Management practice: ontology, structure, content

There is a need to present the results of a search study in the field of management science ontology to highlight its practical attributes. The study of domestic and foreign developments in the field of management of economic systems, management based on system, process and functional approaches allowed us to identify, form and describe the attributes of management practice. At the same time, materials on the structuring, classification and use of management activities from various points of view were used. Based on these generalizations, the interaction of attributes of the management practice of processes and objects in social and economic environments was established. The structure and content of the practice section of management ontology are presented and its relationship with other sections of management science is shown. This research is valuable for research, educational, social and educational activities of processes and objects of economic systems of various types and forms of ownership.

Training and application ofneural network language model for ontology population.

The article considers one of the subtasks of ontology learning -the ontology population, which implies the extension of existing ontology by new instances without changing the ontology structure. A brief overview ofexisting ontology learning approaches and their software implementations is presented. A highly automated technology for ontology population based on training and application of the neural network language model to identify and extract potential instancesof ontology classes from domain texts is proposed. The main stages of its application, as well as the results of its experimental evaluation and the main directions of its further improvement are considered.

Ontology-Aware Deep Learning Enables Ultrafast, Accurate and Interpretable Source Tracking among Sub-Million Microbial Community Samples from Hundreds of Niches

The taxonomical structure of microbial community sample is highly habitat-specific, making it possible for source tracking niches where samples are originated. Current methods face challenges when the number of samples and niches are magnitudes more than current in use, under which circumstances they are unable to accurately source track samples in a timely manner, rendering them difficult in knowledge discovery from sub-million heterogeneous samples. Here, we introduce a deep learning method based on Ontology-aware Neural Network approach, ONN4MST (https://github.com/HUST-NingKang-Lab/ONN4MST), which takes into consideration the ontology structure of niches and the relationship of samples from these ontologically-organized niches. ONN4MST’s superiority in accuracy, speed and robustness have been proven, for example with an accuracy of 0.99 and AUC of 0.97 in a microbial source tracking experiment that 125,823 samples and 114 niches were involved. Moreover, ONN4MST has been utilized on several source tracking applications, showing that it could provide highly-interpretable results from samples with previously less-studied niches, detect microbial contaminants, and identify similar samples from ontologically-remote niches, with high fidelity.

An Automatic Approach to Extending the Consumer Health Vocabulary

PurposeGiven the ubiquitous presence of the internet in our lives, many individuals turn to the web for medical information. A challenge here is that many laypersons (as “consumers”) do not use professional terms found in the medical nomenclature when describing their conditions and searching the internet. The Consumer Health Vocabulary (CHV) ontology, initially developed in 2007, aimed to bridge this gap, although updates have been limited over the last decade. The purpose of this research is to implement a means of automatically creating a hierarchical consumer health vocabulary. This overall purpose is improving consumers’ ability to search for medical conditions and symptoms with an enhanced CHV and improving the search capabilities of our searching and indexing tool HIVE (Helping Interdisciplinary Vocabulary Engineering).Design/methodology/approachThe research design uses ontological fusion, an approach for automatically extracting and integrating the Medical Subject Headings (MeSH) ontology into CHV, and further convert CHV from a flat mapping to a hierarchical ontology. The additional relationships and parent terms from MeSH allow us to uncover relationships between existing terms in the CHV ontology as well. The research design also included improving the search capabilities of HIVE identifying alternate relationships and consolidating them to a single entry.FindingsThe key findings are an improved CHV with a hierarchical structure that enables consumers to search through the ontology and uncover more relationships.Research limitationsThere are some cases where the improved search results in HIVE return terms that are related but not completely synonymous. We present an example and discuss the implications of this result.Practical implicationsThis research makes available an updated and richer CHV ontology using the HIVE tool. Consumers may use this tool to search consumer terminology for medical conditions and symptoms. The HIVE tool will return results about the medical term linked with the consumer term as well as the hierarchy of other medical terms connected to the term.Originality/valueThis is a first attempt in over a decade to improve and enhance the CHV ontology with current terminology and the first research effort to convert CHV's original flat ontology structure to a hierarchical structure. This research also enhances the HIVE infrastructure and provides consumers with a simple, efficient mechanism for searching the CHV ontology and providing meaningful data to consumers.

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

Method of information obtaining from ontology on the basis of a natural language phrase analysis

A method for phrases analyzing in natural languages of inflective type (Ukrainian and Russian) has been developed. The method allows one to outline main expressed ideas and groups of words in the text by which they are stated. The semantic trees of propositions formed in this way, each of which expresses one specific idea, are a convenient source material for constructing queries to the ontology in the SPARQL language. The analysis algorithm is based on the following sequence of basic steps: word tokenize, determining of marker words and phrases, identifying available type of proposition, identifying nouns groups, building a syntactic graph of a sentence, building semantic trees of propositions based on existing types of propositions, substituting parameters from semantic trees of propositions in the corresponding SPARQL query templates. The choice of an appropriate template depends on the type of proposition expressed by a given semantic tree of a proposition. The sets of concepts received as an answer are tied as corresponding answers to the previously defined semantic tree of proposition. In case of non-receipt of information from the ontology, the reduction of noun groups is carried out to express more general concepts and the building queries using them. This allows us to get some answer, although not as accurate as when we use the full noun group. The use of SPARQL query templates requires an a priori known ontology structure, which is also proposed in this paper. Such a system is applicable for dialogue using chat-bots or for automatically receiving answers to questions from the text.