Automation of the development of ontologies of scientific subject domains based on ontology design patterns

At present, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject domains (SSDs). However, the development of an ontology is a rather complicated and time-consuming process. All indications are that when developing SSDs ontologies, it is especially effective to use ontology design patterns (ODPs). This is due to the fact that the SSD ontology, as a rule, contains a large number of typical frag-ments, which are well described by the ODPs. In addition, due to the fact that the use of ODPs greatly facilitates the development of an SSD ontology, it is possible to involve experts in a modeled SSD not possessing the skills of onto-logical modeling. To obtain an ontology that adequately describes the SSD, it is necessary to process a huge number of publications relevant to the modeled SSD. It is possible to facilitate and accelerate the process of populating the ontolo-gy with information from such sources by using the lexical and syntactic patterns of ontological design. The paper pre-sents an approach to the automated development of SSDs ontologies based on a system of heterogeneous ODPs. This system includes both ODPs intended for ontology developers and lexical and syntactic patterns built on the basis of the above-mentioned types of the ODPs and the current version of the SSD ontology.

Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system.

The JECS Core Journal Collection

An understanding of core journal collections is important for the development and management of periodical collections. This investigation explores the structure of a core journal collection related to the Journal of Early Christian Studies, a relatively new periodical in the sub-discipline of church history. Citations of journals extracted from three randomly selected volumes were tabulated and ranked. Library of Congress subject headings were used to categorize the subjects covered by the top-ranked cited journals. It was concluded that a core of nine journals contributed twenty-five percent of all citations. Church history journals predominated as the source of cited material; Roman and Byzantine subject journals were also highly cited. The core journal collection is concentrated around these three subject domains and, wherever early Christian studies is a prominent curricular focus, it is recommended that these nine journals be available in the library.

Seven Primary Data Types in Citizen Science Determine Data Quality Requirements and Methods

Data quality (DQ) is a major concern in citizen science (CS) programs and is often raised as an issue among critics of the CS approach. We examined CS programs and reviewed the kinds of data they produce to inform CS communities of strategies of DQ control. From our review of the literature and our experiences with CS, we identified seven primary types of data contributions. Citizens can carry instrument packages, invent or modify algorithms, sort and classify physical objects, sort and classify digital objects, collect physical objects, collect digital objects, and report observations. We found that data types were not constrained by subject domains, a CS program may use multiple types, and DQ requirements and evaluation strategies vary according to the data types. These types are useful for identifying structural similarities among programs across subject domains. We conclude that blanket criticism of the CS data quality is no longer appropriate. In addition to the details of specific programs and variability among individuals, discussions can fruitfully focus on the data types in a program and the specific methods being used for DQ control as dictated or appropriate for the type. Programs can reduce doubts about their DQ by becoming more explicit in communicating their data management practices.

Software Design Based on Using Ontologies and Algorithm Algebra

This chapter proposes an approach to the automated development of programs based on the use of ontological facilities and algebra-algorithmic toolkit for design and synthesis of programs (IDS). The program design ontology, developed using Protégé system and represented in OWL format, includes concepts from various subject domains (sorting, meteorological forecasting, and other) intended for description of main program objects: data, functions, and relations between them. IDS toolkit generates the initial (skeleton) algorithm scheme based on its ontological description extracted from OWL file. The generated scheme is the basis of further design of the algorithm and synthesis of a program in a target programming language. The approach is illustrated by examples of developing parallel sorting, meteorological forecasting, and N-body simulation programs.

Semantic matching of State Rubricator of Sci-tech Information and OECD headings

The study was completed to solve the problems of information compatibility and integration of subject subspaces into the single one and information retrieval from heterogeneous resources classified within different classifications of sci-tech information. Based on intellectual analysis and classification synonymity achieved through semantic mapping of hierarchy branches, the tables of SRSTI-OECD headings interreflection were designed. The mapping tables are supplemented with indices comprising Russian-English and English-Russian keywords and word combinations to classify OECD subject domains. The statistics of defined semantic matches distribution of classification numbers in SRSTI thematical classes is included. The SRSTI-OECD «adapter» (semantic links between the classifications and two-language subject indices) makes the component of the single network of interrelated classification systems being developed at the Russian National Public Library for Science and Technology

APPROACHES TO KNOWLEDGE EXTRACTION IN SCIENTIFIC SUBJECT DOMAINS

Изучается проблема извлечения знаний из разнородных оцифрованных данных. Дается обзор метрических характеристик, применимых к сравнению предметных областей по формальным признакам. Обсуждаются методы искусственного интеллекта, используемые для классификации информационных ресурсов по предметным областям, и области их применения. Приложения иллюстрируются на примере математических предметных областей.