FEDRR: Fast, Exhaustive Detection of Redundant Hierarchical Relations for Quality Improvement of Large Biomedical Ontologies

Background: Redundant hierarchical relations refer to such patterns as two paths from one concept to another, one with length one (direct) and the other with length greater than one (indirect). Each redundant relation represents a possibly unintended defect that needs to be corrected in the ontology quality assurance process. Detecting and eliminating redundant relations would help improve the results of all methods relying on the relevant ontological systems as knowledge source, such as the computation of semantic distance between concepts and for ontology matching and alignment. Results: This paper introduces a novel and scalable approach, called FEDRR – Fast, Exhaustive Detection of Redundant Relations – for quality assurance work during ontological evolution. FEDRR combines the algorithm ideas of Dynamic Programming with Topological Sort, for exhaustive mining of all redundant hierarchical relations in ontological hierarchies, in O(c·|V|+|E|) time, where |V| is the number of concepts, |E| is the number of the relations, and c is a constant in practice. Using FEDRR, we performed exhaustive search of all redundant is-a relations in two of the largest ontological systems in biomedicine: SNOMED CT and Gene Ontology (GO). 372 and 1609 redundant is-a relations were found in the 2015-09-01 version of SNOMED CT and 2015-05-01 version of GO, respectively. We have also performed FEDRR on over 190 source vocabularies in the UMLS - a large integrated repository of biomedical ontologies, and identified six sources containing redundant is-a relations. Randomly generated ontologies have also been used to further validate the efficiency of FEDRR. Conclusions: FEDRR provides a generally applicable, effective tool for systematic detecting redundant relations in large ontological systems for quality improvement

Scalable Approaches for Auditing the Completeness of Biomedical Ontologies

An ontology provides a formalized representation of knowledge within a domain. In biomedicine, ontologies have been widely used in modern biomedical applications to enable semantic interoperability and facilitate data exchange. Given the important roles that biomedical ontologies play, quality issues such as incompleteness, if not addressed, can affect the quality of downstream ontology-driven applications. However, biomedical ontologies often have large sizes and complex structures. Thus, it is infeasible to uncover potential quality issues through manual effort. In this dissertation, we introduce automated and scalable approaches for auditing the completeness of biomedical ontologies. We mainly focus on two incompleteness issues -- missing hierarchical relations and missing concepts. To identify missing hierarchical relations, we develop three approaches: a lexical-based approach, a hybrid approach utilizing both lexical features and logical definitions, and an approach based on concept name transformation. To identify missing concepts, a lexical-based Formal Concept Analysis (FCA) method is proposed for concept enrichment. We also predict proper concept names for the missing concepts using deep learning techniques. Manual review by domain experts is performed to evaluate these approaches. In addition, we leverage extrinsic knowledge (i.e., external ontologies) to help validate the detected incompleteness issues. The auditing approaches have been applied to a variety of biomedical ontologies, including the SNOMED CT, National Cancer Institute (NCI) Thesaurus and Gene Ontology. In the first lexical-based approach to identify missing hierarchical relations, each concept is modeled with an enriched set of lexical features, leveraging words and noun phrases in the name of the concept itself and the concept\u27s ancestors. Given a pair of concepts that are not linked by a hierarchical relation, if the enriched lexical attributes of one concept is a superset of the other\u27s, a potentially missing hierarchical relation will be suggested. Applying this approach to the September 2017 release of SNOMED CT (US edition) suggested 38,615 potentially missing hierarchical relations. A domain expert reviewed a random sample of 100 potentially missing ones, and confirmed 90 are valid (a precision of 90%). In the second work, a hybrid approach is proposed to detect missing hierarchical relations in non-lattice subgraphs. For each concept, its lexical features are harmonized with role definitions to provide a more comprehensive semantic model. Then a two-step subsumption testing is performed to automatically suggest potentially missing hierarchical relations. This approach identified 55 potentially missing hierarchical relations in the 19.08d version of the NCI Thesaurus. 29 out of 55 were confirmed as valid by the curators from the NCI Enterprise Vocabulary Service (EVS) and have been incorporated in the newer versions of the NCI Thesaurus. 7 out of 55 further revealed incorrect existing hierarchical relations in the NCI Thesaurus. In the third work, we introduce a transformation-based method that leverages the Unified Medical Language System (UMLS) knowledge to identify missing hierarchical relations in its source ontologies. Given a concept name, noun chunks within it are identified and replaced by their more general counterparts to generate new concept names that are supposed to be more general than the original one. Applying this method to the UMLS (2019AB release), a total of 39,359 potentially missing hierarchical relations were detected in 13 source ontologies. Domain experts evaluated a random sample of 200 potentially missing hierarchical relations identified in the SNOMED CT (US edition), and 100 in the Gene Ontology. 173 out of 200 and 63 out of 100 potentially missing hierarchical relations were confirmed by domain experts, indicating our method achieved a precision of 86.5% and 63% for the SNOMED CT and Gene Ontology, respectively. In the work of concept enrichment, we introduce a lexical method based on FCA to identify potentially missing concepts. Lexical features (i.e., words appearing in the concept names) are considered as FCA attributes while generating formal context. Applying multistage intersection on FCA attributes results in newly formalized concepts along with bags of words that can be utilized to name the concepts. This method was applied to the Disease or Disorder sub-hierarchy in the 19.08d version of the NCI Thesaurus and identified 8,983 potentially missing concepts. We performed a preliminary evaluation and validated that 592 out of 8,983 potentially missing concepts were included in external ontologies in the UMLS. After obtaining new concepts and their relevant bags of words, we further developed deep learning-based approaches to automatically predict concept names that comply with the naming convention of a specific ontology. We explored simple neural network, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) combined with LSTM. Our experiments showed that the LSTM-based approach achieved the best performance with an F1 score of 63.41% for predicting names for newly added concepts in the March 2018 release of SNOMED CT (US Edition) and an F1 score of 73.95% for naming missing concepts revealed by our previous work. In the last part of this dissertation, extrinsic knowledge is leveraged to collect supporting evidence for the detected incompleteness issues. We present a work in which cross-ontology evaluation based on extrinsic knowledge from the UMLS is utilized to help validate potentially missing hierarchical relations, aiming at relieving the heavy workload of manual review

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

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

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

Structural indicators for effective quality assurance of snomed ct

The Standardized Nomenclature of Medicine -- Clinical Terms (SNOMED CT -- further abbreviated as SCT) has been endorsed as a premier clinical terminology by many national and international organizations. The US Government has chosen SCT to play a significant role in its initiative to promote Electronic Health Record (EH R) country-wide. However, there is evidence suggesting that, at the moment, SCT is not optimally modeled for its intended use by healthcare practitioners. There is a need to perform quality assurance (QA) of SCT to help expedite its use as a reference terminology for clinical purposes as planned for EH R use. The central theme of this dissertation is to define a group-based auditing methodology to effectively identify concepts of SCT that require QA. As such, similarity sets are introduced which are groups of concepts that are lexically identical except for one word. Concepts in a similarity set are expected to be modeled in a consistent way. If not, the set is considered to be inconsistent and submitted for review by an auditor. Initial studies found 38% of such sets to be inconsistent. The effectiveness of these sets is further improved through the use of three structural indicators. Using such indicators as the number of parents, relationships and role groups, up to 70% of the similarity sets and 32.6% of the concepts are found to exhibit inconsistencies. Furthermore, positional similarity sets, which are similarity sets with the same position of the differing word in the concept’s terms, are introduced to improve the likelihood of finding errors at the concept level. This strictness in the position of the differing word increases the lexical similarity between the concepts of a set thereby increasing the contrast between lexical similarities and modeling differences. This increase in contrast increases the likelihood of finding inconsistencies. The effectiveness of positional similarity sets in finding inconsistencies is further improved by using the same three structural indicators as discussed above in the generation of these sets. An analysis of 50 sample sets with differences in the number of relationships reveal 41.6% of the concepts to be inconsistent. Moreover, a study is performed to fully automate the process of suggesting attributes to enhance the modeling of SCT concepts using positional similarity sets. A technique is also used to automatically suggest the corresponding target values. An analysis of 50 sample concepts show that, of the 103 suggested attributes, 67 are manually confirmed to be correct. Finally, a study is conducted to examine the readiness of SCT problem list (PL) to support meaningful use of EHR. The results show that the concepts in PL suffer from the same issues as general SCT concepts, although to a slightly lesser extent, and do require further QA efforts. To support such efforts, structural indicators in the form of the number of parents and the number of words are shown to be effective in ferreting out potentially problematic concepts in which QA efforts should be focused. A structural indicator to find concepts with synonymy problems is also presented by finding pairs of SCT concepts that map to the same UMLS concept

Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

Structural analysis and auditing of SNOMED hierarchies using abstraction networks

SNOMED is one of the leading healthcare terminologies being used worldwide. Due to its sheer volume and continuing expansion, it is inevitable that errors will make their way into SNOMED. Thus, quality assurance is an important part of its maintenance cycle. A structural approach is presented in this dissertation, aiming at developing automated techniques that can aid auditors in the discovery of terminology errors more effectively and efficiently. Large SNOMED hierarchies are partitioned, based primarily on their relationships patterns, into concept groups of more manageable sizes. Three related abstraction networks with respect to a SNOMED hierarchy, namely the area taxonomy, partial-area taxonomy, and disjoint partial-area taxonomy, are derived programmatically from the partitions. Altogether they afford high-level abstraction views of the underlying hierarchy, each with different granularity. The area taxonomy gives a global structural view of a SNOMED hierarchy, while the partial-area taxonomy focuses more on the semantic uniformity and hierarchical proximity of concepts. The disjoint partial-area taxonomy is devised as an enhancement of the partial-area taxonomy and is based on the partition of the entire collection of so-called overlapping concepts into singly-rooted groups. The taxonomies are exploited as the basis for a number of systematic auditing regimens, with a theme that complex concepts are more error-prone and require special attention in auditing activities. In general, group-based auditing is promoted to achieve a more efficient review within semantically uniform groups. Certain concept groups in the different taxonomies are deemed “complex” according to various criteria and thus deserve focused auditing. Examples of these include strict inheritance regions in the partial-area taxonomy and overlapping partial-areas in the disjoint partial-area taxonomy. Multiple hypotheses are formulated to characterize the error distributions and ratios with respect to different concept groups presented by the taxonomies, and thus further establish their efficacy as vehicles for auditing. The methodologies are demonstrated using SNOMED’s Specimen hierarchy as the test bed. Auditing results are reported and analyzed to assess the hypotheses. With the use of the double bootstrap and Fisher’s exact test (two-tailed), the aforementioned hypotheses are confirmed. Auditing on various complex concept groups based on the taxonomies is shown to yield a statistically significant higher proportion of errors