scholarly journals Ontological approach to automatic generation of questions in intelligent learning systems

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (5) ◽  
pp. 44-52
Author(s):  
Li Wenzu
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Structural Characteristics ◽  
Automatic Generation ◽  
Knowledge Bases ◽  
Subject Area ◽  
Learning Systems ◽  
Multiple Choice Questions ◽  
Technical Standards ◽  
Ontological Approach

This article proposes an approach for designing a general subsystem of automatic generation of questions in intelligent learning systems. The designed subsystem allows various types of questions to be automatically generated based on information from the knowledge bases and save the generated questions in the subsystem knowledge base for future use. The main part of the subsystem is the automatic generation module of questions, which allows one to generate questions of various types based on existing question generation strategies in combination with the structural characteristics of knowledge bases built using OSTIS technology. In this article, a variety of strategies for automatically generated questions are proposed, the use of which allows various types of questions to be automatically generated, such as multiple-choice questions, fill-in-the-blank questions, questions of definition interpretation and etc. The most important part of the subsystem is the knowledge base, which stores the ontology of questions, including the question instances themselves. In this article, the knowledge base is constructed based on OSTIS technical standards. The type classification of automatically generated questions was developed, as well as the subject area for storing generated questions and the corresponding ontology described in the knowledge base of the subsystem. The generated questions are stored in the subsystem knowledge base in the form of SC-code, which is the OSTIS technology standard. When testing users, these automatically generated questions are converted to the corresponding natural language form through the natural language interface. Compared with the existing approaches, the approach proposed in this article has certain advantages, and the subsystem designed using this approach can be used in various OSTISbased systems driven by OSTIS technology.

scholarly journals Ontological approach to Chinese text processing

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (6) ◽  
pp. 49-56
Author(s):  
Q. Longwei
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Evaluation System ◽  
Chinese Language ◽  
Knowledge Bases ◽  
Careful Consideration ◽  
Semantic Model ◽  
Ontological Approach

To implement natural language user interface and an intelligent answer to questions, the knowledgebased semantic model for Chinese language processing is proposed. The article gives careful consideration to the existing methods and various knowledge bases for natural language processing. The analysis of these methods has led to the conclusion that in natural language processing, the knowledge base is the most fundamental and crucial part. The knowledge base makes it possible to ensure processing of a natural language based on initially described knowledge and to explain the processing operations. By virtue of the analysis of various methods for constructing knowledge bases about the English and Chinese languages, an ontological approach to the Chinese language processing was proposed. The Chinese language processing model has two main aspects: the design of knowledge base about the Chinese language and the development of ontology-based knowledge processing machine. The proposed approach is aimed at developing a semantic model of knowledge on the Chinese language. As a stage in the implementation of the approach, I designed the ontology of the Chinese language that can be applied for further processing of the language. This paper considers the preliminary version of the ontology and the principle of building a knowledge base about the Chinese language. There are no uniform standards and evaluation system for designing an ontology. Expansion, refinement and evaluation of the ontology require further research.

scholarly journals The 8 Years of Existence of Xper3: State of the art and future developments of the platform

2021 ◽  
Vol 5 ◽  
Author(s):  
Adeline Kerner ◽  
Sylvain Bouquin ◽  
Rémy Portier ◽  
Régine Vignes Lebbe
Keyword(s):  
User Interface ◽  
Natural Language ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Free Access ◽  
Global Biodiversity Information Facility ◽  
Descriptive Data ◽  
Future Developments ◽  
Discriminating Power ◽  
Phenotypic Quality

The Xper3 platform was launched in November 2013 (Saucède et al. 2020). Xper3 is a free web platform that manages descriptive data and provides interactive identification keys. It is a follow-up to Xper (Forget et al. 1986) and Xper2 (Ung et al. 2010). Xper3 is used via web browsers. It offers a collaborative, multi-user interface without local installation. It is compatible with TDWG’s Structured Descriptive Data (SDD) format. Xper3 and its previous version, Xper2, have already been used for various taxonomic groups. In June 2021, 4743 users had created accounts and edited 5756 knowledge bases. Each knowledge base is autonomous and can be published as a free access key link, as a data paper in publications or on websites. The risk of this autonomy and lack of visibility to already existing knowlege bases is possible duplicated content or overlapping effort. Increasingly, users have asked for a public overview of the existing content. A first version of a searching tool is now available online. Explorer lists the databases whose creators have filled in the extended metadata and have accepted the referencing. The user can search by language, taxonomic group, fossil or current, geography, habitat, and key words. New developments of Xper3 are in progress. Some have a first version online, others are in production and the last ones are future projects. We will present an overview of the different projects in progress and for the future. Calculated descriptors are a distinctive feature of Xper3 (Kerner and Vignes Lebbe 2019). These descriptors are automatically computed from other descriptors by using logical operators (Boolean operators). The use of calculated descriptors remains rare. It is necessary to put forward the calculated descriptors to encourage more feedback in order to improve them. The link between Xper3 and Annotate continues to improve (Hays and Kerner 2020). Annotate offers the possibility of tagging images with controlled vocabularies structured in Xper3. Then, an export from Annotate to Xper3, allows automatic filling in of the Xper3 knowledge base with the descriptions (annotations and numerical measures) of virtual specimens, and then comparing specimens to construct species descriptions, etc. Future developments are in progress that will modify the Xper3 architecture in order to have the same functionalities in both local and online versions and to allow various user interfaces from the same knowledge bases. Xper2-specific features, such as merging states, adding notes, adding definitions and/or illustrations in the description tab, having different ways of sorting and filtering the descriptors during an identification (by groups, identification power, alphabetic order, specialist’s choice) have to be added to Xper3. A new tab in Xper3’s interface is being implemented to give an access to various analysis tools, via API (Application Programming Interface), or R programming code: MINSET: minimum list of descriptors sufficient to discriminate all items MINDESCR: minimum set of descriptors to discriminate an item DESCRXP: generating a description in natural language MERGEMOD: proposing to merge states without loss of discriminating power DISTINXP, DISTVAXP: computing similarities between items or descriptors MINSET: minimum list of descriptors sufficient to discriminate all items MINDESCR: minimum set of descriptors to discriminate an item DESCRXP: generating a description in natural language MERGEMOD: proposing to merge states without loss of discriminating power DISTINXP, DISTVAXP: computing similarities between items or descriptors One last project that we would like to implement is an interoperability between Xper3, platforms with biodiversity data (e.g., Global Biodiversity Information Facility, GBIF) and bio-ontologies. An ID field already exists to add Universally Unique IDentifiers (UUID) for taxa. ID fields have to be added for descriptors and states to link them with ontologies e.g., Phenotypic Quality Ontology PATO, Plant Ontology PO. We are interested in discussing future developments to further improve the user interface and develop new tools for the analysis of knowledge bases.

scholarly journals Introducing External Knowledge to Answer Questions with Implicit Temporal Constraints over Knowledge Base

2020 ◽  
Vol 12 (3) ◽  
pp. 45
Author(s):  
Wenqing Wu ◽  
Zhenfang Zhu ◽  
Qiang Lu ◽  
Dianyuan Zhang ◽  
Qiangqiang Guo
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Question Answering ◽  
Knowledge Bases ◽  
Temporal Information ◽  
Temporal Constraints ◽  
External Knowledge ◽  
Question Answering Systems ◽  
Natural Language Question ◽  
Applied Knowledge

Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which obtains the temporal information in the question through attention mechanisms and external knowledge. Specifically, we encode the external knowledge as vectors, and use additive attention between the question and external knowledge to obtain the temporal information, then further enhance the question vector to increase the accuracy. On the WebQuestions benchmark, our method not only performs better with the overall data, but also has excellent performance regarding questions with implicit temporal constraints, which are separate from the overall data. As we use attention mechanisms, our method also offers better interpretability.

scholarly journals A new method for creating Mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems

2021 ◽  
Vol 2061 (1) ◽  
pp. 012123
Author(s):  
D A Chuvikov ◽  
D V Aladin ◽  
L E Adamova ◽  
O O Varlamov ◽  
V G Osipov
Keyword(s):  
Knowledge Base ◽  
Control Systems ◽  
Knowledge Bases ◽  
Subject Area ◽  
Vehicle Control ◽  
Matrix Form ◽  
Intelligent Vehicle ◽  
Knowledge Map ◽  
Knowledge Model ◽  
The Subject

Abstract This research presents a methodology for creating mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems. At its core, this methodology is kind of instruction for analysts facing the task of formalizing knowledge in a given subject area. The result of this formalization is a “knowledge map” created according to a special proposed template. In the future, this template allows forming a knowledge base for a given subject area in the formalism of bipartite oriented mivar networks. As an example, the subject area of ground-based intelligent vehicle control systems is used as a template. The proposed methodology of knowledge formalization makes it possible to simplify the process of creating models in Wi!Mi “Razumator-Consultant” 2.1 and also to level the probability of logical collisions when designing a knowledge model in the formalism of bipartite oriented mivar networks.

scholarly journals Induction of the transformation rules of the natural language problem statement into a semantic model for generating solver

2020 ◽  
pp. 29-39
Author(s):  
Vladimir Viktorovich Pekunov
Keyword(s):  
Natural Language ◽  
Automatic Generation ◽  
Training Sample ◽  
Subject Area ◽  
Semantic Model ◽  
Vector Data ◽  
Literary Sources ◽  
The Subject ◽  
Inducing Schemes ◽  
First Time

The author considers a problem of automatic synthesis (induction) of the rules for transforming the natural language formulation of the problem into a semantic model of the problem. According to this model a program that solves this problem can be generated. The  problem is considered in relation to the system of generation, recognition and transformation of programs PGEN ++. Based on the analysis of literary sources, a combined approach was chosen to solve this problem, within which the rules for transforming the natural language formulation into a semantic model of the problem are generated automatically, and the specifications of the generating classes and the rules for generating a program from the model are written manually by a specialist in a specific subject area. Within the framework of object-event models, for the first time, a mechanism for the automatic generation of recognizing scripts and related entities (CSV tables, XPath functions) was proposed. Generation is based on the analysis of the training sample, which includes sentences describing objects in the subject area, in combination with instances of such objects. The analysis is performed by searching for unique keywords and characteristic grammatical relationships, followed by the application of simple eliminative-inducing schemes. A mechanism for the automatic generation of rules for replenishing / completing the primary recognized models to full meaning ones is also proposed. Such generation is performed by analyzing the relations between the objects of the training sample, taking into account information from the specifications of the classes of the subject area. The proposed schemes have been tested on the subject area "Simple vector data processing", the successful transformation of natural language statements (both included in the training set and modified) into semantic models with the subsequent generation of programs solving the assigned tasks is shown.

scholarly journals AN ONTOLOGICAL APPROACH TO THE DEVELOPMENT OF A KNOWLEDGE BASE OF A DECISION SUPPORT SYSTEM FOR CHOOSING AN INDIVIDUAL EDUCATIONAL TRAJECTORY

2021 ◽  
pp. 29-34
Author(s):  
О.В. Баюк ◽  
И.О. Лозикова
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Support System ◽  
Subject Area ◽  
Educational Trajectories ◽  
Ontological Approach ◽  
The Subject ◽  
Ontological Modeling ◽  
Ontological Model

В статье рассмотрены актуальные вопросы онтологического моделирования базы знаний интеллектуальной системы поддержки принятия решения (СППР) по выбору индивидуальной образовательной траектории. Цель работы – разработка онтологической модели базы знаний системы поддержки принятия решения для построения индивидуальной образовательной траектории обучающегося. Предметом исследования является онтологическое моделирование процесса построения индивидуальной образовательной траектории. Актуальность работы обусловлена индивидуализацией образовательного процесса, который предлагается осуществлять по индивидуальным образовательным траекториям (ИОТ). При построении ИОТ большая роль отводится самостоятельному выбору и принятию решения обучающимся, где необходимо ясно видеть возможный результат своего выбора. Система построения сценариев выбора ИОТ или Система поддержки принятия решения (СППР, Decision Support System, DSS) является интеллектуальной системой, ядром которой будет база знаний на основе онтологической модели. Представлены методологические и технологические аспекты моделирования онтологии базы знаний предметной области индивидуальных образовательных траекторий, применение современных программных средств и стандартов разработки онтологических моделей. Описан метод и технология онтологического моделирования, представлены основные технологические стандарты и их теоретические основания, а также основные процессы методики проектирования. Дано описание предметной области онтологической модели, подход к построению модели базы знаний индивидуальной образовательной траектории, где сформулированы целевые вопросы данной онтологии для определения её масштаба и компетентности. Представлен последовательный процесс онтологического моделирования знаний описанной предметной области. Разработаны правила логического вывода для проверки компетентности онтологической модели и представлены результаты логических вычислений. Представлен выбор семантических технологий для разработки СППР. В заключение сделаны выводы о преимуществах онтологического подхода к разработке базы знаний системы поддержки принятия решения по выбору индивидуальной образовательной траектории и о программной архитектуре реализации данной системы. The article deals with topical issues of ontological modeling of the knowledge base of the intelligent decision support system (DSS) for the choice of an individual educational trajectory. The purpose of the work is to develop an ontological model of the knowledge base of the decision support system for building an individual educational trajectory of a student. The subject of the research is the ontological modeling of the process of building an individual educational trajectory. The relevance of the work is due to the individualization of the educational process, which is proposed to be carried out according to individual educational trajectories (IOT). When building an IOT, a large role is assigned to the independent choice and decision-making of students, where it is necessary to clearly see the possible result of their choice. The system for constructing IOT selection scenarios or the Decision Support System (DSS, Decision Support System, DSS) is an intelligent system, the core of which will be a knowledge base based on an ontological model. Methodological and technological aspects of modeling the ontology of the knowledge base of the subject area of individual educational trajectories, the use of modern software tools and standards for the development of ontological models are presented. The method and technology of ontological modeling are described, the main technical standards and their theoretical foundations are presented, as well as the main processes of the design methodology. The article describes the subject area of the ontological model, an approach to building a knowledge base model of an individual educational trajectory, where the target questions of this ontology are formulated to determine its scale and competence. A sequential process of ontological modeling of knowledge of the described subject area is presented. The rules of logical inference for checking the competence of the ontological model are developed and the results of logical calculations are presented. The choice of semantic technologies for the development of DSS is presented. In conclusion, conclusions are made about the advantages of the ontological approach to the development of the knowledge base of the decision support system for choosing an individual educational trajectory and about the software architecture of the implementation of this system.

Intelligent Support for Building Knowledge Bases for Natural Language Processing

2007 ◽  
pp. 86-113 ◽  
Author(s):  
Son B. Pham ◽  
Achim Hoffmann
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Current Knowledge ◽  
Knowledge Bases ◽  
Intelligent Support ◽  
Current Knowledge Base ◽  
Complex Knowledge ◽  
Comparable Quality

In this chapter we discuss ways of assisting experts to develop complex knowledge bases for a variety of natural language processing tasks. The proposed techniques are embedded into an existing knowledge acquisition framework, KAFTIE, specifically designed for building knowledge bases for natural language processing. Our intelligent agent, the rule suggestion module within KAFTIE, assists the expert by suggesting new rules in order to address incorrect behavior of the current knowledge base. The suggested rules are based on previously entered rules which were “hand-crafted” by the expert. Initial experiments with the new rule suggestion module are very encouraging as they resulted in a more compact knowledge base of comparable quality to a fully hand-crafted knowledge base. At the same time the development time for the more compact knowledge base was considerably reduced.

SWFQA Semantic Web Based Framework for Question Answering

2019 ◽  
Vol 9 (1) ◽  
pp. 88-106
Author(s):  
Irphan Ali ◽  
Divakar Yadav ◽  
Ashok Kumar Sharma
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Knowledge Bases ◽  
Digital Information ◽  
Web Based ◽  
Question Answering System ◽  
User Query

A question answering system aims to provide the correct and quick answer to users' query from a knowledge base. Due to the growth of digital information on the web, information retrieval system is the need of the day. Most recent question answering systems consult knowledge bases to answer a question, after parsing and transforming natural language queries to knowledge base-executable forms. In this article, the authors propose a semantic web-based approach for question answering system that uses natural language processing for analysis and understanding the user query. It employs a “Total Answer Relevance Score” to find the relevance of each answer returned by the system. The results obtained thereof are quite promising. The real-time performance of the system has been evaluated on the answers, extracted from the knowledge base.

scholarly journals The Design of Inductive Modeling Tools Using Ontologies

2020 ◽  
pp. 46-54
Author(s):  
Halyna A. Pidnebesna ◽  
Keyword(s):  
Knowledge Base ◽  
Statistical Data ◽  
Subject Area ◽  
Formal Description ◽  
Complex Objects ◽  
Process Information ◽  
Modeling Tools ◽  
Ontological Approach ◽  
New Knowledge ◽  
The Subject

The architecture of the GMDH-based inductive modeling tools is considered. A feature is the use of the knowledge base in the form of an ontology of the subject area of inductive modeling. The application of the ontological approach to the design of the knowledge base makes it possible to automatically acquire new knowledge, efficiently process information in the modeling of complex objects of different nature according to statistical data, generate queries and obtain logical inferences. Fragments of the GMDH-based inductive modeling ontology are given as an example of creating a formal description of the subject area. The Protege onto editor was used to construct ontologies.

scholarly journals A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations

2017 ◽  
Author(s):  
Kangqi Luo ◽  
Xusheng Luo ◽  
Xianyang Chen ◽  
Kenny Q. Zhu
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Knowledge Base ◽  
Search Algorithm ◽  
Knowledge Bases ◽  
Data Driven ◽  
Learning Approach ◽  
Data Driven Approach ◽  
Structured Knowledge

This paper studies the problem of discovering the structured knowledge representation of binary natural language relations.The representation, known as the schema, generalizes the traditional path of predicates to support more complex semantics.We present a search algorithm to generate schemas over a knowledge base, and propose a data-driven learning approach to discover the most suitable representations to one relation. Evaluation results show that inferred schemas are able to represent precise semantics, and can be used to enrich manually crafted knowledge bases.

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