Representation Language Research Articles

Adding Metalogic Features to Knowledge Representation Languages*

In this paper we present a methodology for introducing customizable metalogic features in logic-based knowledge representation and reasoning languages. The proposed approach is based on concepts of introspection and reflection previously introduced and discussed by various authors in relevant literature. This allows a knowledge engineer to specify enhanced reasoning engines by defining properties and meta-properties of relations as expressible for instance in OWL. We employ meta-level axiom schemata based upon a naming (reification) device. We propose general principles for extending the semantics of “host” formalisms accordingly. Consequently, suitable pre-defined libraries of properties can be made available, while user-defined new schemata are also allowed. We make the specific cases of Answer Set Programming (ASP) and Datalog±, where such features may be part of software engineering toolkits for these programming paradigms. On the one hand, concerning ASP, we extend the programming principles and practice to accommodate the proposed methodology, so as to perform meta-reasoning within the plain ASP semantics. The computational complexity of the resulting framework does not change. On the other hand, we show how metalogic features can significantly enrich Datalog± with minor changes to its operational semantics (provided in terms of “chase”) and, also in this case, no additional complexity burden.

Incorporating symbolic domain knowledge into graph neural networks

Our interest is in scientific problems with the following characteristics: (1) Data are naturally represented as graphs; (2) The amount of data available is typically small; and (3) There is significant domain-knowledge, usually expressed in some symbolic form. These kinds of problems have been addressed effectively in the past by Inductive Logic Programming (ILP), by virtue of 2 important characteristics: (a) The use of a representation language that easily captures the relation encoded in graph-structured data, and (b) The inclusion of prior information encoded as domain-specific relations, that can alleviate problems of data scarcity, and construct new relations. Recent advances have seen the emergence of deep neural networks specifically developed for graph-structured data (Graph-based Neural Networks, or GNNs). While GNNs have been shown to be able to handle graph-structured data, less has been done to investigate the inclusion of domain-knowledge. Here we investigate this aspect of GNNs empirically by employing an operation we term "vertex-enrichment" and denote the corresponding GNNs as "VEGNNs". Using over 70 real-world datasets and substantial amounts of symbolic domain-knowledge, we examine the result of vertex-enrichment across 5 different variants of GNNs. Our results provide support for the following: (a) Inclusion of domain-knowledge by vertex-enrichment can significantly improve the performance of a GNN. That is, the performance VEGNNs is significantly better than GNNs across all GNN variants; (b) The inclusion of domain-specific relations constructed using ILP improves the performance of VEGNNs, across all GNN variants. Taken together, the results provide evidence that it is possible to incorporate symbolic domain knowledge into a GNN, and that ILP can play an important role in providing high-level relationships that are not easily discovered by a GNN.

Transcendental representation of Diophantine equation and some of its inherent properties

We know that Diophantine equations are polynomial equations with integer coefficients and they are having integer solutions. In this paper we are revisits one of the Diophantine Equation xn + yn=znin different perspective, to study some of its inherent properties. In this paper we are proven transcendental representation of above Diophantine equations is zyn2=1+2x2-1. By substituting n = 2, the quadratic Diophantine equation is satisfies Pythagorean theorem, which is having transcendental representation zy=1+2x2-1. Also we are finding all primitive and non primitive Pythagorean triples by choosing of x value from following four disjoint Sets (whose union is becomes to Set of all positive integers). A = x,y,z:zy=1+2x2-1ifxisoddprimenumberoritspowersB = x,y,z:zy=1+2x2p-122-1ifxisoddcompositeanditspowers,forsomep=1,2,3..C = x,y,z:zy=1+2x22-1ifxisgeometricpowerof2 D=x,y,z:zy=1+2x2p22-1ifxisevencompositebutnotgeometricpowerof2,forsomep=1,2,3⋯. And with using of programming coding of ‘c’ language for above transcendental representation of Diophantine equation,we are proven Fermat’s Last Theorem for n > 2.

Knowledge-Base Degrees of Inconsistency: Complexity and Counting

Description logics (DLs) are knowledge representation languages that are used in the field of artificial intelligence (AI). A common technique is to query DL knowledge bases, e.g., by Boolean Datalog queries, and ask for entailment. But real world knowledge-bases are often obtained by combining data from various sources. This, inherently, might result in certain inconsistencies (with respect to a given query) and requires to estimate a degree of inconsistency before using a knowledge-base. In this paper, we provide a complexity analysis of fixed-domain non-entailment (NE) on Datalog programs for well-established families of knowledge bases (KBs). We exhibit a detailed complexity map for the decision cases, counting and projected counting, which may serve as a quantitative measure for inconsistency of a KB with respect to a query. Our results show that NE is natural for the second, third, and fourth level of the polynomial (counting) hierarchy depending on the type of the studied query (stratified, normal, disjunctive) and one level higher for the projected versions. Further, we show fixed-parameter tractability by bounding the treewidth, provide a constructive algorithm, and show its theoretical limitation in terms of conditional lower bounds.

Verifying Plans and Scripts for Robotics Tasks Using Performance Level Profiles

Performance-Level Profiles (PLPs) were introduced as a type of action representation language suitable for capturing the behavior of functional code for robotics. This paper addresses two issues that PLPs raise: (1) Their formal semantics. (2) How to verify a script or a plan that schedule the use of components that have been documented by PLPs. We provide a formal semantics for PLPs by mapping them to probabilistic timed automata (PTAs). We also show how, given a script that refers to components specified using PLPs, we derive a PTA specification of the entire system. This PTA can be used to verify the system’s properties and answers queries about its behavior. Finally, we empirically evaluate an implemented system based on these ideas, demonstrating its scalability. The result is a pragmatic approach for verifying component-based robotic systems.

A method and tools for prototyping components of intelligent systems based on transformations

The paper describes a method and tools for prototyping components of intelligent systems. These means are the parts of the PESoT technology based on generative and visual programming as well as the model transformations. The proposed method redefines the main stages and models of the standardized model-driven approach, in particular, Model Driven Architecture in the context of developing knowledge bases and expert systems that use the logical rules formalism. The description of the method is presented. Proposed tools include languages and software. Specifically, the Rule Visual Modeling Language and Personal Knowledge Base Designer are used for knowledge bases engineering including modeling and codification; the Transformation Model Representation Language and Knowledge Base Development System support model transformations and the rapid creation of computation-independent models. Described tools were applied to solve problems in the field of reliability and safety of technical systems.

Channeling Multiliteracies in Digital Era: A Case Study of EFL Student-Made Video Project in Vocational High School

<p><em>The rapid growth of technology and the new demand of workforce in 21<sup>st</sup> century have intrigued the research to explore multiliteracies in EFL vocational high school through student-made video project. The skills to process information from mass media and operate in a digitized field are deemed to be necessary for vocational high school students. Student-made video project can be considered as an alternative in training students to enhance their multiliteracies skill, which will be explored in this research. Through a case study design, the integration process and salient modes of multiliteracies are captured. Involving a teacher and students of a vocational high school, the findings revealed that the student-made video project are conducted through four main stages, namely experiencing, conceptualizing, analyzing, and applying. The modes of multiliteracies appeared more frequent are visual representation, audio representation, and oral language in the form of video visual, audio of the video, script writing, and classroom discussion.</em><em></em></p>

Research on Translation Processing of Complex Long Sentences Based on Multi-strategy Analysis Algorithm

Syntax analysis depends on a certain grammatical theory. In formal grammar system, dependency grammar has gradually become the focus of researchers. In this paper, an automatic translation method of English-Chinese terms based on multi-strategy fusion is proposed, which uses the term case base to optimize the translation of terms with low evaluation scores obtained from the translation quality estimation method. Through the levenshtein algorithm, we find a number of terms translation examples with high similarity with the source language of the terms to be optimized, including grammatical semantic features, sentence length, punctuation marks, functional words and context conditions, etc., to segment and simplify complex long sentences and compound generate translations. By designing the same knowledge representation for different languages, the algorithm can be universally used for translation systems of different languages.

An Optimization Model for Process Traceability in Case-Based Reasoning Based on Ontology and the Genetic Algorithm

A traceability system can quickly and accurately query product supply chain circulation information. It is difficult to obtain processing information and achieve sharing due to the big data, long duration, large amount of equipment and scattered and easily lost processing information. In tracing the application of the case-based reasoning (CBR) to product processing, we can solve the problem of establishing an accurate and complete mathematical model between the processing information and product quality defects by finding a target record in a large amount of historical data. The traceability model of ontology and CBR uses the web ontology language (OWL) for case representation to simplify the CBR framework for big data, classifies process data and standardizes information storage. A joint optimization algorithm based on the genetic algorithm (GA) and CBR is proposed to establish an optimization model of process traceability, which is applied to case retrieval and reuse. This algorithm optimizes the genetic operator by combining the optimal preservation strategy and roulette selection method and uses an exponential-scale transformation method to stretch the fitness function. The experiments show that the optimized traceability model can infer information from garbled codes, wrong codes and missing messages to quickly determine the problematic products, thus effectively improving the traceability accuracy of product processing.

Creating Design Principles of a Learning Environment for Teaching Vector Spaces

In this study, determining the design principles of a technology-supported learning environment for teaching vector spaces by taking into account the representation languages defined by Hillel (2000), Harel's (2000) pedagogical principles and Sierpinska's (2000) thinking modes on Linear Algebra teaching were intended to be established. The research is a design-based research and three cycles were conducted to determine the design principles for the learning environment. The study group of the first cycle consists of 51, the second cycle's working group was 44, and the third cycle's study group consisted of 11 teacher candidates. The data of the research were obtained by field notes and video recordings. By analyzing the field notes and video recordings, design principles for the learning environment were determined after the first two cycles in light of the literature. The third cycle was carried out with the determined design principles, and the design principles were revised in line with the opinions of the teacher candidates and the course teacher after the application with the reports obtained during the application process. Design principles in the light of the results obtained from the research are as follows ; the use of technology, usage of modes of description, tasks, worksheets, the role of the teacher and group work. It is thought that a learning environment that will be created by paying attention to these design principles will contribute to the pre-service teachers' differentiation and use of different languages, the development of thinking styles, and to meet the principles of concreteness, necessity and generalizability.

Creating Design Principles of a Learning Environment for Teaching Vector Spaces

In this study, determining the design principles of a technology-supported learning environment for teaching vector spaces by taking into account the representation languages defined by Hillel (2000), Harel's (2000) pedagogical principles and Sierpinska's (2000) thinking modes on Linear Algebra teaching were intended to be established. The research is a design-based research and three cycles were conducted to determine the design principles for the learning environment. The study group of the first cycle consists of 51, the second cycle's working group was 44, and the third cycle's study group consisted of 11 teacher candidates. The data of the research were obtained by field notes and video recordings. By analyzing the field notes and video recordings, design principles for the learning environment were determined after the first two cycles in light of the literature. The third cycle was carried out with the determined design principles, and the design principles were revised in line with the opinions of the teacher candidates and the course teacher after the application with the reports obtained during the application process. Design principles in the light of the results obtained from the research are as follows ; the use of technology, usage of modes of description, tasks, worksheets, the role of the teacher and group work. It is thought that a learning environment that will be created by paying attention to these design principles will contribute to the pre-service teachers' differentiation and use of different languages, the development of thinking styles, and to meet the principles of concreteness, necessity and generalizability.

Using UML class diagrams for content ontology design patterns engineering

The paper presents a method for Ontology Design Patterns (ODP) engineering based on the transformation of conceptual models serialized in the XML-like formats. In this paper, we use UML class diagrams as the source of conceptual models. The method includes the following activities: design and serialization of a source conceptual domain model; analysis and transformation of an XML structure of a source conceptual model to an ontological schema model; transformation of an ontological schema model to a content ODP code in the OWL 2 DL format; verification and modification of obtained content ODPs by domain experts. All transformations are implemented with the use of original domain-specific language, namely, Transformation Model Representation Language (TMRL). The proposed method is used for ODPs engineering in tasks of industrial safety inspection of technical systems. Obtained ODPs can be used for knowledge bases and intelligent systems engineering.

Survey on Interpretable Semantic Textual Similarity and its Applications

Both semantic representation and related natural language processing(NLP) tasks has become more popular due to the introduction of distributional semantics. Semantic textual similarity (STS)is one of a task in NLP, it determinesthe similarity based onthe meanings of two shorttexts (sentences). Interpretable STS is the way of giving explanation to semantic similarity between short texts. Giving interpretation is indeedpossible tohuman, but, constructing computational modelsthat explain as human level is challenging. The interpretable STS task give output in natural way with a continuous value on the scale from [0, 5] that represents the strength of semantic relation between pair sentences, where 0 is no similarity and 5 is complete similarity. This paper review all available methods were used in interpretable STS computation, classify them, specifyan existing limitations, and finally give directions for future work. This paper is organized the survey into nine sections as follows: firstly introduction at glance, then chunking techniques and available tools, the next one is rule based approach, the fourth section focus on machine learning approach, after that about works done via neural network, and the finally hybrid approach concerned. Application of interpretable STS, conclusion and future direction is also part of this paper.

Spatio-Temporal Convolutional Networks and N-Ary Ontologies for Human Activity-Aware Robotic System

Endowing a companion robot with cognitive abilities to recognize human daily activities, in particular from body skeletons information, is a significant challenge, which needs complex and novel approaches. Recently, most of the proposed approaches exploit the hand-crafted features or the predefined traversal rules techniques to recognize human daily activities from skeleton information, which often lead to the deficit of robustness and generalization. In this work, a novel hybrid framework for human activity-aware robotic system is proposed. In the low-level, a novel Spatio-Temporal Joint based Convolutional Neural Network (STJ-CNN) is proposed to recognize human daily activities in the ambient environments. In the high-level, novel representation and inference services based on Narrative Knowledge Representation Language (NKRL) are proposed to represent and combine the detected human activities with the ambient events, and to infer the semantic context of the detected activity. Empirical experiments on real-world datasets have been conducted, besides an online demonstration created to validate the proposed approach. The final results show that the proposed approach outperforms the baseline models with a significant improvement up to 24% in terms of $F$-score on DAHLIA dataset.

O uso de infográficos de genética como recurso didático no ensino médio

Infográficos são gêneros de textos multimodais, comumente apresentados no jornalismo em notícias, reportagens ou até em divulgação tecnológica-científica. Por se valer de uma combinação entre linguagens verbal e não verbal, o infográfico é um recurso didático interessante para ser utilizado também no ensino-aprendizagem de jovens do ensino médio. Quando analisamos os conteúdos de genética, observamos nos alunos uma certa dificuldade na compreensão dos conceitos devido a sua complexidade, sendo por esse motivo, o ramo da biologia escolhido como tema para a confecção dos infográficos. Este trabalho visa, portanto, criar e disponibilizar ao menos dez infográficos que contemplem diferentes áreas da genética para educadores e educandos do ensino médio e/ou pré-vestibulandos da comunidade escolar da cidade de Londrina e região. Aproveitando das facilidades decorrentes da evolução digital, serão criados canais de divulgação por meio de redes sociais e outras plataformas que permitirão a divulgação dos infográficos a outras comunidades mais distantes geograficamente, maximizando o compartilhamento de toda informação gerada. Por integrar de forma proporcional as modalidades de informação e imagem, esperamos com esse trabalho contribuir de maneira efetiva tanto com os professores, na transmissão de conceitos genéticos e de suas peculiaridades de forma mais ilustrativa, quanto com os educandos, na assimilação de conteúdos mais densos, de uma forma mais interessante. Acreditamos também que este artigo é capaz de contribuir com futuras pesquisas na área da infografia e com seus benefícios no processo de ensino-aprendizagem.

The computational origin of representation.

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations-whatever they are-must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This theory implements a version of conceptual role semantics by positing an internal universal representation language in which learners may create mental models to capture dynamics they observe in the world. The theory formalizes one account of how truly novel conceptual content may arise, allowing us to explain how even elementary logical and computational operations may be learned from a more primitive basis. I provide an implementation that learns to represent a variety of structures, including logic, number, kinship trees, regular languages, context-free languages, domains of theories like magnetism, dominance hierarchies, list structures, quantification, and computational primitives like repetition, reversal, and recursion. This account is based on simple discrete dynamical processes that could be implemented in a variety of different physical or biological systems. In particular, I describe how the required dynamics can be directly implemented in a connectionist framework. The resulting theory provides an "assembly language" for cognition, where high-level theories of symbolic computation can be implemented in simple dynamics that themselves could be encoded in biologically plausible systems.

An SQL Domain Ontology Learning for Analyzing Hierarchies of Structures in Pre-Learning Assessment Agents

This paper presents the use of description logics (DL) in the definition and development of a Structured Query Language (SQL) domain ontology for a multi-agent based pre-assessment system. Description logics is a knowledge representation language for defining terms or classes, the relationships between classes, their instances, including individuals and literals. In a formal school curriculum, modules of learning are inter-dependent. So, teaching and learning follows an ordered sequence of learning from lower-level module(s) to higher-level ones. This process enables students to gain mastery of lower-level materials before moving up the ladder to higher-level learning. To describe an SQL ontology and its representation for a multi-agent based system application, this paper uses a description logic language to present the organization of learning modules into DesiredConcept $$ $$ , PrerequisiteConcept $$ $$ and LeafNodes $$ $$ as well as their associated relationships, namely, hasPrerequisite and hasKB between the learning modules. The paper thus presents a TBox and an Abox of a DL ontology and further transformation into a first-order predicate for a multi-agent based system that was implemented in Jason.

A Declarative PCG Tool for Casual Users

Declarative programming allows designers to create procedural content generators by providing descriptions of desired artifacts rather than bespoke algorithms to generate them. Unfortunately, these systems are notoriously inaccessible, requiring considerable sophistication with formal systems, and detailed understanding of the impact of equivalent formalizations on the system performance. Imaginarium is a declarative PCG system for tabletop role playing games (TTRPGs). Following Compton’s work on casual creators, it trades expressiveness for accessibility. As with Nelson’s Inform 7, its source language is a highly structured subset of English. A single sentence, such as children are parented by at least one adult can be used to simultaneously introduce the predicates child, adult, and parent, along with a cardinality constraint over the parent relation. We describe the system, its knowledge representation language, and the issues in their design. Together, they allow users with minimal STEM background to engage in playful experimentation.

Timelines in Spectral Composition: A cognitive approach to musical creativity

What takes place in the minds of composers when they struggle to incorporate a given temporal concept into a musical work? Spectral composers have produced detailed theoretical proposals about time in music, but how exactly those ideas influenced their musical practices remains an extremely challenging question. Graphical representations in their sketches provide invaluable clues. Through the analyses of Gérard Grisey’s and Kaija Saariaho’s manuscripts, we show how the theoretical frameworks for the basic cognitive operations of blending and anchoring, which underlie the construction of complex meanings, can shed light on the intricate musical uses of timelines by spectral composers. We combine the universal claims of this cognitive analysis with the diachronic perspective of a musicological study, teasing out the mental paths that these composers may have followed to create novel aesthetic proposals from their experience with graphic representations of sound, mainly spectrograms, and from techniques of electroacoustic studios. Thus we pave the way towards a common language for understanding time representation across electroacoustics and music in general, based on this mixed methodology. Through such shared tenets, the cognitive study of music can reciprocally contribute to burgeoning fields such as time representation, meaning construction and creativity.

Language representation and presurgical language mapping in pediatric epilepsy: A narrative review.

As one of the most common neurological diseases in children, epilepsy affects 0.9-2% of children. Complex interactions among the etiologies of epilepsy, interictal discharges, seizures, and antiepileptic drugs lead to cognitive impairments in children with epilepsy. Since epilepsy is considered as a network disorder, in which seizures have a widespread impact on many parts of the brain, childhood epilepsy can even affect the normal development of language. About 25% of children with epilepsy do not respond to medications; therefore, brain surgery is considered as a treatment option for some of them. Presurgical neuropsychological evaluations including language mapping are recommended to preserve cognitive and language abilities of patients after surgery. Functional magnetic resonance imaging as a non-invasive technique for presurgical language mapping has been widely recommended in many epileptic centers. The present study reviewed language representation and presurgical language mapping in children with epilepsy. Mapping language in children with epilepsy helps to localize the epileptogenic zone, and also, to predict the cognitive outcome of epilepsy surgery and possible cognitive rehabilitation. This review collected information about language representation and language mapping in pediatric epilepsy settings.