentence. The research has three main problems; how to determine CErel on an EDU-concept pair containing both causative and effect concepts in one EDU, how to extract causal pathways from EDU-concept pairs having CErel and how to indicate and represent implicit effect/causative-concept EDUs as implicit mediators with comprehension on extracted causal pathways. Therefore, we apply EDU’s word co-occurrence concept (wrdCoc) as an EDU-concept and the self-Cartesian product of a wrdCoc set from the documents for extracting wrdCoc pairs having CErel into a wrdCoc-pair set from the documents after learning CErel on wrdCoc pairs by supervised-machine learning. The wrdCoc-pair set is used for extracting the causal pathways by wrdCoc-pair matching through the documents. We then propose transitive closure and a dynamic template to indicate and represent the implicit mediators with the explicit ones. In contrast to previous works, the proposed approach enables causal-pathway extraction with high accuracy from the documents
Touristic Destinations’ Theme Determination for GIS Applications
No full textThis research aims to determine touristic destination’s theme (especially tourism activity theme) from the tourism web documents for geographic information system (GIS) applications, i.e. guiding the main interesting tourism activities to tourists. There are two major problems of the theme acquisition; tourism activity extraction and tourism activity generalization. Therefore, this research proposes of using Naïve Bayes Classifier to determine word co-occurrences between verbs and nouns with the tourism activity concept from web documents. Furthermore, this research also applies the fuzzy concept along with the imputation technique, to determine the tourism activity theme by generalizing the extracted tourism activity. The result of the tourism activity extraction shows successfully the precision and recall of 85% and 77%, respectively, with Mean Reciprocal Rank (MRR) of the tourism activity theme is 0.5.</jats:p
- …
