Extracting a Knowledge Base of Mechanisms from COVID -19 Papers (2021)

Abstract: The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.

## related work mechanisms in science the concept of mechanisms, also referred to as functional relations, is fundamental across the sciences. for example mechanisms are described in biomedical ontologies @xcite @xcite , engineering @xcite , and across science. mechanisms can be natural (e.g., the mechanism by which amylase in saliva breaks down starch into sugar), artificial (electronic devices), non-physical constructs (algorithms, economic policies), and very often a blend (a pacemaker regulating the beating of a heart through electricity and ai algorithms). although seemingly intuitive, exact definitions of mechanisms are subject to debate in the philosophy of science @xcite ). an oxford dictionary definition of mechanisms refers to a natural or established process by which something takes place or is brought about. more intricate definitions discuss "complex systems producing a behavior", "entities and activities productive of regular changes", "a structure performing a function in virtue of its parts and operations", or the distinction between "correlative property changes" and "activity determining how a correlative change is achieved" @xcite . abstract definitions can help with generalization across many important types of mechanisms. the schema we propose (sec. 3) is inspired by such definitions, operationalizing them and making them more concrete, and also simple enough for models and human annotators to identify. ## mechanism relation schema we present a schema that builds upon and consolidates many of the types of mechanisms discussed in sec. 2. our defined schema has three key properties: (1) it uses a generalized concept of mechanism relations, capturing specific types of mechanisms in existing schema and extending them broadly; we formally define each mechanism as a relation (e 1 , e 2 , class) between entities e 1 and e 2 , where each entity e is a text span and the class indicates the type of the mechanism relation. entities all share a single common type and can be either natural (e.g., protein functions, viral mechanistic activities) or artificial (e.g., algorithms, devices), to capture the generality of the concepts in science (see fig. 2 ). we allow each entity to take part in multiple relations (tuples) within a given text, leading to a "mechanism graph". mechanisms are categorized into two coarse-grained classes: 2 direct mechanisms include activities of a mechanistic nature -actions explicitly performed by an entity, such as descriptions of a virus binding to a cell, and explicit references to a function (e.g., a use of a drug for treatment, or the use of ai for drug design as in fig. 1 ). indirect mechanisms include influences or associations without explicit mechanistic information or mention of a function (such as describing observed effects, without the process involved). these relations correspond more to "input-output correlations" @xcite , such as indicating that covid-19 may lead to economic impacts but not how (fig. 1 ), as opposed to direct mechanisms describing "inner workings" -revealing more of the intermediate states that lead from initial conditions (covid-19) to final states (price inflation) or explicitly describing a function. as an example for the utility of this distinction between direct and indirect relations, consider an md looking to generate a structured list of all uses of a treatment (direct mechanism), but not include side effects or complications (indirect). ## kb construction we describe our approach (depicted in fig. 3 ) for extracting a knowledge base of mechanisms using our unified schema. we first curate mechanic, an annotated dataset of general mechanisms from a small collection of scientific papers (sec. 4.1). we then train a model on our annotated data to extract mechanism relations from the entire cord-19 corpus of scientific papers; we use it to build comb, a knowledge base of mechanisms across the entire cord-19 corpus of (sec. 4.2), which supports semantic search for relations (sec. 4.3). ## conclusion we introduced a unified schema for mechanisms that generalizes across many types of activities, functions and influences. we constructed and distributed mechanic, a dataset of papers related to covid-19 annotated with this schema. we trained an ie model and applied it to covid-19 literature, constructing comb, a kb of 1.5m mechanisms. we showcased the utility of comb in structured search for mechanism relations in covid-19 literature. in a study with mds active in the fight against the disease, our system is rated higher than pubmed search for both utility and quality. our unified view of mechanisms can help generalize and scale the study of covid-19 and related areas. more broadly, we envision a kb of mechanisms that enables the transfer of ideas across the literature @xcite , such as by finding relationships between mechanisms in sars-cov-2 and other viruses, and assists in literature-based discovery @xcite by finding cross-document causal links.