Knowledge Representation System Research Articles

Human-Robot Shared Assembly Taxonomy: A step toward seamless human-robot knowledge transfer

Future manufacturing will witness a shift in human-robot relationships toward collaboration, compassion, and coevolution. This will require seamless human-robot knowledge transfer. Differences in language and knowledge representation hinder the transfer of knowledge between humans and robots. Thus, a unified knowledge representation system that can be shared by humans and robots is essential. Driven by this need in a product assembly scenario, we propose the Human-Robot Shared Assembly Taxonomy (HR-SAT). With HR-SAT, any comprehensive assembly task can be represented as a knowledge graph that both humans and robots can understand. To ensure consistency in task decomposition and representation, we define the key elements of HR-SAT. HR-SAT incorporates rich assembly information and provides necessary information for diverse applications, e.g., process planning, quality checking, and human-robot collaboration. The usage and practicality of HR-SAT are demonstrated through two case studies. As a unified assembly process representation schema, HR-SAT constitutes a critical step toward seamless human-robot knowledge transfer. The specifications of HR-SAT and the two case studies are available at: https://iai-hrc.github.io/hr-sat.

Approach to knowledge management and the development of a multi-agent knowledge representation and processing system

Objectives. A multi-agent knowledge representation and processing system (MKRPS) comprises a distributed artificial intelligence system designed to solve problems that are difficult or impossible to solve using monolithic systems. Solving complex problems in an MKRPS is accomplished by communities of intelligent software agents that use cognitive data structures, logical inference, and machine learning. Intelligent software agents are able to act rationally under conditions of incompleteness and ambiguity of incoming information. The aim of the present work is to identify models and methods, as well as software modules and tools, for use in developing a highly efficient MKRPS.Methods. Agent-based modeling methods were used to formally describe and programmatically simulate the rational behavior of intelligent agents, expert evaluation methods, the mathematical apparatus of automata theory, Markov chains, fuzzy logic, neural networks, and reinforcement learning.Results. An MKRPS structure diagram, a multi-agent solver, and microservices access control diagram were developed. Methods for distribution of intelligent software agents on the MKRPS nodes are proposed along with algorithms for optimizing the logical structure of the distributed knowledge base (DKB) to improve the performance of the MKRPS in terms of volume, cost and time criteria.Conclusions. The proposed approach to the development and use of intelligent software agents combines knowledge-based reasoning mechanisms with neural network models. The developed MKRPS structure and DKB control diagram includes described methods for optimizing the DKB, determining the availability of microservices used by the agents, ensuring the reliability assurance and coordinated functioning of the computing nodes of the system, as well as instrumental software tools to simplify the design and implementation of the MKRPS. The results demonstrate the effectiveness of the presented approach to knowledge management and the development of a high-performance problem-oriented MKRPS.

Information Technology-Based Law Enforcement in Increasing Public Trust in the Police

The present article offers a succinct summary of the scholarly investigations on the function of information technology (IT) in law enforcement and its influence on the level of trust that the public places in this institution. This study aims to investigate the significance of information technology (IT) in law enforcement and its correlation with the level of public confidence in law enforcement agencies. The methodology employed in this study is a literature review, which involves a systematic analysis of pertinent research articles and scholarly papers to extract significant insights. The results underscore the multifaceted uses of information technology in law enforcement, encompassing data mining and crime investigation systems, sophisticated IT methodologies, and knowledge representation and advisory systems. The utilization of IT applications has been observed to augment law enforcement capabilities and promote the establishment of public trust in the police. The study's findings suggest that the appropriate and principled utilization of information technology can facilitate openness, liability, and civic participation, resulting in enhanced rapport between law enforcement entities and the populace. The present study offers significant contributions to law enforcement, policymaking, and research by providing insights on how information technology (IT) can improve public trust in the police.
  

A Knowledge Representation System for the Indian Stock Market

Investors at well-known firms are increasingly becoming interested in stock forecasting as they seek more effective methods to predict market behavior using behavioral finance tools. Accordingly, studies aimed at predicting stock performance are gaining popularity in both academic and business circles. This research aims to develop a knowledge graph-based model for representing stock price movements using fundamental ratios of well-known corporations in India. The paper uses data from 15 ratios taken from the top 50 companies according to market capitalization in India. The data were processed, and different algorithms were used to extract tuples of knowledge from the data. Our technique involves guiding a domain expert through the process of building a knowledge graph. The scripts of the proposed knowledge representation and data could be found here: GitHub. The work can be integrated with a deep learning model for explainable forecasting of stock price.

Semantic Representation of Robot Manipulation with Knowledge Graph

Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic representation framework based on a knowledge graph is presented, including (1) a multi-layer knowledge-representation model, (2) a multi-module knowledge-representation system, and (3) a method to extract manipulation knowledge from multiple sources of information. Moreover, with the aim of generating semantic representations of entities and relations in the knowledge base, a knowledge-graph-embedding method based on graph convolutional neural networks is proposed in order to provide high-precision predictions of factors in manipulation tasks. Through the prediction of action sequences via this embedding method, robots in real-world environments can be effectively guided by the knowledge framework to complete task planning and object-oriented transfer.

SEMANTIC MEDIA: POLITICAL ECONOMY PERSPECTIVES ON PLATFORMIZED FACT PRODUCTION

Large media platforms are now in the habit of providing facts in their products and representing knowledge to various publics. For example, Google’s Knowledge Graph is a database of facts that Google uses to provide quick answers to publics who use their products, while Wikipedia has a product called Wikidata that similarly stores facts about the world in data formats through which various apps can retrieve the data. Microsoft, Amazon, and IBM use similar fact storing and retrieval techniques in their products. This panel introduces papers that take a political economy perspective on such platformaized versions of fact production and examines the underlying infrastructures, histories, and modeling techniques used in such knowledge representation systems. Knowledge representation, long a central topic in archiving work in library and information sciences, is a key feature of platforms and practiced by internet companies more broadly. Much of this work has historically centered on metadata models that seek to organize and describe information in standardized ways. In the context of expanding this data organizing and labeling work into the wider web, one of the main facilitators was the “Semantic Web” project proposed by Tim-Berners Lee and the World Wide Web Consortium (W3C). Today, many of the same principles, technologies, and standards that were proposed by those early projects in metadata modeling from groups like W3C are found at companies like Google and Facebook, organizations like Wikipedia, government portals, and beyond. These platform metadata models are typically produced by industry professionals (e.g., taxonomists, ontologists, knowledge engineers, etc.) who help structure information for algorithmic processing on platforms and their recommender systems. Such structured information is supposed to add a layer of contextual expressivity to web data that would otherwise be more difficult to parse, though the issue of context control is not unproblematic in relation to statements of facts. In many of these automated systems, metadata models contribute to articulating ready-made facts that then travel through these systems and eventually reach the products that are engaged by everyday web users. This panel connects scholars working in information, media studies, and science and technology studies to discuss these semantic technologies. The first paper presents data gathered from interviews with semantic web practitioners who build or have built metadata models at large internet and platform companies. It presents results from a qualitative study of these platform data management professionals (collectively referred to as “metadata modelers”) and draws from unstructured interviews (n=10) and archival research. The paper describes the image of a metadata ecology along with selected work-related contestations expressed by interview subjects regarding some of the difficulties and intractable problems in metadata modeling work. The paper includes a discussion of the political economy of platform semantics through an examination of critical semantic web literature and ends with some policy concerns. The second paper translates the method of tracing “traveling facts” from science studies to the context of online knowledge about evolving, historic events. The goal is to understand the socio-political impact of the semantic web as it has been implemented by monopolistic digital platforms and how such practices intersect in the context of Wikipedia, where the majority of knowledge graph entities are sourced from. The paper describes how the adoption (and domination) by platform companies of linked data has catalyzed a re-shaping of web content to accord with the question and answer linked data formats, weakening the power of open content licenses to support local knowledge and consolidating the power of algorithmic knowledge systems that favor knowledge monopolies. The third paper discusses building a semantic foundation for machine learning and examines how information infrastructures that convey meaning are intimately tied to colonial labor relations. It traces the practice of building a digital infrastructure that enables machines to learn from human language. The paper describes examples from an ethnographic study of semantic computing and its infrastructuring practices to show how such techniques are materially and discursively performative in their co-emergence with techno-epistemic discourses and politico-economic structures. It examines sociomaterial process in which classifications, standards, metadata, and methods co-emerge with processes of signification that reconstitute and/or shift hegemonic ecologies of knowledge. The fourth paper evaluates and examines the ethics of “free” data (CC-0) in Wikidata by evaluating the sources and usage of data from and within Wikidata. From knowledge graphs to AI training, Wikidata is the semantic web platform that is being used across the Internet to power new platforms. Through a consideration of the ways in which Wikidata scrapes Wikipedia’s “share alike” knowledge through scraping metadata and the significant donations and partnerships from large technology firms (Google in particular), this paper addresses ethical concerns within the largest semantic web platform, how these transformations of knowledge alienate donated volunteer labor, and offers some ways in which these issues might be mitigated.

KinRob: An ontology based robot for solving kinematic problems

Intelligent answering technology, which enables computers to solve problems automatically, is often used to develop tutorial systems, and has a wide range of application prospects. However, due to the lack of linguistic analysis and understanding methods, there are few researches on intelligent algorithms for solving kinematics problems. Developing such an algorithm is challenging, because solving kinematics problems is a complex task that includes text understanding, problem analysis, and automatic solution. To understand all these complexities involved in kinematics problems requires background knowledge. And only when an automatic solver contains a powerful internal knowledge representation system can it perform these tasks. We, thus, develop KinRob, an tutorial system for solving kinematics problems by combining neural network and ontology. Firstly, we propose an ontology for KinRob, which defines the knowledge of kinematics, and can help the robot understand a kinematics problem. Secondly, to match the text in natural language with the ontology, we propose a novel tagging scheme based on the kinematic problem understanding model in named entity recognition (NER). Finally, extensive experiments are conducted, and the experimental results show that the performance of the proposed method on a dataset of kinematic problems from authoritative sources better than the baseline algorithms.

Towards World Identification in Description Logics

The logical analysis of the usability of nominals, that are introduced by Hybrid Logic, informal descriptions of the world (within modern knowledge representation systems and semantic systems) is very important because nominals, as second sorts of propositional symbols, can support logical identification of the described world at specific states. This paper will focus on answering the philosophical-logical question of ‘how a fundamental world description in the description logic (DL) and a nominal can be related to each other?’. Based on my assumption that nominals can support more adequate identification of the world in DL, this paper will deal with the concept of ‘world identification’. Accordingly, based on a logical-terminological analysis of nominals, it will analyse hybridised fundamental world descriptions. Relying on the outcomes, this research will reach the idea that we can have a hybrid description logic with the analysed concepts.

INSTRUCTION FOR COMPILERS OF THE HISTORICAL DICTIONARY OF LINGUISTIC TERMS

The article deals with description of compiling the Historical Dictionary of Linguistic Terms that will present formal and semantic  evolution of the Ukrainian linguistic terms from the earliest times to the middle of the 20th century. Compilation of the dictionary involves the following stages: 1. Creation of the diachronic corpus of linguistic texts (ISTLING) as a source of  information about the history of Ukrainian linguistic terms (it will include not only sources in Ukrainian, but also in Polish, German, Russian, etc. and present their role in the formation of the Ukrainian linguistic terminology). 2. Compilation of linguistic profiles of sources – preliminary general information about the peculiarities of the use of linguistic terms in these sources. 3. Formation of the register which task is not only to present the lexeme recorded in the texts of the studied period, but also to show its unity (the term and its variants), its  word-forming connections, grammatical forms (selectively), its use in texts in different periods, fixation in dictionaries. Each term (even if it occurs only once in the corpus) recorded in the sources is a registered unit. 4. Generalization of material (from linguistic profiles of  dictionary sources) in diachronic cognitive maps, i.e. in the system of linguistic knowledge representation, which reflects the processes of conceptualization and categorization of the linguistic term concept in the projection onto the linguistic term that nominates it. 5. Modeling  an entry for the dictionary based on a diachronic cognitive map of a linguistic term. It has 8 zones: vocable, or register word; modern  definition of the term; etymology of the term (if possible); the first fixation of the modern term; formal versions of the term in chronological  order with certification; semantic development of the term; peculiarities of categorization of the term concept (if the material provides such  an opportunity); term «projection» in modern linguistics. The article contains fragments of the register, samples of linguistic profiles of individual sources, diachronic cognitive maps of linguistic  terms, as well as some examples of dictionary entries.

Role of frame structure in the development of KRS for learning dialogues.

Dialogues are building blocks of tasks and non-tasks of communication, which happen between objects in the universe. Each dialogue is a source of linguistic knowledge within a natural language that explains and elaborates with frame structure in general. In this paper, it is noticed that various forms like (nouns, pronouns, yes-no questions and deletion) are essential part of each dialogue structure (DSS) in Chandan’s work ਜੜਾਂ/Roots. With the help of frames, knowledge representation system (KRS) is prepared for such dialogues in Punjabi. On the other hand, it is argued that highest numbers of nouns are total 45 in DS2 and only 1 deletion case finds in DS3. While DS1 and DS2 both have similar number of 2-2 cases of yes-no questions. The overall evaluation is successfully matched with proposed an algorithm based on frames.

Action Rule Mining With Predications on Semantic Representations of Data

Without a doubt, the World Wide Web (WWW) has already altered the way we share knowledge. The exploitation of the web is one of the biggest challenges in the area of intelligent information management. The Semantic Web was presented to ease this situation by taking the WWW into a distributed global system for knowledge representation and computing. In this work, we study action rule mining in the semantic web data and aim to assist users in uncovering previously unknown and potentially useful workable strategies. We present rule-based action rules and object-based action rules for extracting actionable patterns from a large graph dataset without the extra step of converting graph data into transaction data.

Extraction of Event-Related Information from Text for the Representation of Cultural Heritage

In knowledge representation systems for Cultural Heritage (CH) there is a vast amount of curated textual information for CH objects and sites. However, the large-scale study of the accumulated knowledge is difficult as long as it is provided in the form of free text. By extracting the most significant pieces of information from textual descriptions of CH objects and sites and compiling them in a single comprehensive knowledge graph, conforming to a standard would facilitate its exploitation from multiple perspectives including study, presentation and narratives. The method proposed by this research work was to employ Natural Language Processing, and reinforcement learning for semantic knowledge extraction, and a knowledge representation standard of the CH domain for the knowledge graph thus making the extracted knowledge directly compatible with linked open data platforms and CH representation systems.

An ontology-driven framework for knowledge representation of digital extortion attacks

With the COVID-19 pandemic and the growing influence of the Internet in critical sectors of industry and society, cyberattacks have not only not declined, but have risen sharply. In the meantime, ransomware is at the forefront of the most devastating threats that have launched the lucrative illegal business. Due to the proliferation and variety of ransomware forays, there is a need for a new theory of categories. The intricacy and multiplicity of components involved in digital extortions entails the construction of a knowledge representation system that is able to organize large volumes of information from heterogeneous sources in a formal structured format and infer new knowledge from it. This paper suggests and develops a dedicated ontology of digital blackmails, called Rantology, with a particular focus on ransomware assaults. The logic coded in this ontology allows to assess the maliciousness of programs based on various factors, including called API functions and their behaviors. The proposed framework can be used to facilitate interoperability between cybersecurity experts and knowledge-based systems, and identify sensitive points for surveillance. The evaluation results based on several criteria confirm the adequacy of the suggested ontology in terms of clarity, modularity, consistency, coverage and inheritance richness.

Anthology: A Social Simulation Framework

Social simulation research seeks to understand the dynamics of complex human behavior by simulating populations of individual decision-makers as multi-agent systems. However, prior work in games and entertainment fail to account for interactions between social behavior, geography, and relationships in a manner that allows researchers to easily reuse their frameworks and model social characters. We present Anthology, an extensible software framework for modeling human social systems, within the context of an ongoing research agenda to integrate AI techniques from social simulation games and computational social science to enable researchers to model and reason about the complex dynamics of human social behavior. Anthology comprises a motive-based agent decision making algorithm; a knowledge representation system for relationships; a flexible specification language for precondition-effect-style actions; a user interface to inspect and interact with the simulation as it runs in real-time; and an extensive user documentation and reference manual. We describe our participatory research design process used for the developing Anthology, the state of the current system, it's limitations and our future development directions.

Knowledge Authoring with Factual English

Knowledge representation and reasoning (KRR) systems represent knowledge as collections of facts and rules. Like databases, KRR systems contain information about domains of human activities like industrial enterprises, science, and business. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. One solution could be to extract knowledge from English text, and a number of works have attempted to do so (OpenSesame, Google's Sling, etc.). Unfortunately, at present, extraction of logical facts from unrestricted natural language is still too inaccurate to be used for reasoning, while restricting the grammar of the language (so-called controlled natural language, or CNL) is hard for the users to learn and use. Nevertheless, some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. In this paper, we address this issue by transplanting the KALM framework to a neural natural language parser, mStanza. Here we limit our attention to authoring facts and queries and therefore our focus is what we call factual English statements. Authoring other types of knowledge, such as rules, will be considered in our followup work. As it turns out, neural network based parsers have problems of their own and the mistakes they make range from part-of-speech tagging to lemmatization to dependency errors. We present a number of techniques for combating these problems and test the new system, KALMFL (i.e., KALM for factual language), on a number of benchmarks, which show KALMFL achieves correctness in excess of 95%.

Semantic Encyclopedias and Boolean Dreams

When metadata becomes knowledge, opportunities for multiplicity and risks of harm and exclusion arise. As GLAM institutions contribute to the Semantic Web, we must pay attention to the implications of participation. While the Semantic Web grew out of the flourishing of web technologies in the 1990s, recognizing its roots in classical/symbolic AI (referred to as Good Old Fashioned Artificial Intelligence, or GOFAI)—in particular, expert systems and knowledge representation—encourages critical questions like: which problems from knowledge representation and expert systems does the Semantic Web inherit? Are GOFAI failures really failures, or does the gap between rhetoric and practice point to generative possibilities (some of which can now be seen in Semantic Web initiatives)? What can we learn from AI critics, feminist approaches, and the unmasking of encyclopedic neutrality? This research article will explore how critiques of AI expert systems and Cyc, an ongoing project to create a common sense knowledge base, might apply to Semantic Webefforts like Wikipedia, Wikidata, DBpedia, and Schema.org.

Graph-based construction of minimal models

Reasoning with minimal models is at the heart of many knowledge representation systems. Yet, it turns out that this task is formidable even when very simple theories are considered. It is, therefore, crucial to devise methods that attain good performances in most cases. To this end, a path to follow is to find ways to break the task at hand into several sub-tasks that can be solved separately and in parallel. And, in fact, we show that minimal models of positive propositional theories can be decomposed based on the structure of the dependency graph of the theories: this observation turns out to be useful for many applications involving computation with minimal models. In particular, we introduce a new algorithm for minimal model finding based on model decomposition. The algorithm temporal worst-case complexity is exponential in the size s of the largest connected component of the dependency graph, but the actual cost depends on the size of the largest component actually encountered at run time that can be far smaller than s, and on the class of theories to which components belong. For example, if all components reduce to either an Head Cycle Free or an Head Elementary-set Free theory, the algorithm is polynomial in the size of the theory.

ОНТОЛОГИЧЕСКИЙ ПОДХОД В СОЗДАНИИ РОБОТОТЕХНИЧЕСКИХ КОМПЛЕКСОВ С ПОВЫШЕННОЙ СТЕПЕНЬЮ АВТОНОМНОСТИ

The aspects necessary for the implementation of robotic complexes with an increased degree ofautonomy (RC with IDA) in practical work are considered. The distinctive features of such complexes,the needs of the corresponding intelligent information control systems (IIСS) are indicated. The requirementof situational awareness is highlighted and, as a consequence, the need for a diverse systemof knowledge representation, means of perception of the external environment and comparison of operationalinformation with models and a priori information about this environment. In addition, it is pointedout the need to automate the processes of creating RC with IDA, accessibility, and simplification of theiruse. In order to answer these questions, the paper proposes to use the concept and mechanisms of ontologiesin relation to autonomous robotics. Examples of existing solutions in this area are given. In robotics,ontologies are used to define and conceptualize knowledge accepted by the community, using aformal description that is machine-readable, shared, and contains the flexibility to justify this knowledgein order to derive additional information. Ontologies are of considerable interest for multi-agent systemsfor organizing interaction between agents and with other systems in heterogeneous environments,the possibility of reuse and support for the development of new RCs. The author describes the constructionof an ontology proposed by the author in such an applied field as information support for targetedmovements of autonomous ground vehicles based on technical vision systems. All consideration is conductedin the configuration space of the information and control systems of the RC with IDA. This spaceallows you to aggregate a large number of different technologies used in the construction of RC. Theembodiment of a particular system in this space corresponds to the "assembly point". The coordinationof the forms of knowledge representation in the IICS is ensured by the consistent consideration of planesin this space. As a connecting link – a means for automated translation of descriptions of descriptiveontologies into descriptions of functional, machine-readable ontologies, the use of the language of information-motor actions and interpretive navigation commands is proposed. In conclusion, the shorttermprospects for the development of the described approach are considered, and wishes are expressedto the domestic community of roboticists.

Language-Agnostic Knowledge Representation for a Truly Multilingual Semantic Web

As the Internet users’ communities and the information on the web are increasing exponentially, that too associated with a wide range of different cultures and languages; meaningful search in a multilingual setting is required. It is crucial to develop a simple to use, granular, and adaptable knowledge representation system that could constitute the core of Semantic Web. It should be well-prepared for multilingual and multicultural settings by virtue of internationalizing the code. In this work, a model for ontology-based language-agnostic Semantic Web application architecture is offered which is independent of today’s heterogeneous encoding of language resources and provides smooth exploitation. The suggested system architecture enables a quick and meaningful search for information independent of the end user’s preferred language and cultural conventions, an easy extension of the application with new information, as well as its easy localization for new language contexts. The model’s usability is proven by a prototype application simulation in the domain of Indian biodiversity.

Methods and techniques for management of ontolodgy-based knowledge representation models in the context of BIG data

Ontology-based knowledge representation models in the context of big data are one way to reduce complexity for data processing across methods of semantic description. This research paper aims at providing an overview of the methods and techniques for efficient management of the ontology-based models that improve big data systems. For this case, the shapes constraint language (SHACL) for information validation was reviewed as the key method. The knowledge representation systems and reasoners are studied and reviewed in the paper as well. It describes approaches based on ontologies in the context of big data. The proper management of ontology-based knowledge representation models through offered methods and techniques brings improved data integration, big data quality, and business process integration.