Systematic reviews (SRs) are a crucial component of evidence-based clinical practice. Unfortunately, SRs are labor-intensive and unscalable with the exponential growth in literature. Automating evidence synthesis using machine learning models has been proposed but solely focuses on the text and ignores additional features like citation information. Recent work demonstrated that citation embeddings can outperform the text itself, suggesting that better network representation may expedite SRs. Yet, how to utilize the rich information in heterogeneous information networks (HIN) for network embeddings is understudied. Existing HIN models fail to produce a high-quality embedding compared to simply running state-of-the-art homogeneous network models. To address existing HIN model limitations, we propose SR-CoMbEr, a community-based multi-view graph convolutional network for learning better embeddings for evidence synthesis. Our model automatically discovers article communities to learn robust embeddings that simultaneously encapsulate the rich semantics in HINs. We demonstrate the effectiveness of our model to automate 15 SRs.
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Textual Evidence Mining via Spherical Heterogeneous Information Network Embedding
Scientific literature, as one of the major knowledge resources, provides abundant textual evidence that has great potential to support high-quality scientific hypothesis validation. In this paper, we study the problem of textual evidence mining in scientific literature: given a scientific hypothesis as a query triplet, find the textual evidence sentences in scientific literature that support the input query. A critical challenge for textual evidence mining in scientific literature is to retrieve high-quality textual evidence without human supervision. Because it is non-trivial to obtain a large set of human-annotated articles con-taining evidence sentences in scientific literature. To tackle this challenge, we propose EVIDENCEMINER, a high-quality textual evidence retrieval method for scientific literature without human-annotated training examples. To achieve high-quality textual evidence retrieval, we leverage heterogeneous information from both existing knowledge bases and massive unstructured text. We propose to construct a large heterogeneous information network (HIN) to build connections between the user-input queries and the candidate evidence sentences. Based on the constructed HIN, we propose a novel HIN embedding method that directly embeds the nodes onto a spherical space to improve the retrieval performance. Quantitative experiments on a huge biomedical literature corpus (over 4 million sentences) demonstrate that EVIDENCEMINER significantly outperforms baseline methods for unsupervised textual evidence retrieval. Case studies also demonstrate that our HIN construction and embedding greatly benefit many downstream applications such as textual evidence interpretation and synonym meta-pattern discovery.
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- NSF-PAR ID:
- 10279811
- Date Published:
- Journal Name:
- BigData'20: IEEE 2020 Int. Conf. on Big Data, Dec. 2020
- Volume:
- 2020
- Issue:
- 1
- Page Range / eLocation ID:
- 828 to 837
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData50022.2020.9377958
