Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context


In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept “knowledge unit” and “knowmetrics” for measuring knowledge. However, the definition on “computable knowledge object” remains controversial so far in different fields. For example, it is defined as (1) quantitative scientific concept in natural science and engineering, (2) knowledge point in the field of education research, and (3) semantic predications, i.e., Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, provides a basic data infrastructure for measuring medical knowledge. In general, the study of extracting SPO triples as computable knowledge unit from unstructured scientific text has been overwhelmingly focusing on scientific knowledge per se. Since the SPO triples would be possibly extracted from hypothetical, speculative statements or even conflicting and contradictory assertions, the knowledge status (i.e., the uncertainty), which serves as an integral and critical part of scientific knowledge has been largely overlooked. This article aims to put forward a framework for Medical Knowmetrics using the SPO triples as the knowledge unit and the uncertainty as the knowledge context. The lung cancer publications dataset is used to validate the proposed framework. The uncertainty of medical knowledge and how its status evolves over time indirectly reflect the strength of competing knowledge claims, and the probability of certainty for a given SPO triple. We try to discuss the new insights using the uncertainty-centric approaches to detect research fronts, and identify knowledge claims with high certainty level, in order to improve the efficacy of knowledge-driven decision support.


Knowledge metrics, or knowmetrics, or epistometrics (a form of epistemology) is an interdisciplinary research fields on measuring knowledge instead of information (Hou et al. 2009) (Galyavieva 2013). In general, metrics are defined as a combined whole of quantitative techniques associated with a specialized field of scientific knowledge, in order to obtain descriptive, evaluative or prospective results from the activities or phenomena analysis (Cavaller 2008). In contrast to other metric disciplines such as bibliometrics, scientometrics, and informetrics, the concept of knowmetrics was initially introduced for the quantitative analysis on “element of knowledge” by Chinese scientometricians Prof. Hongzhou Zhao and Guo Hua Jiang (Zhao and Jiang 1984), as well as Prof. Zeyuan Liu (Liu 1999). Taking the entire system of human knowledge as its research subject, knowmetrics is an emerging subject that carries out a comprehensive study of knowledge capacity of the society and the social connection of knowledge through such methods as quantitative analysis and computing technology (Liu and Liu 2002). However, this definition only covers the general research paradigms based on traditional approach in branches of science of science and scientometrics. It involves little of the methodology of measuring knowledge units, which is the key to knowmetric research as is stated by Prof. Liu and his colleagues (Hou et al. 2009).

In fact, there is no consensus on the definition of knowmetrics. We think this concept has evolved and been promoted by three communities generally. The first is from the macro-level by the science of science community, who concentrates on knowmetrics and its application of the measurement of knowledge economies. The second is from the mecro-level by the community of scientometrics and informatics, who focus on the science knowledge graph for mapping science domains and measuring knowledge structure in science or in a research field (Chen 2006b; Borner et al. 2003). And the third is from the micro-level by the medical informatics community who combined the medical knowledge organization systems (such as MeSH, UMLS) and the computational techniques for implicit knowledge discovery in biomedical sciences (Keselman et al. 2010; Bakal et al. 2018). Both the second and the third community’s concerns are scientific publications mining. Specifically, the former focuses on the analysis of bibliographical data from scientific publications, while the latter emphasizes on (1) the “knowledge unit”, in terms of Subject-Predicate-Object (SPO) triples extracted from the scientific text (Kilicoglu et al. 2020), or (2) the “computable knowledge object”, expressed in code such as disease prediction models, learned from big data (Friedman and Flynn 2019; Flynn et al. 2018). The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, provides a basic data infrastructure for measuring medical knowledge at the level of knowledge units (Kilicoglu et al. 2012). In general, the study of extracting SPO triples as computable knowledge unit from unstructured scientific text has been overwhelmingly focusing on scientific knowledge per se. However, the status of scientific knowledge, i.e., the uncertainty, which serves as an integral and critical part of scientific knowledge, has been largely overlooked.

Uncertain scientific knowledge refers to the knowledge comes from hypothetical, speculative statements, or even conflicting and contradictory assertions, which are critical to understand the incremental and transformative development of scientific knowledge. The study and measurement of uncertainty of scientific knowledge and how uncertainty was expressed in scientific texts opens up a new area in the study in scientometrics and informetrics (Chen and Song 2017c). (Evans and Foster 2011) introduced the concept of “metaknowledge”, i.e., knowledge about knowledge. The growth of electronic publication and informatics archives makes it possible to harvest vast quantities of metaknowledge. The computational production and consumption of metaknowledge will allow researchers and policymakers to leverage more scientific knowledge—explicit, implicit, and contextual, in their efforts to advance science, such as recalibrate scientific certainty in particular propositions. Inspired by (Kuhn and Hacking, 2012), the evolutionary process of science can also be understood from the perspective of uncertainty of knowledge, which is evolving over time. Essentially, incremental science can be understood for a given unsolved scientific issue progressing from a wholly unknown state, to hypotheses or speculations, and then verifying the uncertainty surrounding the hypothesis, until reaching a reasonable conclusion which has a considerable proportion of evidence. Transformative science involves raising conflicting and contradictory interpretations with previous knowledge, leading to scientific disputes, and steadily promoting revolution. One extreme of uncertainty is “entirely unknown”; the other is “generally accepted as fact”. There are mainly two forms of expression in between: one is hypothesis and speculation (i.e., hedging in language), the other is contradictory and controversy, corresponding to incremental and transformative development of science, respectively.

Uncertain knowledge is particularly common in medical sciences. Recently, two independent studies investigated the frequency of textual uncertainty cues, such as “may”, “might”, “could”, as well as “controversial”, “contradictory”, and “conflicting”, in all sentences or only citing sentences in Elsevier’s full text database. They have found that after social sciences, medical sciences rank second according to the frequency of uncertain text, and medical sciences have the most “disagreement”, i.e., controversial scientific claims (Murray et al. 2019; Chen et al. 2018). It is not rare in medical practices to encounter “medical reversals”, in which prior studies that claimed some therapeutic benefit were contradicted by subsequent research (Prasad et al. 2013). Through an analysis of more than 3000 randomized controlled trials (RCTs) published in three leading medical journals (the Journal of the American Medical Association, the Lancet, and the New England Journal of Medicine), (Herrera-Perez et al. 2019) have identified 396 medical reversals. The reality in medical practice is that doctors continually make decisions on the basis of imperfect data and limited knowledge, which may lead to diagnostic uncertainty, coupled with the uncertainty that arising from unpredictable patient responses to treatment and from health care outcomes that are far from binary. (Simpkin and Schwartzstein 2016) believe that a shift toward the acknowledgment and acceptance of uncertainty of medical knowledge is essential—for physicians, for patients, and for health care system as a whole.

Scientific publications can be seen as records of knowledge claims on a research question, supported by empirical evidence. By examining the linguistic characteristics exposed in scientific texts, it is possible to quantitatively measure the uncertainty of scientific knowledge. Removing redundant part of scientific text and extracting structured knowledge unit is the key to realize intelligent knowledge mining and promote knowledge translation from research to action, but this process often ignores the representation of knowledge status, i.e., the uncertainty. This article aims to propose a framework for “Medical Knowmetrics” using the SPO triples as the knowledge unit and the uncertainty as the knowledge context. The lung cancer publications dataset is used to validate the framework.

Related work

We summarized recent advances from two aspects. The first is to identify uncertain cue words in biomedical scientific text; the second refers to extracting machine-understandable knowledge from unstructured biomedical text.

Identifying uncertain cue words in biomedical scientific text

The research on identifying uncertainty expressions in scientific literature started in the late 1990 s (Hyland 1996). It was first used in the scientific writing field for analyzing 63 hedging cue words, which indicate that the author’s scientific judgments are subjectively cautious and cover about 11% of the sentences in PubMed abstracts (Light et al. 2004). During 2010 s, it was continually investigated by the community of computational linguistics (Zerva 2019; Thompson et al. 2011; Szarvas et al. 2012), which treated hedging as a linguistic phenomenon and used computer science for automatic recognition. In the past three years, the community of informatics has begun to converge on the measurement of uncertainty in science and pay more attention to the distribution of uncertain cue words in full-text (Chen and Song 2017b). They also extend the scope of textual uncertainty from only hedging cues by computational linguists to a broad-coverage of uncertainty such as controversial, inconsistence and contradiction contained in scientific text (Chen et al. 2018).

A few studies have been carried out using the full-text of scientific publications. For instance, (Mercer et al. 2004) took the full-text of the 985 BioMed Central papers as corpus and found that the hedging words described by (Hyland 1996) are more likely to appear in citing sentences. Recently, using citing sentences and the cited publications in PubMed Central full-text database, (Small 2018) proposed a measure called hedging rate of a given publication, which is defined as the proportion of citing sentences with the three most prominent hedging terms “may”, “could” and “might” in all citing sentences. He found that method papers have a lower hedging rate than non-method papers, which means method paper in general has higher certainty than non-method paper. In general, rates of hedging are found to be higher for papers with fewer citances, suggesting that the certainty of scientific results is directly related to citation frequency, and early citing sentences will have a higher hedging rate than later citing sentences (Small et al. 2019). By comparing word usage of citing sentences for low-hedged and highly hedged papers, it was found that low-hedged, or high certainty, papers were associated with action verbs denoting the application of methods and acquisition of data, and words specifically denoting quantitative methods (e.g., “using”, “performed”, …). High-hedged, or low certainty, papers, on the other hand, were associated with words denoting interpretation and justification of ideas (e.g., “suggest”, “evidence”, …) (Small 2019). Most recently, (Small 2020) suggests a new direction for quantitative science studies: it will be important for quantitative science studies to address confirmation in science and the role of evidence in that process by approaching confirmation as a bibliometric problem.

Disagreement or dispute is one type of uncertainty of scientific knowledge. Dispute in science is central to the production of new knowledge. (Murray et al. 2019) recently developed a methodology for investigating disagreement in science based on citing sentences from the Elsevier ScienceDirect database. They focus on citation sentences containing the disagreement signal phrases “conflict” or “contradict”, which occur alongside filter phrases “studies” or “results”. We noticed that the authors employed combinations of cue words to identify “disagreement” statements. Disagreement filter phrases must appear within a four-word window of the signal. (Atanassova et al. 2018) also argued that the task of identifying uncertain sentences cannot be accomplished by recognizing cue words only. And a terms-combination strategy is also recommended for the detection of speculative statements in scientific text (Malhotra et al. 2013). Supplementing additional words as filters will enhance the precision of uncertain sentence recognition.

Extracting machine-understandable knowledge from unstructured biomedical text

Firstly, the advancement in semantic knowledge representation provides insights for the research to be carried out in this paper. SemRep is a rule-based natural language processing system, based on (1) medical concepts, (2) concept types (e.g., drugs, diseases), and (3) semantic relationships between concepts (e.g., drugs-TREAT-diseases) in Unified Medical Language System (UMLS). Using SemRep, one can extract SPO triples from biomedical text. In 2012, the project team developed SemMedDB to provide a basic data infrastructure for measuring medical knowledge, which stored the semantic predications and the corresponding sentences extracted from the title and abstract of PubMed publications based on SemRep (Kilicoglu et al. 2012). Although SemRep and SemMedDB accomplish the extraction and storage of large-scale structured knowledge units, they lose most of the meta-knowledge, especially the uncertainty status of scientific assertions. This enables the development of computable medical knowledge units in this work.

The second is the “nano-publication” model (Groth et al. 2010). The meaning of “nano” here refers to the smallest knowledge unit which is machine-readable. It includes three parts: (a) assertion expressed by subject-predicate-object triples, (b) source information, and (c) publication information, which is the metadata about nano-publication itself, including the creator, creation date and the version of the nano-publication. The third is a micropublication model proposed by (Clark et al. 2014). The minimal form of a micropublication is a statement with its attribution. The maximal form is a statement with its complete supporting argument, consisting of all relevant evidence, interpretations, discussion and challenges brought forward in support of or opposition to it.

However, each of the above three models have advantages and disadvantages. First, SemRep and SemMedDB accomplish the extraction and storage of large-scale structured knowledge units, but lose most of the meta-knowledge, especially the uncertainty status of scientific assertions. For example, scholars tend to modify their conclusions to express them as objectively as possible. Assertions may be speculations and not facts, and they even may come from conflicting, contradictory, and controversial statements. It's the same as nano-publication, which enables computable knowledge units that can be traced by assigning unique IDs. However, it does not indicate whether the scientific assertion is a conclusive claim from the current new experiment, or only cite a previous scientific assertion, or simply a hypothetical statement. Second, while micropublications enable us to formalize the arguments and evidence in scientific publications, but the arguments, or the core assertions, are statements in natural language sentence and are not structured thus not computable.

Methodology and framework

A representation model for computable medical knowledge objects

The framework is built on a key aspect of scientific papers: claims. Claims are natural language sentences in a scientific paper that expresses a relationship between two entities. In particular, how one of them affects, manipulates, or causes the other entity. Firstly, we introduce the concept of basic knowledge unit (Fig. 1), which is defined as one identical SPO triple derived from multiple source sentences to produce a new measure that represents a consensus across various claims. For example, we can extract one triple “Hydroxychloroquine TRAETS Covid-19” in more than two claims, such as the conclusive sentences in the end of the abstract that are from various PubMed articles. Basic knowledge unit are thus much less abundant than individual knowledge claims. In principle, each basic knowledge unit exists only once (as a unit of assertion) and it is “associated” with multiple, potentially many thousands of instances of claims that assert the same, but differ in provenance. And we propose a representation model for computable medical knowledge objects (Fig. 2), which consists of four elements: (1) the unique ID, (2) basic knowledge unit, (3) supporting sentences as the knowledge sources, and (4) uncertainty as the knowledge status.

Fig. 1

The concept of basic knowledge unit

Fig. 2

A representation model for computable medical knowledge objects

We classified the uncertainty of medical knowledge into three levels, ‘hedging’, ‘diversity’ and ‘controversy/contradiction’. Hedging is a particularly relevant concept in understanding how scientists characterize the tentative and context-dependent nature of scientific claims. Commonly used hedging words include “may”, “could”, and “might”. Diversity refers to such claims that connect the same entities but with different associations that not necessarily contradict each other. The idea here is to capture the presence of scientific claims that may deserve further investigation because they have different semantics although they share almost the same context. The difference between contradiction and controversy is that contradiction is the act of contradicting while controversy is a debate, and discussion of opposing opinions and conflicting results. The case of controversy/contradiction refers to that there are at least two claims with the same pairs of entities that have opposite semantic orientation. The textual uncertainty can be detected (1) within one-sentence to identify apparent hedging using cue words such as may, could, and might, as well as apparent controversy/contradiction using cue words such as conflicting, controversial, and contradictory, and (2) from cross-sentences to identify inapparent diversity, and inapparent controversy/contradiction using the semantic orientation of knowledge claims in the documents.

A framework for extracting uncertain knowledge

Our classification here focuses on two core uncertain aspects: (1) controversy/contradiction, and (2) diversity. Controversy arises when two or more claims semantically contradict each other; diversity means the presence of different semantics of the claims that do not contradict each other but provide different insights. We use SemRep to extract SPO triples in abstracts of PubMed publications, and store the SPO triples together with their corresponding sentences in our local databases, which formed the basis of our study.

Using SemRep to extract SPO triples from sentences in abstracts of PubMed publications

SemRep is a well-known knowledge-based semantic relation interpreter developed by the National Library of Medicine (NLM), with semantic relation structured in the form of SPO triple. Briefly, SemRep identifies semantic triples by interpreting the scientific text, which exploits syntactic analysis in natural language processing and structured domain knowledge from UMLS with three knowledge sources. Currently, SemRep facilities the semantic relation extraction from PubMed database by setting MEDLINE format as default of input text, which ensures it a powerful tool for knowledge mining. SemRep has not been formally evaluated, due to the lack of a gold standard corpus; however, many task-based evaluations have been reported that SemRep generates some errors with its precision scores varied between 75 and 96% (Rindflesch and Fiszman 2003; Kilicoglu et al. 2012).

Identifying knowledge with controversy/contradiction

We use two approaches to discovering controversial or contradictory knowledge. The first is directly searching such sentences which contain three prominent cue terms “controversial”, “contradictory”, and “conflicting” inspired by (Murray et al. 2019).

The second approach is using the semantic orientation on the rule that, (1) two or more SPO triples are extracted in scientific claims from various sources, and (2) they have the same subject (e.g., drug) and object (e.g., disease) but opposite predicates. In order to define opposite predicates, we classify the predicates into 3 groups: Excitatory, Inhibitory and General (Table 1). After examining the 58 semantic relations in UMLS, we find 4 relations, e.g., than as, ISA, same as, compared with, are derived from non-causal claims and have no corresponding opposite forms. The left 54 predicates that are part of the semantic relations in UMLS are then classified in our study.

Table 1 Excitatory, Inhibitory and General predicates

Since we are more concerned with such knowledge as the effectiveness of treatments or potential causes of diseases in medical sciences, causal claims as well as the corresponding “subject-predicates-object” triples are the main units in medical knowledge metrics studies. Causal claims suggest a relationship between two concepts and assert that one concept has explicit excitatory or inhibitory influences on the other. Excitatory influence indicates a direct activation or enhancement, e.g. AUGMENTS, CAUSES, COMPLICATES, PREDISPOSES, PRODUCES, and STIMULATES. An inhibitory influence is the opposite of excitatory and indicates direct deactivation or suppression, e.g., DISRUPTS, INHIBITS, PREVENTS, and TREATS. The final type, general, neither excitatory nor inhibitory, e.g., ADMINISTERED TO, AFFECTS, ASSOCIATED WITH, COEXISTS WITH, and so on, doesn’t explicitly state whether it is excitatory or inhibitory causal claims (Table 1).

Here, group E lists the excitatory relations, while group I records the inhibitory relations. Generally, a knowledge claim C1-R1-C2 is considered to be contradictory with another knowledge claim C1-R2-C2 under the following cases:

  • R1 is an excitatory relation label (for example CAUSES) and R2 is an inhibitory one (for instance PREVENTS);

  • R1 is an inhibitory relation label (for example TREATS) and R2 is an excitatory one (for instance NEG_PREVENTS).

  • R1 is an general relation label (for example ASSOCIATED WITH) and R2 is its negative form (for instance NEG_ASSOCIATED WITH).

Table 2 shows a pair of knowledge with contradiction, where the first semantic predication contradicts the last one due to contradictory predicates “PREDISPOSES” and “NEG_PREDISPOSES”.

Table 2 A pair of contradictory knowledge claims

Taking advantage of semantic predications from SemRep, the discovery of contradictory knowledge between drug and disease could be expressed by the formula below.


Identifying knowledge claims with diversity

For two or more SPO triples interpreted by SemRep from scientific claims, if (1) they have identical subject (e.g., drug) and object (e.g., disease), but various predicates; (2) all of these predicates belong to either group E or group I and do not constitute a contradiction, then we will consider the knowledge concerning a given pair with diversity. Table 3 gives an example of drug-disease knowledge with diversity.

Table 3 An example of diverse knowledge

The new algorithm for diverse knowledge discovery is formulated as follows.



Based on the proposed framework which uses semantic predications as the knowledge unit and the uncertainty as the knowledge context, we try to extract and characterize the uncertain knowledge on the field of drug treatment for lung cancer (Fig. 3).

Fig. 3

The experimental flowchart of our study

Selecting medical publications with high-level clinical evidence

Different types of studies providing different levels of evidence. In a real-world application, it may be more preferable to assign lower weight to a contradiction due to a case study finding compared to a contradiction between the findings of two Randomized Controlled Trials or systematic reviews (Rosemblat et al. 2019). To extract the computable knowledge units from supporting sentences and discover the contradictory knowledge with higher confidence and precision, the PubMed publications with high-level clinical evidence were queried and selected using both the information of Publication Type (PT) and Medical Subject Headings (MeSH) according to (Haynes 2006):

Publication Types (PT):

“Guideline” OR “Practice Guideline” OR “Meta-Analysis” OR “Multicenter Study” OR “Randomized Controlled Trial” OR “Clinical Trial” OR “Clinical Trial, Phase I” OR “Clinical Trial, Phase II” OR “Clinical Trial, Phase III” OR “Clinical Trial, Phase IV” OR “Pragmatic Clinical Trial” OR “Comparative Study” OR “Controlled Clinical Trial”.

Medical subject headings (MeSH):

“Meta-Analysis as Topic” OR “Randomized Controlled Trials as Topic” OR “Systematic Reviews as Topic” OR “Clinical Trials as Topic” OR “Clinical Trials, Phase I as Topic” OR “Clinical Trials, Phase II as Topic” OR “Clinical Trials, Phase III as Topic” OR “Clinical Trials, Phase IV as Topic”.

We collected 9,215 medical abstracts on “lung cancer” published during Jan 1999-Jul 2019 (Fig. 3). After locally preservation, we run the batch mode of SemRep for semantic predications (SPO triples) extraction, and 89,919 SPO triples were extracted from 71,828 sentences (knowledge claims). To restrict generous biomedical concepts within drug and disease, we make proper use of UMLS knowledge by selecting the semantic predications with the subject and object holding semantic types “Chemicals and Drugs” and “Disease or Syndrome”. Besides, the hierarchy of “Pharmacologic Actions” from MeSH thesaurus was adopted to automatically filter out the generic concepts of drugs, such as “Antineoplastic Agents”, “Anti-Inflammatory Agents”.

Filtering out SPO triples supported by hedging sentences

In order to discover the contradictory knowledge with higher confidence and precision, we automatically filter out the semantic predications interpreted from uncertain claims by using hedges. Generally, if the claims in scientific texts contain hedging terms, then we can infer the authors consider part of their work as uncertain in some respect. Consequently, the semantic predications interpreted from these uncertain claims will be regarded with uncertainty. For our purpose, we do not need to identify all the hedges in scientific claims, only the three most prominent ones, namely “may”, “could”, and “might” were selected according to (Small et al. 2019). Through this process, 479 triples were filtered out from 401 sentences containing these three hedges.

Extracting uncertain knowledge claims within one sentence

In general, 24 triples and 19 claims were detected by using cue words ‘conflicting’, ‘controversial’, and ‘contradictory’ within one sentence. Two of them were not in the scope of the present study, because the subjects were not a drug (nor drug candidate compound) (Table 4). Here, the chemotherapy agents had the highest number of apparent contradictions among all agents (11; 64.7%). During the 1970 s–1980 s, the majority of oncological trials concentrated on evaluating response rates, disease‐free intervals, and overall survival as endpoints. In recent decades, the perceived role of chemotherapy in the treatment of advanced cancers has been changed. Palliative care is promoted as an approach to improve quality. Palliative chemotherapy has been the backbone of therapy in advanced-stage disease and has evolved over time. Moreover, chemotherapy side effects elimination while keeping the original therapeutic advantages has been of great interest over the last decades. The need for better evidence for the most effective type of chemotherapy regimen can lead to medical reversals and suggest that reality checks should be encouraged for established practices to avoid subjectivity and medical inertia.

Table 4 Basic knowledge units detected by using cue words ‘conflicting’, ‘controversial’, and ‘contradictory’ within one sentence

Extracting uncertain knowledge claims from cross-sentences

We then implemented the presented algorithm 1 and 2 to automatically discover biomedical knowledge with contradiction and diversity. Finally, 127 candidate pairs of drug-disease knowledge with controversy and 66 candidate pairs with diversity were discovered for further analysis and evaluation.

Filtering out NLP errors and checking manually

The manual biocuration aims to identify the accuracy of candidate knowledge based on the automatic algorithms, in terms of manually validation of the semantic predications and their supporting claims. In detail, two tasks involved in this process

  • Check whether the drug and disease were well recognized by MetaMap, which was already embedded in SemRep to map biomedical text into UMLS metathesaurus (e.g., Named Entity Recognition error (NER error), refer to the first row in Table 5);

  • Verify whether SemRep’s predicates properly indicated the semantic interactions between given drug and disease involved in supporting claims (e.g., Semantic Relation Extraction error (SRE error), see the last row in Table 5).

Table 5 Examples of inaccurate semantic predications from SemRep

To ensure consistence of biocuration in the annotation process, two authors (JD and XL) firstly established the criteria above, then one author (XL) annotated the candidate knowledge with contradiction and diversity, finally the other two authors (JD and PS) reviewed all the annotation results and made ultimate decision.

Identified drug-disease knowledge with controversial/contradiction

We found 25 groups of contradicted knowledge claims. Four of them were not a drug (nor drug candidate compound), which were beyond the scope of the present study. Our analysis of the contextual characteristics of 21 inapparent contradictions led to a categorization into seven main classes (Tables 6 and 7). And the frequency of uncertain sentences across the whole dataset was counted and adopted as a measure to quantify the uncertainty of a specific SPO triple (see the third column of Table 6).

Table 6 The 25 pairs of drug-disease relations with contradiction
Table 7 Categories of contextual characteristics for explaining these contradictions

(1) Heterogeneity in study design. The contradictions between studies pertained to the complexity of methodological factors, such as different participants, interventions, durations, or sample sizes. Eleven groups of the identified contradictions were due to the heterogeneity in study design. The following examples have shown that the different dosage of agents’ intervention affects the results. In particular, the heterogeneity associated with methodological diversity would indicate the results of studies have been biased estimated. Empirical evidence suggests that some aspects of the design can affect the result of clinical trials. Since medicine research is partly a statistically driven science, a certain amount of reversal of standards of care is inevitable. For example,

Topic – 16 [Paclitaxel – TREATS/NEG_TREATS—extensive-stage small cell lung cancer].

The purpose of this study was to evaluate the therapeutic effectiveness of paclitaxel in previously untreated patients with extensive-stage small-cell lung cancer (SCLC). [PMID: 10521070].

Paclitaxel in this dose and schedule should not be used as front-line therapy for patients with ES-SCLC. [PMID: 18303437].

(2) The contradiction of observational studies and RCTs. Observational studies can test several hypotheses at a low cost in a short period of time in an epidemiological cohort representing the general population. While the weakness of observational studies is confounding bias, both the exposures and outcome events can be affected by confounders. Beta-carotene was initially supported by many epidemiological studies and laboratory investigations as potent chemoprevention against cancer. Nevertheless, RCTs involving supplementation of pharmacologic doses revealed the absence of beneficial effects and the potential for harm with beta-carotene use. Failing to account for potential confounders may lead the association of exposures and outcomes were over-/under- estimated, or may draw the opposite conclusion.

(3) Research settings or real word settings. Many medical reversals involve conditions for the development of cancer, or the standard of cancer care has been promoted over the years based primarily on pathophysiological considerations under only laboratory settings. Clinical trials involving supplementation of pharmacologic doses may reveal detrimental effects. Targeted therapies provide much more effective treatment for specific molecularly defined NSCLC subsets. The contradiction was due to the development of NSCLC studies. For example,

Topic – 4 [cetuximab – TREATS/NEG_TREATS—Non-Small Cell Lung Carcinoma].

The use of cetuximab, a monoclonal antibody targeting the epidermal growth factor receptor (EGFR), has the potential to increase survival in patients with advanced non-small-cell lung cancer. [PMID: 19410716].

Expert opinion: Cetuximab currently has no role in NSCLC treatment outside of research settings. [PMID: 29534625].

(4) Cost-effectiveness. Cost-effectiveness is also an important concern. For cost-saving, the optimal strategy is to abandon ineffective medical practices. Lung cancer accounts for 20% of all cancer care budgets with limited benefits, besides the major source of morbidity and mortality, but also the healthcare costs which was associated with the diagnosis, staging, and management. For cost-effectiveness, it’s necessary to identify patients in potential risks and tailoring the application of follow-up to the estimated risk individually.

(5) Short-term outcome. In clinical trials of cancer agents, the endpoint might be mortality, decreased pain, or the absence of disease. 5 years survival rate as surrogate endpoints may be used instead of stronger indicators to predict the clinical benefits, including a shrinking tumor or lower biomarker levels, because the results of the trial can be measured sooner. The following example has shown that Lung Diseases are treated as adverse events (surrogate endpoints) of erlotinib use.

Topic – 11 [erlotinib – PREDISPOSES/NEG_AUGMENTS—Lung Diseases, Interstitial].

Our meta-analysis has demonstrated that erlotinib, gefitinib, and afatinib are associated with an increased risk of high-grade interstitial lung disease in patients with NSCLC. [PMID: 25804125].

The addition of erlotinib to chemotherapy was well tolerated, with no increase in hematologic toxicity, and no treatment-related interstitial lung disease. [PMID: 19738125].

(6) Publication bias, citation bias, and time-lag bias. Publication bias occurs when positive trials involving a medical intervention have been publicized more than neutral or negative trials of similar quality. Since specialist articles apparently continued to cite the studies that supported their own lines of research, the presence of refuting data was not mentioned in many articles.

(7) Inaccuracy of SemRep. Our analysis was based on semantic triples captured by the SemRep from the mining of biomedical literature abstracts. Some groups of contradictions were due to semantic differential was not correct. The parser identified the phrases that are mapped to UMLS Metathesaurus concepts by the MetaMap program (Aronson and Lang 2010). Some studies evaluated the precision and recall of the predication of SemRep, the SemRep’s precision for clinically relevant predictions is estimated at about 75%, and the recalls were reported around 50%. The following example has shown the SemRep precision error.

Topic—3 [Carboplatin—TREATS—Thrombocytopenia].

Conversely, Grade 3–4 thrombocytopenia was more common (P < 0.0009) and platelet transfusion was more frequent (P < 0.05) with carboplatin therapy. [PMID: 11505405].

In addition, after eliminated inaccurate semantic predications generated by SemRep, the entire pair of contradictory knowledge were filtered out. And these drug-disease knowledge claims were further identified and 6 of them were classified into the class of diversity.

Identified drug-disease knowledge with diversity

54 pairs of knowledge were manually identified from 66 candidates automatically discovered by proposed algorithm. Therefore, the number of knowledge pairs with diversity will be 60 (e.g., 54 + 6), while 6 were identified through manual biocuration of automatic discovery of knowledge with contradiction. For the drug-disease knowledge with diversity, we investigated their diverse predicates (Table 8). Clearly, the three predominated groups are PREVENTS&TREATS, CAUSES&PREDISPOSES and DISRUPTS&TREATS.

Table 8 Identified diverse knowledge with their diversity labels


The Concept evolution of knowledge metrics

Through a comprehensive review of the relevant literature from several communities, such as science and science, scientometrics and informetrics, as well as medical informatics, we concluded that the research object of knowmetrics (i.e., measuring “what”) developed from the total amount of scientific publications, to the bibliographic data, and then to the knowledge assertions in scientific literature.

The concept of Knowmetrics is firstly proposed by Prof. Liu Zeyuan in a commentary article on “Prof. Hongzhou Zhao and Scientometrics in China” in 1999 (Liu 1999). Most of his relevant works were published in Chinese journals. The earliest work in English is a conference paper by him and his team member (Hou et al. 2009). The initial idea of knowmetrics focused on measuring knowledge production at the country level and quantify its association with a country’s economic growth. Following the theorical framework, in an empirical study, Liu and his colleagues used the numbers of papers and patents as the two most important factors to measure knowledge capacity for OECD countries, and discussed the importance of R&D expenditures and total number of researchers in a country’s knowledge production (Jiang et al. 2006). But, in this period, the measurable knowledge unit is not well defined.

During the same period, the emergence of science mapping and information visualization techniques, e.g., CiteSpace developed by Prof. Chaomei Chen (Chen 2017,2006a) and the subsequent introduction to China by Prof. Liu and his team (Chen et al. 2009; Liu et al. 2008), presents unprecedented opportunities for the development of knowmetrics. The focus of such studies is constructing science knowledge graph from the bibliographic data of scientific literature, such as the keywords, authors and cited references, for mapping science domains and measuring knowledge structure in science or in a research field. However, the research of knowmetrics under this framework is still exploring the literature unit, rather than the real knowledge unit buried in the literature. We try to fill this gap.

Measuring medical knowledge based on SPO triples

In this paper, we have introduced a conceptual framework for measuring medical knowledge (or called “medical knowmetrics”), which takes SPO triples as the knowledge unit and the uncertain information as the context. The textual uncertainty associated with a given SPO triple has been detected from abstract sentences in scientific literature.

How to represent and quantify it has not been well defined, as is stated by Prof. Liu and his collaborators (Hou et al. 2009) and other scholars who developed mathematical methods for measuring knowledge (Ye 2017) (Fanelli 2019). To solve this problem, we are inspired by the interdisciplinary research between medical informatics and informetrics in our previous studies (Du and Li 2020; Du 2020). Knowledge unit is the smallest unit that is computer readable to define knowledge in a particular field. (Quigley and Debons 1999) offers an interrogative-based approach to differentiating and quantifying information and knowledge within text. Knowledge is text that answers how/why in the problem space; Information is text that answers when/where/who/what in the problem space; Data is text that answers no question in the problem space. (Sidi et al. 2009) then presents an Interrogative Knowledge Identification framework to identify unstructured documents that encompassed knowledge, information, and data. (Ding et al. 2013) proposed the new concept of knowledge entities, which is as carriers of knowledge units in scientific articles (e.g., keywords, topics, subject categories, datasets, key methods, and key theories), and domain entities (e.g., biological entities: genes, drugs, and diseases). While the concept of entitymetrics has extended research objects from scientific literature to knowledge entities, the entity co-occurrence network and entity citation network only show a correlation between entities, not involving their causal nature. For example, for the co-occurrence between drugs and diseases, we do not know whether it is referring to the “therapeutic use” or “induced adverse event” of a given drug for a given disease. We argued that the semantic predications based on the “entity-relationship-entity” triples answered the questions of “how”, and such triples associated to a given particular topic which formed the knowledge graph answered the questions of “why” in the framework by (Quigley and Debons 1999). For example, consider this sequence of two relations (obtained from SemMedDB) with “stimulates” and “treats” predicates in the following order: Mercaptopurine⟶stimulates⟶Cytarabine triphosphate⟶treats⟶ Leukemia. This subgraph explains why mercaptopurine can treat Leukemia.

From the above discussion, it is reasonable for measuring medical knowledge at the level of semantic predications, i.e., Subject-Predicate-Object (SPO) triples, which can be generated by SemMedDB, a PubMed-scale repository of biomedical semantic predications. In this sense, the concept of “science knowledge map” and the later emerging “knowledge graph” in computer science or medical informatics is different. The former refers to the co-occurrence network of two entities, such as co-authorship network and co-citation network. The latter in nature is a graph, which consist of “subject-predicates-object” triples, a semantic knowledge representation adapting semantic natural language processing technology to address information overload in medical publications (Keselman et al. 2010; Rosemblat et al. 2013; Chen and Song 2017b) or literature-based knowledge discovery (Hu et al. 2018). We think the “subject-predicates-object” triples are an effective approach for mining knowledge assertions from unstructured textual data.

Measuring the SPO triples related textual uncertainty

Currently, there are only a few studies on measuring the uncertainty level of scientific text surrounding the resulted SPO triples. In one chapter of their book, Chen and Song (2017a) analyzed conflicting and contradictory sentences and their SPO triples in SemMedDB. The SemRep project team proposed a method to assign factuality values for the semantic predications extracted from medical literature to reflect the uncertainty level of knowledge, i.e., PROBABLE, POSSIBLE, DOUBTFUL, COUNTERFACT, UNCOMMITTED, and CONDITIONAL (Kilicoglu et al. 2017).

There are also a few studies on identifying contradictory medical knowledge from cross-sentences based on semantic predication rules that the subject and object are the same but the predicate is just opposite between two or more SPO triples. For example, Alamri (2016) classified the predicates into three types: (a) active/causing, such as AUGMENTS and CAUSES; (b) passive/suppressing, such as DISRUPTS and PREVENTS; and (c) neutral, such as ADMINISTERED_TO and OCCURS_IN. If any of these types of relation pairs or their negative relationship can be extracted from two or more sentences, such as CAUSES and NEG-CAUSES, or active/causing and passive/suppressing are extracted, respectively, such as CAUSES and PREVENTS, the knowledge coming from the involved sentences is contradictory. Similarly, the SemRep project team adopted this rule to identify contradictory medical knowledge in clinical research papers. They identified clinically relevant predicate pairs and focused on (a) predicates with causal meaning, such as TREATS versus CAUSES, PREVENTS versus CAUSES, TREATS versus PREDISPOSES, and PREVENTS versus PREDISPOSES; and (b) predicates without causal meaning, including TREATS, PREVENTS, CAUSES, PREDISPOSES and their negative-form predicates (Rosemblat et al. 2019). In order to identify controversial claims which semantically contradict each other, (Pinto et al. 2019) focus on the following associations that are part of the semantic relations in UMLS: “affects”, “associated-with”, “causes”, “inhibits”, “prevents”, “process-of”, “treats” as well as its corresponding negative counterparts, e.g., “neg-affects”, “neg-associated-with”. However, they only scratched the surface of “controversy” using direct negations of the semantic orientation. One difference in our classification with (Alamri, 2016) is that we consider TREATS, which means a drug has a deactivation influence on a disease, as one of the Inhibitory relations, instead of the General (or Neutral) relations. And our classification differs with (Rosemblat et al. 2019) in that we are not just focusing on clinical relevant semantic relations by adding three predicates, e.g., AUGMENTS, PRODUCES, and STIMULATES.

Concerning the above research, the uncertainty framework is somewhat complicated, and it is sometimes difficult to distinguish during manual annotations, such as “probably” and “possible”. The classification of uncertainty needs to be simplified. Contradictory knowledge is identified based on the rule “with the same subject and object but opposite predicate” in semantic predications, but this rule ignores the textual uncertainty of the source sentence. To fill out the above gap, in our study, we simplified the classification of the uncertainty of medical knowledge, by focusing on only two types, i.e., ‘diversity’ and ‘controversy/contradiction’. And we have filtered out the semantic predications interpreted from uncertain claims by using ‘hedging’ before detecting contradictory knowledge from cross-sentences. In addition, the scientific article in the health sciences evolved from the letter form and purely descriptive style in the seventeenth century to a very standardized structure in the twentieth century known as introduction, methods, results, and discussion (IMRaD) (Sollaci and Pereira 2004). Semantic predications extracted from all the IMRaD sentences of a given paper may cause redundant. For example, the identical SPO triples from sentences in the title, the objective and the conclusion in the abstract tend to be extracted, whereas the knowledge assertion of a research is most important in the conclusive sentences. The SPO triples derived only from the conclusive sentences in the abstract may be distinguish for knowledge discovery in our future analysis.

Potential applications of detecting the uncertainty of medical knowledge

Firstly, measuring knowledge with SPO triples as the units and uncertainty as context could promote analytics from correlation to causality, and suggest innovative schemes for scientific frontier recognition. The SPO triples are the indivisible minimum knowledge units that define the causal relationship between entities, such as the therapeutic relationship (e.g., therapeutic effect) or causal relationship (e.g., side effect) between drugs and diseases. The frontiers of science always consist of a high degree of uncertain and competitive knowledge claims, unsolved scientific problems and unconfirmed controversial results. Mining the knowledge that is at the stage of speculation, even contradiction or disputation provide a novel method to identify the frontier of science, which is different from the common practice in current information science research by using the highly cited papers.

Secondly, by introducing the perspective of “filling knowledge gap and reducing knowledge uncertainty”, it may provide new solutions for research evaluation. By measuring the uncertainty level of a basic knowledge unit, one can reveal the extent to which a given scientific research diminishes the uncertainty for scientific problems in this field, such as verifying hypotheses or speculations, and resolving contradictions or disputes. This perspective highlights the value of the produced knowledge in solving scientific problems, and it will help reform the prevailing research evaluation mechanism that too much focusing on publications, citations and journals’ impact factors.

Lastly, a measure to quantify the uncertainty of a given SPO triple could be developed by counting the frequency of uncertain sentences across all sentences. We can estimate the probability of the certainty level for a given knowledge claim. Medical science is a science of uncertainty, and its knowledge is often ambiguous, inconsistent, or even inaccurate. Medical decision-making can only be based on the limited, uncertain knowledge in reality. For specific medical problems or decision-making requirements (e.g., which drugs can be effectively used to treat lung cancer), if we can distinguish the related knowledge claims with higher certainty level from those with lower level, it will help to improve the efficiency of computational knowledge-driven decision support.


We have proposed a conceptual framework of Medical Knowmetrics by using semantic predications as the knowledge unit and the uncertainty as the knowledge context. And a case study on extracting and characterizing clinical knowledge with ‘diversity’ and ‘controversy or contradiction’ on drug treatment for lung cancer from large-scale published medical documents has validated the proposal. The uncertainty of scientific knowledge and how its status evolves over time indirectly reflect the strength of competing knowledge claims, the contribution for filling up knowledge gap, as well as the probability of certainty for a given knowledge claim. So, we try to provide new insights using the uncertainty-centric approaches to detecting research fronts, evaluating academic contributions and improving the efficacy of computable knowledge driven decision support. We expect to deepen the methodologies from scientometrics and informetrics, to knowmetrics and broaden their new application fields.

In the future, we will extend the source sentences to citing sentences. In addition, with citation contexts we can access the collective comments on a specific scientific issue from various scientific community, use hedging words to measure uncertainty of the associated evidence, and characterize the evolution of uncertainty over time. But, how to define citing sentences associated with a semantic triple, and can we achieve a higher enough performance when extracting semantic triples from citing sentences, as compared with those in titles and abstracts, remain future research. In our opinions, from the perspective of textual analysis of uncertainty in citing sentences, we can capture how scholars evaluate the reliability and validity of relevant knowledge claims. This could explain the evolutionary process of scientific knowledge more effectively.


  1. ALAMRI, A. 2016. The detection of contradictory claims in biomedical abstracts. University of Sheffield.

  2. ATANASSOVA, I., REY, F., CLAUDE & BERTIN, M. 2018. Studying Uncertainty in Science: a distributional analysis through the IMRaD structure. WOSP - 7th International Workshop on Mining Scientific Publications at 11th edition of the Language Resources and Evaluation Conference. Miyazaki, Japan.

  3. Bakal, G., Talari, P., Kakani, E. V., & Kavuluru, R. (2018). Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations. Journal of Biomedical Informatics, 82, 189–199.

    Article  Google Scholar 

  4. Borner, K., Chen, C. M., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.

    Article  Google Scholar 

  5. CAVALLER, V. 2008. Datametrics? About the architecture of the metric disciplines. Fourth International Conference on Webometrics, Informetrics and Scientometrics & Ninth COLLNET Meeting.

  6. CHEN, C. 2006a. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. 57, 359–377.

  7. CHEN, C. 2017. Science Mapping: A Systematic Review of the Literature %J Journal of Data and Information Science. 2, 1–40.

  8. Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3, 191–209.

    Article  Google Scholar 

  9. Chen, C., & Song, M. (2017). Patterns and Trends in Semantic Predications. In Representing Scientific Knowledge (pp. 283-336). Springer, Cham.

  10. Chen, C., & Song, M. (2017). Representing Biomedical Knowledge. Representing Scientific Knowledge: Springer.

    Google Scholar 

  11. CHEN, C. & SONG, M. 2017c. The Uncertainty of Science: Navigating Through the Unknown. In: CHEN, C. & SONG, M. (eds.) Representing Scientific Knowledge: The Role of Uncertainty. Cham: Springer International Publishing.

  12. Chen, C., Song, M., & Heo, G. E. (2018). A scalable and adaptive method for finding semantically equivalent cue words of uncertainty. Journal of Informetrics, 12, 158–180.

    Article  Google Scholar 

  13. CHEN, C. M. . (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57, 359–377.

    Article  Google Scholar 

  14. Clark, T., Ciccarese, P. N., & Goble, C. A. (2014). Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications. Journal of Biomedical Semantics, 5, 28.

    Article  Google Scholar 

  15. Ding, Y., Song, M., Han, J., Yu, Q., Yan, E., Lin, L., & Chambers, T. (2013). Entitymetrics: Measuring the Impact of Entities. PLoS ONE, 8, e71416.

    Article  Google Scholar 

  16. DU, J. . (2020). Measuring uncertainty of medical knowledge: A literature review. Data Analysis and Knowledge Discovery, 4, 14–27.

    Google Scholar 

  17. Du, J., & Li, X. (2020). A knowledge graph of combined drug therapies using semantic predications from biomedical literature: Algorithm development. JMIR Med Inform, 8, e18323.

    Article  Google Scholar 

  18. Evans, J. A., & Foster, J. G. (2011). Metaknowledge. Science, 331, 721.

    Article  Google Scholar 

  19. FANELLI, D. . (2019). A theory and methodology to quantify knowledge. R Soc Open Sci, 6, 181055.

    Article  Google Scholar 

  20. Flynn, A. J., Friedman, C. P., Boisvert, P., Landis-Lewis, Z., & Lagoze, C. (2018). The Knowledge Object Reference Ontology (KORO): A formalism to support management and sharing of computable biomedical knowledge for learning health systems. Learning Health Systems.

    Article  Google Scholar 

  21. Friedman, C. P., & Flynn, A. J. (2019). Computable knowledge: An imperative for learning health systems. Learning Health Systems, 3, 10203.

    Article  Google Scholar 

  22. GALYAVIEVA, M. S. . (2013). On the formation of the concept of informetrics (Review). Scientific and Technical Information Processing, 40, 89–96.

    Article  Google Scholar 

  23. Groth, P., Gibson, A., & Velterop, J. (2010). The anatomy of a nanopublication. Information Services & Use, 30, 51–56.

    Article  Google Scholar 

  24. HAYNES, R. B. . (2006). Of studies, syntheses, synopses, summaries, and systems: the “5S” evolution of information services for evidence-based healthcare decisions. Evidence-based medicine, 11, 162–164.

    Article  Google Scholar 

  25. Herrera-Perez, D., Haslam, A., Crain, T., Gill, J., Livingston, C., Kaestner, V., & Prasad, V. (2019). Meta-research: A comprehensive review of randomized clinical trials in three medical journals reveals 396 medical reversals. Elife, 8, e45183.

    Article  Google Scholar 

  26. HOU, H.-Y., LIU, Z.-Y., CHEN, C.-M., WANG, X.-W., QU, T.-P. & LU, C.-T. 2009. The Interdisciplinary Feature of Knowmetrics. In: LARSEN, B. & LETA, J. (eds.) Proceedings of Issi 2009 - 12th International Conference of the International Society for Scientometrics and Informetrics, Vol 2.

  27. HU, Z.-Y., ZENG, R.-Q., QIN, X.-C., WEI, L. & ZHANG, Z. A Method of Biomedical Knowledge Discovery by Literature Mining Based on SPO Predications: A Case Study of Induced Pluripotent Stem Cells. In: PERNER, P., ed. Machine Learning and Data Mining in Pattern Recognition, 2018// 2018 Cham. Springer International Publishing, 383–393.

  28. Hyland, K. (1996). Talking to the academy: forms of hedging in science research articles. Written communication, 13(2), 251–281.

    Article  Google Scholar 

  29. JIANG, Z., LIU, Z. & ZHAO, W. Dynamic model of knowledge growth of the OECD countries and knowledge capacities measuring. International Workshop on Webometrics, Informetrics and Scientometrics & seventh COLLNET meeting (10–12 May 2006, Nancy), 2006. 120–128.

  30. Keselman, A., Rosemblat, G., Kilicoglu, H., Fiszman, M., Jin, H., Shin, D., & Rindflesch, T. C. (2010). Adapting semantic natural language processing technology to address information overload in influenza epidemic management. Journal of the American Society for Information Science and Technology, 61(12), 2531–2543.

    Article  Google Scholar 

  31. Kilicoglu, H., Rosemblat, G., Fiszman, M., & Shin, D. (2020). Broad-coverage biomedical relation extraction with SemRep. BMC Bioinformatics, 21, 1–28.

    Article  Google Scholar 

  32. Kilicoglu, H., Shin, D., Fiszman, M., Rosemblat, G., & Rindflesch, T. C. (2012). SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics, 28(23), 3158–3160.

    Article  Google Scholar 

  33. Kilicoglu, H., Rosemblat, G., & Rindflesch, T. C. (2017). Assigning factuality values to semantic relations extracted from biomedical research literature. PLoS ONE, 12, e0179926.

    Article  Google Scholar 

  34. KUHN, T. S. & HACKING, I. 2012. The Structure of Scientific Revolutions: 50th Anniversary Edition.

  35. LIU, Z., CHEN, Y. & HOU, H. 2008. Mapping Knowledge Domains: Methods and Applications (in Chinese), People's Publishing House.

  36. LIU, Z. & LIU, F. 2002. On the metholody of knowmetrcis research (in Chinese). Science of Science and Management of S.&.T., 23, 5–9.

  37. Liu, Z. (1999). Hongzhou ZHAO and scientometrics in China. Studies in Science of Science, 17(4), 104–109.

    Google Scholar 

  38. Malhotra, A., Younesi, E., Gurulingappa, H., & Hofmann-Apitius, M. (2013). ‘HypothesisFinder:’ A Strategy for the detection of speculative statements in scientific text. PLOS Computational Biology, 9, e1003117.

    Article  Google Scholar 

  39. Mercer, R. E., Di Marco, C., & Kroon, F. W. (2004). The Frequency of Hedging Cues in Citation Contexts in Scientific Writing. In A. Y. Tawfik & S. D. Goodwin (Eds.), Advances in Artificial Intelligence, Berlin, Heidelberg. Berlin Heidelberg: Springer.

    Google Scholar 

  40. Murray, D., Lamers, W., Boyack, K., Larivière, V., Sugimoto, C. R., Van Eck, N. J. Waltman, L. Measuring disagreement in science. 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings, 2019. 2370–2375.

  41. Pinto, J. M. G., Wawrzinek, J. & Balke, W. What Drives Research Efforts? Find Scientific Claims that Count! 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2–6 June 2019 2019. 217–226.

  42. Prasad, V., Vandross, A., Toomey, C., Cheung, M., Rho, J., Quinn, S., et al. (2013). A decade of reversal: An analysis of 146 contradicted medical practices. Mayo Clinic Proceedings, 88, 790–798.

    Article  Google Scholar 

  43. Quigley, E. J., Debons, A. 1999. Interrogative theory of information and knowledge. Proceedings of the 1999 ACM SIGCPR conference on Computer personnel research. New Orleans, Louisiana, USA: Association for Computing Machinery.

  44. Rosemblat, G., Fiszman, M., Shin, D., & Kilicoglu, H. (2019). Towards a characterization of apparent contradictions in the biomedical literature using context analysis. Journal of Biomedical Informatics, 98, 103275.

    Article  Google Scholar 

  45. Rosemblat, G., Resnick, M. P., Auston, I., Shin, D., Sneiderman, C., Fizsman, M., & Rindflesch, T. C. (2013). Extending SemRep to the public health domain. Journal of the American Society for Information Science and Technology, 64(10), 1963–1974.

    Article  Google Scholar 

  46. Sidi, F., Jabar, M. A., Selamat, M. H., Ghani, A. A. A., & Sulaiman, M. N. (2009). Framework for Interrogative Knowledge Identification. J Computer Information Science, 2, 109–115.

    Google Scholar 

  47. Simpkin, A. L., & Schwartzstein, R. M. (2016). Tolerating uncertainty — the next medical revolution? New England Journal of Medicine, 375, 1713–1715.

    Article  Google Scholar 

  48. SMALL, H. . (2018). Characterizing highly cited method and non-method papers using citation contexts: The role of uncertainty. Journal of Informetrics, 12, 461–480.

    Article  Google Scholar 

  49. Small, H. 2019. What makes some scientific findings more certain than others? A study of citing sentences for low-hedged papers. 17th International Conference on Scientometrics and Informetrics.

  50. Small, H. 2020. Past as prologue: Approaches to the study of confirmation in science. Quantitative Science Studies.

  51. Small, H., Boyack, K. W., & Klavans, R. (2019). Citations and certainty: a new interpretation of citation counts. Scientometrics, 118, 1079–1092.

    Article  Google Scholar 

  52. Sollaci, L. B., & Pereira, M. G. (2004). The introduction, methods, results, and discussion (IMRAD) structure: a fifty-year survey. Journal of the Medical Library Association, 92, 364–367.

    Google Scholar 

  53. YE, F. Y. 2017. Measuring Knowledge: A Quantitative Approach to Knowledge Theory. In: YE, F. Y. (ed.) Scientific Metrics: Towards Analytical and Quantitative Sciences. Singapore: Springer Singapore.

  54. Zhao, H. Z., & Jiang, G. H. (1984). On the element of Knowledge and exponential growth rate (in Chinese). Science of Science and Management of ST, 5, 41–43.

    Google Scholar 

Download references


Xiaoying Li and Suyuan Peng contributed equally. Jian Du proposed the conceptual framework, designed the research and wrote the manuscript. Xiaoying Li conducted the data analysis and Suyuan Peng interpreted and characterized the results. Both authors participated in writing the manuscript. This work was funded by the National Natural Science Foundation of China (72074006, 71603280), and the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (2017QNRC001).

Author information



Corresponding author

Correspondence to Jian Du.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, X., Peng, S. & Du, J. Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context. Scientometrics (2021).

Download citation


  • Knowledge metrics
  • Uncertainty
  • Semantic predications
  • Knowledge discovery
  • SemRep