Missing lateral relationships in top‑level concepts of an ontology

Background: Ontologies house various kinds of domain knowledge in formal structures, primarily in the form of concepts and the associative relationships between them. Ontologies have become integral components of many health information processing environments. Hence, quality assurance of the conceptual content of any ontology is critical. Relationships are foundational to the definition of concepts. Missing relationship errors (i.e., unintended omissions of important definitional relationships) can have a deleterious effect on the quality of an ontology. An abstraction network is a structure that overlays an ontology and provides an alternate, summarization view of its contents. One kind of abstraction network is called an area taxonomy, and a variation of it is called a subtaxonomy. A methodology based on these taxonomies for more readily finding missing relationship errors is explored. Methods: The area taxonomy and the subtaxonomy are deployed to help reveal concepts that have a high likelihood of exhibiting missing relationship errors. A specific top-level grouping unit found within the area taxonomy and subtaxonomy, when deemed to be anomalous, is used as an indicator that missing relationship errors are likely to be found among certain concepts. Two hypotheses pertaining to the effectiveness of our Quality Assurance approach are studied. Results: Our Quality Assurance methodology was applied to the Biological Process hierarchy of the National Cancer Institute thesaurus (NCIt) and SNOMED CT’s Eye/vision finding subhierarchy within its Clinical finding hierarchy. Many missing relationship errors were discovered and confirmed in our analysis. For both test-bed hierarchies, our Quality Assurance methodology yielded a statistically significantly higher number of concepts with missing relationship errors in comparison to a control sample of concepts. Two hypotheses are confirmed by these findings. Conclusions: Quality assurance is a critical part of an ontology’s lifecycle, and automated or semi-automated tools for supporting this process are invaluable. We introduced a Quality Assurance methodology targeted at missing relationship errors. Its successful application to the NCIt’s Biological Process hierarchy and SNOMED CT’s Eye/vision finding subhierarchy indicates that it can be a useful addition to the arsenal of tools available to ontology maintenance personnel

Outlier concepts auditing methodology for a large family of biomedical ontologies

Background: Summarization networks are compact summaries of ontologies. The “Big Picture” view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that have outgoing lateral relationships, we have developed the partial-area taxonomy summarization network. Prior research has identified one kind of outlier concepts, concepts of small partials-areas within partial-area taxonomies. Previously we have shown that the small partial-area technique works successfully for four ontologies (or their hierarchies). Methods: To improve the Quality Assurance (QA) scalability, a family-based QA framework, where one QA technique is potentially applicable to a whole family of ontologies with similar structural features, was developed. The 373 ontologies hosted at the NCBO BioPortal in 2015 were classified into a collection of families based on structural features. A meta-ontology represents this family collection, including one family of ontologies having outgoing lateral relationships. The process of updating the current meta-ontology is described. To conclude that one QA technique is applicable for at least half of the members for a family F, this technique should be demonstrated as successful for six out of six ontologies in F. We describe a hypothesis setting the condition required for a technique to be successful for a given ontology. The process of a study to demonstrate such success is described. This paper intends to prove the scalability of the small partial-area technique. Results: We first updated the meta-ontology classifying 566 BioPortal ontologies. There were 371 ontologies in the family with outgoing lateral relationships. We demonstrated the success of the small partial-area technique for two ontology hierarchies which belong to this family, SNOMED CT’s Specimen hierarchy and NCIt’s Gene hierarchy. Together with the four previous ontologies from the same family, we fulfilled the “six out of six” condition required to show the scalability for the whole family. Conclusions: We have shown that the small partial-area technique can be potentially successful for the family of ontologies with outgoing lateral relationships in BioPortal, thus improve the scalability of this QA technique

Outlier concepts auditing methodology for a large family of biomedical ontologies

Background Summarization networks are compact summaries of ontologies. The “Big Picture” view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that have outgoing lateral relationships, we have developed the "partial-area taxonomy" summarization network. Prior research has identified one kind of outlier concepts, concepts of small partials-areas within partial-area taxonomies. Previously we have shown that the small partial-area technique works successfully for four ontologies (or their hierarchies). Methods To improve the Quality Assurance (QA) scalability, a family-based QA framework, where one QA technique is potentially applicable to a whole family of ontologies with similar structural features, was developed. The 373 ontologies hosted at the NCBO BioPortal in 2015 were classified into a collection of families based on structural features. A meta-ontology represents this family collection, including one family of ontologies having outgoing lateral relationships. The process of updating the current meta-ontology is described. To conclude that one QA technique is applicable for at least half of the members for a family F, this technique should be demonstrated as successful for six out of six ontologies in F. We describe a hypothesis setting the condition required for a technique to be successful for a given ontology. The process of a study to demonstrate such success is described. This paper intends to prove the scalability of the small partial-area technique. Results We first updated the meta-ontology classifying 566 BioPortal ontologies. There were 371 ontologies in the family with outgoing lateral relationships. We demonstrated the success of the small partial-area technique for two ontology hierarchies which belong to this family, SNOMED CT’s Specimen hierarchy and NCIt’s Gene hierarchy. Together with the four previous ontologies from the same family, we fulfilled the “six out of six” condition required to show the scalability for the whole family. Conclusions We have shown that the small partial-area technique can be potentially successful for the family of ontologies with outgoing lateral relationships in BioPortal, thus improve the scalability of this QA technique

STRUCTURAL AND LEXICAL METHODS FOR AUDITING BIOMEDICAL TERMINOLOGIES

Biomedical terminologies serve as knowledge sources for a wide variety of biomedical applications including information extraction and retrieval, data integration and management, and decision support. Quality issues of biomedical terminologies, if not addressed, could affect all downstream applications that use them as knowledge sources. Therefore, Terminology Quality Assurance (TQA) has become an integral part of the terminology management lifecycle. However, identification of potential quality issues is challenging due to the ever-growing size and complexity of biomedical terminologies. It is time-consuming and labor-intensive to manually audit them and hence, automated TQA methods are highly desirable. In this dissertation, systematic and scalable methods to audit biomedical terminologies utilizing their structural as well as lexical information are proposed. Two inference-based methods, two non-lattice-based methods and a deep learning-based method are developed to identify potentially missing hierarchical (or is-a) relations, erroneous is-a relations, and missing concepts in biomedical terminologies including the Gene Ontology (GO), the National Cancer Institute thesaurus (NCIt), and SNOMED CT. In the first inference-based method, the GO concept names are represented using set-of-words model and sequence-of-words model, respectively. Inconsistencies derived between hierarchical linked and unlinked concept pairs are leveraged to detect potentially missing or erroneous is-a relations. The set-of-words model detects a total of 5,359 potential inconsistencies in the 03/28/2017 release of GO and the sequence-of-words model detects 4,959. Domain experts’ evaluation shows that the set-of-words model achieves a precision of 53.78% (128 out of 238) and the sequence-of-words model achieves a precision of 57.55% (122 out of 212) in identifying inconsistencies. In the second inference-based method, a Subsumption-based Sub-term Inference Framework (SSIF) is developed by introducing a novel term-algebra on top of a sequence-based representation of GO concepts. The sequence-based representation utilizes the part of speech of concept names, sub-concepts (concept names appearing inside another concept name), and antonyms appearing in concept names. Three conditional rules (monotonicity, intersection, and sub-concept rules) are developed for backward subsumption inference. Applying SSIF to the 10/03/2018 release of GO suggests 1,938 potentially missing is-a relations. Domain experts’ evaluation of randomly selected 210 potentially missing is-a relations shows that SSIF achieves a precision of 60.61%, 60.49%, and 46.03% for the monotonicity, intersection, and sub-concept rules, respectively. In the first non-lattice-based method, lexical patterns of concepts in Non-Lattice Subgraphs (NLSs: graph fragments with a higher tendency to contain quality issues), are mined to detect potentially missing is-a relations and missing concepts in NCIt. Six lexical patterns: containment, union, intersection, union-intersection, inference-contradiction, and inference-union are leveraged. Each pattern indicates a potential specific type of error and suggests a potential type of remediation. This method identifies 809 NLSs exhibiting these patterns in the 16.12d version of NCIt, achieving a precision of 66% (33 out of 50). In the second non-lattice-based method, enriched lexical attributes from concept ancestors are leveraged to identify potentially missing is-a relations in NLSs. The lexical attributes of a concept are inherited in two ways: from ancestors within the NLS, and from all the ancestors. For a pair of concepts without a hierarchical relation, if the lexical attributes of one concept is a subset of that of the other, a potentially missing is-a relation between the two concepts is suggested. This method identifies a total of 1,022 potentially missing is-a relations in the 19.01d release of NCIt with a precision of 84.44% (76 out of 90) for inheriting lexical attributes from ancestors within the NLS and 89.02% (73 out of 82) for inheriting from all the ancestors. For the non-lattice-based methods, similar NLSs may contain similar quality issues, and thus exhaustive examination of NLSs would involve redundant work. A hybrid method is introduced to identify similar NLSs to avoid redundant analyses. Given an input NLS, a graph isomorphism algorithm is used to obtain its structurally identical NLSs. A similarity score between the input NLS and each of its structurally identical NLSs is computed based on semantic similarity between their corresponding concept names. To compute the similarity between concept names, the concept names are converted to vectors using the Doc2Vec document embedding model and then the cosine similarity of the two vectors is computed. All the structurally identical NLSs with a similarity score above 0.85 is considered to be similar to the input NLS. Applying this method to 10 different structures of NLSs in the 02/12/2018 release of GO reveals that 38.43% of these NLSs have at least one similar NLS. Finally, a deep learning-based method is explored to facilitate the suggestion of missing is-a relations in NCIt and SNOMED CT. Concept pairs exhibiting a containment pattern is the focus here. The problem is framed as a binary classification task, where given a pair of concepts, the deep learning model learns to predict whether the two concepts have an is-a relation or not. Positive training samples are existing is-a relations in the terminology exhibiting containment pattern. Negative training samples are concept-pairs without is-a relations that are also exhibiting containment pattern. A graph neural network model is constructed for this task and trained with subgraphs generated enclosing the pairs of concepts in the samples. To evaluate each model trained by the two terminologies, two evaluation sets are created considering newer releases of each terminology as a partial reference standard. The model trained on NCIt achieves a precision of 0.5, a recall of 0.75, and an F1 score of 0.6. The model trained on SNOMED CT achieves a precision of 0.51, a recall of 0.64 and an F1 score of 0.56

Enrichment of ontologies using machine learning and summarization

Biomedical ontologies are structured knowledge systems in biomedicine. They play a major role in enabling precise communications in support of healthcare applications, e.g., Electronic Healthcare Records (EHR) systems. Biomedical ontologies are used in many different contexts to facilitate information and knowledge management. The most widely used clinical ontology is the SNOMED CT. Placing a new concept into its proper position in an ontology is a fundamental task in its lifecycle of curation and enrichment. A large biomedical ontology, which typically consists of many tens of thousands of concepts and relationships, can be viewed as a complex network with concepts as nodes and relationships as links. This large-size node-link diagram can easily become overwhelming for humans to understand or work with. Adding concepts is a challenging and time-consuming task that requires domain knowledge and ontology skills. IS-A links (aka subclass links) are the most important relationships of an ontology, enabling the inheritance of other relationships. The position of a concept, represented by its IS-A links to other concepts, determines how accurately it is modeled. Therefore, considering as many parent candidate concepts as possible leads to better modeling of this concept. Traditionally, curators rely on classifiers to place concepts into ontologies. However, this assumes the accurate relationship modeling of the new concept as well as the existing concepts. Since many concepts in existing ontologies, are underspecified in terms of their relationships, the placement by classifiers may be wrong. In cases where the curator does not manually check the automatic placement by classifier programs, concepts may end up in wrong positions in the IS-A hierarchy. A user searching for a concept, without knowing its precise name, would not find it in its expected location. Automated or semi-automated techniques that can place a concept or narrow down the places where to insert it, are highly desirable. Hence, this dissertation is addressing the problem of concept placement by automatically identifying IS-A links and potential parent concepts correctly and effectively for new concepts, with the assistance of two powerful techniques, Machine Learning (ML) and Abstraction Networks (AbNs). Modern neural networks have revolutionized Machine Learning in vision and Natural Language Processing (NLP). They also show great promise for ontology-related tasks, including ontology enrichment, i.e., insertion of new concepts. This dissertation presents research using ML and AbNs to achieve knowledge enrichment of ontologies. Abstraction networks (AbNs), are compact summary networks that preserve a significant amount of the semantics and structure of the underlying ontologies. An Abstraction Network is automatically derived from the ontology itself. It consists of nodes, where each node represents a set of concepts that are similar in their structure and semantics. Various kinds of AbNs have been previously developed by the Structural Analysis of Biomedical Ontologies Center (SABOC) to support the summarization, visualization, and quality assurance (QA) of biomedical ontologies. Two basic kinds of AbNs are the Area Taxonomy and the Partial-area Taxonomy, which have been developed for various biomedical ontologies (e.g., SNOMED CT of SNOMED International and NCIt of the National Cancer Institute). This dissertation presents four enrichment studies of SNOMED CT, utilizing both ML and AbN-based techniques

Applications of big knowledge summarization

Advanced technologies have resulted in the generation of large amounts of data ( Big Data ). The Big Knowledge derived from Big Data could be beyond humans\u27 ability of comprehension, which will limit the effective and innovative use of Big Knowledge repository. Biomedical ontologies, which play important roles in biomedical information systems, constitute one kind of Big Knowledge repository. Biomedical ontologies typically consist of domain knowledge assertions expressed by the semantic connections between tens of thousands of concepts. Without some high-level visual representation of Big Knowledge in biomedical ontologies, humans cannot grasp the big picture of those ontologies. Such Big Knowledge orientation is required for the proper maintenance of ontologies and their effective use. This dissertation is addressing the Big Knowledge challenge - How to enable humans to use Big Knowledge correctly and effectively (referred to as the Big Knowledge to Use (BK2U) problem) - with a focus on biomedical ontologies. In previous work, Abstraction Networks (AbNs) have been demonstrated successful for the summarization, visualization and quality assurance (QA) of biomedical ontologies. Based on the previous research, this dissertation introduces new AbNs of various granularities for Big Knowledge summarization and extends the applications of AbNs. This dissertation consists of three main parts. The first part introduces two advanced AbNs. One is the weighted aggregate partial-area taxonomy with a parameter to flexibly control the summarization granularity. The second is the Ingredient Abstraction Network (IAbN) for the National Drug File - Reference Terminology (NDF-RT) Chemical Ingredients hierarchy, for which the previously developed AbNs for hierarchies with outgoing relationships, are not applicable. Since NDF-RT\u27s Chemical Ingredients hierarchy has no outgoing relationships. The second part describes applications of the two advanced AbNs. A study utilizing the weighted aggregate partial-area taxonomy for the identification of major topics in SNOMED CT\u27s Specimen hierarchy is reported. A multi-layer interactive visualization system of required granularity for ontology comprehension, based on the weighted aggregate partial-area taxonomy, is demonstrated to comprehend the Neoplasm subhierarchy of National Cancer Institute thesaurus (NCIt). The IAbN is applied for drug-drug interaction (DDI) discovery. The third part reports eight family-based QA studies on NCIt\u27s Neoplasm, Gene, and Biological Process hierarchies, SNOMED CT\u27s Infectious disease hierarchy, the Chemical Entities of Biological Interest ontology, and the Chemical Ingredients hierarchy in NDF-RT. There is no one-size-fits-all QA method and it is impossible to find a QA method for each individual ontology. Hence, family-based QA is an effective way, i.e., one QA technique could be applicable to a whole family of structurally similar ontologies. The results of these studies demonstrate that complex concepts and uncommonly modeled concepts are more likely to have errors. Furthermore, the three studies on overlapping concepts in partial-area taxonomies reported in this dissertation combined with previous three studies prove the success of overlapping concepts as a QA methodology for a whole family of 76 similar ontologies in BioPortal