Introducing the Big Knowledge to Use (BK2U) challenge

Visual comprehension and orientation into the COVID-19 CIDO ontology

The current intensive research on potential remedies and vaccinations for COVID-19 would greatly benefit from an ontology of standardized COVID terms. The Coronavirus Infectious Disease Ontology (CIDO) is the largest among several COVID ontologies, and it keeps growing, but it is still a medium sized ontology. Sophisticated CIDO users, who need more than searching for a specific concept, require orientation and comprehension of CIDO. In previous research, we designed a summarization network called "partial-area taxonomy" to support comprehension of ontologies. The partial-area taxonomy for CIDO is of smaller magnitude than CIDO, but is still too large for comprehension. We present here the "weighted aggregate taxonomy" of CIDO, designed to provide compact views at various granularities of our partial-area taxonomy (and the CIDO ontology). Such a compact view provides a "big picture" of the content of an ontology. In previous work, in the visualization patterns used for partial-area taxonomies, the nodes were arranged in levels according to the numbers of relationships of their concepts. Applying this visualization pattern to CIDO's weighted aggregate taxonomy resulted in an overly long and narrow layout that does not support orientation and comprehension since the names of nodes are barely readable. Thus, we introduce in this paper an innovative visualization of the weighted aggregate taxonomy for better orientation and comprehension of CIDO (and other ontologies). A measure for the efficiency of a layout is introduced and is used to demonstrate the advantage of the new layout over the previous one. With this new visualization, the user can "see the forest for the trees" of the ontology. Benefits of this visualization in highlighting insights into CIDO's content are provided. Generality of the new layout is demonstrated.

The use of SNOMED CT, 2013-2020: a literature review

OBJECTIVE

This article reviews recent literature on the use of SNOMED CT as an extension of Lee et al's 2014 review on the same topic. The Lee et al's article covered literature published from 2001-2012, and the scope of this review was 2013-2020.

MATERIALS AND METHODS

In line with Lee et al's methods, we searched the PubMed and Embase databases and identified 1002 articles for review, including studies from January 2013 to September 2020. The retrieved articles were categorized and analyzed according to SNOMED CT focus categories (ie, indeterminate, theoretical, pre-development, implementation, and evaluation/commodity), usage categories (eg, illustrate terminology systems theory, prospective content coverage, used to classify or code in a study, retrieve or analyze patient data, etc.), medical domains, and countries.

RESULTS

After applying inclusion and exclusion criteria, 622 articles were selected for final review. Compared to the papers published between 2001 and 2012, papers published between 2013 and 2020 revealed an increase in more mature usage of SNOMED CT, and the number of papers classified in the "implementation" and "evaluation/commodity" focus categories expanded. When analyzed by decade, papers in the "pre-development," "implementation," and "evaluation/commodity" categories were much more numerous in 2011-2020 than in 2001-2010, increasing from 169 to 293, 30 to 138, and 3 to 65, respectively.

CONCLUSION

Published papers in more mature usage categories have substantially increased since 2012. From 2013 to present, SNOMED CT has been increasingly implemented in more practical settings. Future research should concentrate on addressing whether SNOMED CT influences improvement in patient care.

Opportunity recognition efficiency of small Chinese firms: findings from data envelopment analysis

Purpose

Small firms in China anticipate entrepreneurial opportunities for continual growth. However, they may fail to recognize opportunities because of their inefficiency in managing their knowledge.

Design/methodology/approach

In this explorative paper, the authors assess the opportunity recognition efficiency of 168 small Chinese firms using data envelopment analysis (DEA). Supplementary Tobit regressions were conducted for further exploring the factors that influence the firms’ efficiency in opportunity recognition.

Findings

Results from the DEA suggest that most respondents recognize significantly fewer opportunities than those with equivalent knowledge stock. Moreover, many firms have low levels of pure technical efficiency but high levels of scale efficiency, indicating insufficient use of knowledge as a major reason for inefficiency in opportunity recognition. The Tobit regressions show that sales and research and development intensity are relevant to a firm’s opportunity recognition efficiency.

Research limitations/implications

This study calls for the investigation of efficiency issues in opportunity recognition and suggests that managers guard against unwarranted loss of opportunities owing to inefficient use of existing knowledge elements.

Originality/value

First, the authors introduce the concept of opportunity recognition efficiency within the entrepreneurial process. Second, they manifest the role of knowledge management in opportunity recognition. Third, they introduce DEA to investigate the relationship between knowledge stock and opportunity recognition. Fourth, this study reveals that inefficient use of knowledge is a disadvantage of small Chinese firms in terms of opportunity recognition.