An up-to-date knowledge-based literature search and exploration framework for focused bioscience domains

Kavuluru, Ramakanth; Thomas, Christopher; Sheth, Amit P.; Chan, Victor; Wang, Wenbo; Smith, Alan; Soto, Armando; Walters, Amy

doi:10.1145/2110363.2110396

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…al. [34] and called Scooner, that also leverages structured knowledge to facilitate LBD. Users can perform assertion-driven (semantic) browsing by first selecting one or more annotated concepts/subjects from one MEDLINE article, then selecting a relevant predicate (p) for which (s) is the subject, and finally selecting an object (o) grounded in a different MEDLINE article.…”

Section: Related Workmentioning

confidence: 99%

A graph-based recovery and decomposition of Swanson’s hypothesis using semantic predications

Cameron

Bodenreider

Yalamanchili

et al. 2013

Journal of Biomedical Informatics

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Objectives This paper presents a methodology for recovering and decomposing Swanson’s Raynaud Syndrome–Fish Oil Hypothesis semi-automatically. The methodology leverages the semantics of assertions extracted from biomedical literature (called semantic predications) along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations originally discovered manually by Swanson. Demonstrating that Swanson’s manually intensive techniques can be undertaken semi-automatically, paves the way for fully automatic semantics-based hypothesis generation from scientific literature. Methods Semantic predications obtained from biomedical literature allow the construction of labeled directed graphs which contain various associations among concepts from the literature. By aggregating such associations into informative subgraphs, some of the relevant details originally articulated by Swanson has been uncovered. However, by leveraging background knowledge to bridge important knowledge gaps in the literature, a methodology for semi-automatically capturing the detailed associations originally explicated in natural language by Swanson has been developed. Results Our methodology not only recovered the 3 associations commonly recognized as Swanson’s Hypothesis, but also decomposed them into an additional 16 detailed associations, formulated as chains of semantic predications. Altogether, 14 out of the 19 associations that can be attributed to Swanson were retrieved using our approach. To the best of our knowledge, such an in-depth recovery and decomposition of Swanson’s Hypothesis has never been attempted. Conclusion In this work therefore, we presented a methodology for semi- automatically recovering and decomposing Swanson’s RS-DFO Hypothesis using semantic representations and graph algorithms. Our methodology provides new insights into potential prerequisites for semantics-driven Literature-Based Discovery (LBD). These suggest that three critical aspects of LBD include: 1) the need for more expressive representations beyond Swanson’s ABC model; 2) an ability to accurately extract semantic information from text; and 3) the semantic integration of scientific literature with structured background knowledge.

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Section: Related Workmentioning

confidence: 99%

A graph-based recovery and decomposition of Swanson’s hypothesis using semantic predications

Cameron

Bodenreider

Yalamanchili

et al. 2013

Journal of Biomedical Informatics

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“…Due to excessive time consumption involved in manual curation, knowledge bases are generally not complete/exhaustive [41]. NLP approaches can be used to extract relations from particular sentences using the linguistic structure of a sentence (syntactic/dependency parse) especially involving the spans of named entities that occur in it [1, 12, 14, 16, 32]. Even though such systems are popular for relation extraction, they are error prone and might result in extraction of coincidental outcomes that cannot be considered general knowledge.…”

Section: Background: Knowledge Acquisition and Approachesmentioning

confidence: 99%

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Bakal

Talari

Kakani

et al. 2018

Journal of Biomedical Informatics

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Background Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. Objective To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. Methods We used 7,000 treats and 2,918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80%–20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Results Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. Conclusions We employed semantic graph patterns connecting pairs of candidate entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.

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“…In ongoing research, we developed Scooner [20], [21], which is a concept-based, user-driven approach to exploration. This approach is predicated on the view of a document, not only as a bag-of-words both also as a set-of-entities .…”

Section: Related Workmentioning

confidence: 99%

Semantic Predications for Complex Information Needs in Biomedical Literature

Cameron

Kavuluru

Bodenreider³

et al. 2011

2011 IEEE International Conference on Bioinformatics and Biomedicine

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Many complex information needs that arise in biomedical disciplines require exploring multiple documents in order to obtain information. While traditional information retrieval techniques that return a single ranked list of documents are quite common for such tasks, they may not always be adequate. The main issue is that ranked lists typically impose a significant burden on users to filter out irrelevant documents. Additionally, users must intuitively reformulate their search query when relevant documents have not been not highly ranked. Furthermore, even after interesting documents have been selected, very few mechanisms exist that enable document-to-document transitions. In this paper, we demonstrate the utility of assertions extracted from biomedical text (called semantic predications) to facilitate retrieving relevant documents for complex information needs. Our approach offers an alternative to query reformulation by establishing a framework for transitioning from one document to another. We evaluate this novel knowledge-driven approach using precision and recall metrics on the 2006 TREC Genomics Track.

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An up-to-date knowledge-based literature search and exploration framework for focused bioscience domains

Cited by 8 publications

References 20 publications

A graph-based recovery and decomposition of Swanson’s hypothesis using semantic predications

A graph-based recovery and decomposition of Swanson’s hypothesis using semantic predications

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Semantic Predications for Complex Information Needs in Biomedical Literature

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