Abstract: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 unstru… Show more
“…For the sake of completeness, we briefly describe the crucial components of the OC-2-KB system along with our new experiments. Please refer to [50] for more system details. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Based on a manually annotated sample corpus (i.e., 23 obesity and cancer abstracts), we previously adopted [50] 12 relation classes from the Relation Ontology (RO) [54]. We added an “other” category in this work to temporarily hold all predicates that cannot be classified into one of the 12 specific relation classes.…”
Section: Methodsmentioning
confidence: 99%
“…In our previous work [50], we created a IE software pipeline, Obesity and Cancer to Knowledge Base (OC-2-KB), aiming to build an evidence-based obesity and cancer Semantic Web KB. Using a set of NLP and machine learning techniques [51], OC-2-KB was able to automatically extract subject-predicate-object semantic triples from PubMed abstracts related to obesity and cancer.…”
Section: Introductionmentioning
confidence: 99%
“…The primary contributions of this work in comparison to our previous study [50], are detailed below:
We conducted two rounds of crowdsourcing experiments to assess the feasibility of adding human knowledge to the KB construction process.We extended our original corpus from 23 random obesity and cancer related Pubmed abstracts to 82 abstracts from systematic review papers. We curated a preliminary obesity and cancer knowledge base (OCKB) with these abstracts using OC-2-KB and evaluated the query results against this OCKB.…”
BackgroundThere is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organized, not evidenced-based, of poor quality, and confusing to health information consumers. A formal knowledge representation such as a Semantic Web knowledge base (KB) can help better organize and deliver quality health information. We previously presented the OC-2-KB (Obesity and Cancer to Knowledge Base), a software pipeline that can automatically build an obesity and cancer KB from scientific literature. In this work, we investigated crowdsourcing strategies to increase the number of ground truth annotations and improve the quality of the KB.MethodsWe developed a new release of the OC-2-KB system addressing key challenges in automatic KB construction. OC-2-KB automatically extracts semantic triples in the form of subject-predicate-object expressions from PubMed abstracts related to the obesity and cancer literature. The accuracy of the facts extracted from scientific literature heavily relies on both the quantity and quality of the available ground truth triples. Thus, we incorporated a crowdsourcing process to improve the quality of the KB.ResultsWe conducted two rounds of crowdsourcing experiments using a new corpus with 82 obesity and cancer-related PubMed abstracts. We demonstrated that crowdsourcing is indeed a low-cost mechanism to collect labeled data from non-expert laypeople. Even though individual layperson might not offer reliable answers, the collective wisdom of the crowd is comparable to expert opinions. We also retrained the relation detection machine learning models in OC-2-KB using the crowd annotated data and evaluated the content of the curated KB with a set of competency questions. Our evaluation showed improved performance of the underlying relation detection model in comparison to the baseline OC-2-KB.ConclusionsWe presented a new version of OC-2-KB, a system that automatically builds an evidence-based obesity and cancer KB from scientific literature. Our KB construction framework integrated automatic information extraction with crowdsourcing techniques to verify the extracted knowledge. Our ultimate goal is a paradigm shift in how the general public access, read, digest, and use online health information.
“…For the sake of completeness, we briefly describe the crucial components of the OC-2-KB system along with our new experiments. Please refer to [50] for more system details. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Based on a manually annotated sample corpus (i.e., 23 obesity and cancer abstracts), we previously adopted [50] 12 relation classes from the Relation Ontology (RO) [54]. We added an “other” category in this work to temporarily hold all predicates that cannot be classified into one of the 12 specific relation classes.…”
Section: Methodsmentioning
confidence: 99%
“…In our previous work [50], we created a IE software pipeline, Obesity and Cancer to Knowledge Base (OC-2-KB), aiming to build an evidence-based obesity and cancer Semantic Web KB. Using a set of NLP and machine learning techniques [51], OC-2-KB was able to automatically extract subject-predicate-object semantic triples from PubMed abstracts related to obesity and cancer.…”
Section: Introductionmentioning
confidence: 99%
“…The primary contributions of this work in comparison to our previous study [50], are detailed below:
We conducted two rounds of crowdsourcing experiments to assess the feasibility of adding human knowledge to the KB construction process.We extended our original corpus from 23 random obesity and cancer related Pubmed abstracts to 82 abstracts from systematic review papers. We curated a preliminary obesity and cancer knowledge base (OCKB) with these abstracts using OC-2-KB and evaluated the query results against this OCKB.…”
BackgroundThere is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organized, not evidenced-based, of poor quality, and confusing to health information consumers. A formal knowledge representation such as a Semantic Web knowledge base (KB) can help better organize and deliver quality health information. We previously presented the OC-2-KB (Obesity and Cancer to Knowledge Base), a software pipeline that can automatically build an obesity and cancer KB from scientific literature. In this work, we investigated crowdsourcing strategies to increase the number of ground truth annotations and improve the quality of the KB.MethodsWe developed a new release of the OC-2-KB system addressing key challenges in automatic KB construction. OC-2-KB automatically extracts semantic triples in the form of subject-predicate-object expressions from PubMed abstracts related to the obesity and cancer literature. The accuracy of the facts extracted from scientific literature heavily relies on both the quantity and quality of the available ground truth triples. Thus, we incorporated a crowdsourcing process to improve the quality of the KB.ResultsWe conducted two rounds of crowdsourcing experiments using a new corpus with 82 obesity and cancer-related PubMed abstracts. We demonstrated that crowdsourcing is indeed a low-cost mechanism to collect labeled data from non-expert laypeople. Even though individual layperson might not offer reliable answers, the collective wisdom of the crowd is comparable to expert opinions. We also retrained the relation detection machine learning models in OC-2-KB using the crowd annotated data and evaluated the content of the curated KB with a set of competency questions. Our evaluation showed improved performance of the underlying relation detection model in comparison to the baseline OC-2-KB.ConclusionsWe presented a new version of OC-2-KB, a system that automatically builds an evidence-based obesity and cancer KB from scientific literature. Our KB construction framework integrated automatic information extraction with crowdsourcing techniques to verify the extracted knowledge. Our ultimate goal is a paradigm shift in how the general public access, read, digest, and use online health information.
“…Lossio-Ventura et al . 20 introduced a pipeline to build an obesity and cancer knowledge base. Very recently, Lossio-Ventura et al also noticed the reliability issue of knowledge base, so they further proposed to incorporate cross-sourcing process to improve the reliability of the their previously developed knowledge base.…”
† Equal Contribution ‡ The work is done while the author is at CMU.The increasing amount of scientific literature in biological and biomedical science research has created a challenge in the continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answers to this challenge. In this paper, we aim to further improve the reliability of biomedical text-mining by training the system to directly simulate the human behaviors such as querying the PubMed, selecting articles from queried results, and reading selected articles for knowledge. We take advantage of the efficiency of biomedical text-mining, the flexibility of deep reinforcement learning, and the massive amount of knowledge collected in UMLS into an integrative artificial intelligent reader that can automatically identify the authentic articles and effectively acquire the knowledge conveyed in the articles. We construct a system, whose current primary task is to build the genetic association database between genes and complex traits of the human. Our contributions in this paper are three-fold: 1) We propose to improve the reliability of text-mining by building a system that can directly simulate the behavior of a researcher, and we develop corresponding methods, such as Bi-directional LSTM for text mining and Deep Q-Network for organizing behaviors. 2) We demonstrate the effectiveness of our system with an example in constructing a genetic association database. 3) We release our implementation as a generic framework for researchers in the community to conveniently construct other databases.
The increasing amount of scientific literature in biological and biomedical science research has created a challenge in continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answers to this challenge. In this paper, we aim to further improve the reliability of biomedical text-mining by training the system to directly simulate the human behaviors such as querying the PubMed, selecting articles from queried results, and reading selected articles for knowledge. We take advantage of the efficiency of biomedical text-mining, the flexibility of deep reinforcement learning, and the massive amount of knowledge collected in UMLS into an integrative artificial intelligent reader that can automatically identify the authentic articles and effectively acquire the knowledge conveyed in the articles. We construct a system, whose current primary task is to build the genetic association database between genes and complex traits of human. Our contributions in this paper are threefold: 1) We propose to improve the reliability of text-mining by building a system that can directly simulate the behavior of a researcher, and we develop corresponding methods, such as Bi-directional LSTM for text mining and Deep Q-Network for organizing behaviors. 2) We demonstrate the effectiveness of our system with an example in constructing a genetic association database. 3) We release our implementation as a generic framework for researchers in the community to conveniently construct other databases.
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