• Journal of Food Hygiene and Safety
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• v.27 no.4
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• pp.375-387
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• 2012

The objective of this study is investigation of contamination levels and assessment of health risk effects of heavy metals in herbal pills. 31 Items and 93 samples were obtained for this investigation from major herbal medicine producing areas, herbal markets and on-line supermarkets from Jan to Jun in 2010. Inductively coupled plasma mass spectrometer method was conducted for the quantitative analysis of Pb, Cd and As. In addition, the mercury analyzer system was conducted for that of Hg without sample digestion. The average contents of heavy metals in samples were as follows : 0.87 mg/kg for Pb, 0.08 mg/kg for Cd, 2.87 mg/kg for As and 0.16 mg/kg for Hg, respectively. In addition, the average contents of heavy metals in different parts of plants, including cortex, fructus, herba, radix, seed, algae and others were 0.63 mg/kg, 3.94 mg/kg, 1.42 mg/kg, 1.05 mg/kg, 0.16 mg/kg, 22.31 mg/kg and 10.17 mg/kg, respectively. After the estimations of dietary exposure, the acceptable daily intake (ADI), the average daily dose (ADD), the provisional tolerable weekly intake (PTWI) and the relative hazard of heavy metals were evaluated. As the results, the relative hazards compared to PTWI in samples were below the recommended standard of JECFA as Pb 3.1%, Cd 0.9%, Hg 0.5%. Cancer risks through slope factor (SF) by Ministry of Environment Republic Korea and Environmental Protection Agency was $4.24{\times}10^{-7}$ for Pb and $3.38{\times}10^{-4}$ for As (assuming that the total arsenic content was equal to the inorganic arsenic). Based on our results, possible Pb-induced cancer risks in herbal pills according to parts used including cortex, fructus, herba, radix, seed, algae and others were $1.95{\times}10^{-7}$, $1.45{\times}10^{-6}$, $2.14{\times}10^{-7}$, $6.27{\times}10^{-7}$, $1.99{\times}10^{-8}$, $3.61{\times}10^{-7}$ and $9.64{\times}10^{-8}$, respectively. Possible As-induced cancer risks in herbal pills by parts used including cortex, fructus, herba, radix, seed, algae and others were $1.54{\times}10^{-5}$, $7.24{\times}10^{-5}$, $1.23{\times}10^{-4}$, $2.02{\times}10^{-5}$, $3.25{\times}10^{-6}$, $2.18{\times}10^{-3}$ and $5.67{\times}10^{-6}$ respectively. Taken together, these results indicate that the majority of samples except for some samples with relative high contents of heavy metals were safe.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.