Structured Knowledge Representation Research Articles

A Knowledge Graph-Driven Analysis of the Interlinkages among the Sustainable Development Goal Indicators in Different Spatial Resolutions

The way towards sustainable development is paved through the commitment to the 17 Sustainable Development Goals (SDGs), which encompass a wide range of global challenges. The successful progress of these goals depends on the identification and understanding of their interconnected nature. A plethora of data is made available for tracking targets related to the SDGs at country, regional and urban levels. However, various challenges are identified to semantically align and homogeneously represent such data to improve their interoperability, comparability and analysis. In the current work, we provide an innovative solution for analyzing SDG-oriented data based on the development of a Knowledge Graph that provides access to semantically aligned data for the SDGs. We consider Knowledge Graphs as a suitable technology for the representation of data related to the interlinkages among SDGs, since they provide a structured representation of knowledge that incorporates entities, relationships and attributes, organized in a graph format. We examine the interlinkages among indicators of the same SDG, as well as across indicators of the various SDGs. Such interlinkages are further evaluated as synergies or trade-offs. Our analysis is applied in country and regional levels, considering various constraints in terms of data quality and availability. In total 476 synergies are identified at the national level among the SDGs, compared to 140 trade-offs. The SDGs that mostly participate in the synergies are SDGs 17, 10, 9 and 8, while SDGs 7 and 16 participate in most of the trade-offs. At the regional level, SDGs 8, 4 and 9 are more active in terms of interlinkages.

A Survey of Multi-modal Knowledge Graphs: Technologies and Trends

In recent years, Knowledge Graphs (KGs) have played a crucial role in the development of advanced knowledge-intensive applications, such as recommender systems and semantic search. However, the human sensory system is inherently multi-modal, as objects around us are often represented by a combination of multiple signals, such as visual and textual. Consequently, Multi-modal Knowledge Graphs (MMKGs), which combine structured knowledge representation with multiple modalities, represent a powerful extension of KGs. Although MMKGs can handle certain types of tasks (e.g., visual query answering) or queries that standard KGs cannot process, and they can effectively tackle some standard problems (e.g., entity alignment), we lack a widely accepted definition of MMKG. In this survey, we provide a rigorous definition of MMKGs along with a classification scheme based on how existing approaches address four fundamental challenges: representation, fusion, alignment, and translation, which are crucial to improving an MMKG. Our classification scheme is flexible and allows for easy incorporation of new approaches, as well as a comparison of two approaches in terms of how they address one of the fundamental challenges mentioned above. As the first comprehensive survey of MMKG, this article aims to inspire and provide a reference for relevant researchers in the field of Artificial Intelligence.

A turning point few-shot learning for COVID-19 diagnosis

The current landscape of medical diagnostics grapples with a critical challenge posed by the limitations of existing meta-learning techniques in interpreting complex representations from limited labeled data, particularly evident in COVID-19 datasets. In response to this gap, our paper introduces a groundbreaking Turning Point (TP) methodology designed to enhance the interpretability of machine learning diagnostics, specifically addressing the shortcomings highlighted in conventional meta-learning approaches. Our Turning Point-based Few-Shot Learning (TPFSL) model goes beyond traditional FSL methods by embracing structured knowledge representation, departing from unstructured metric spaces. We found that our TPFSL model outperformed the state-of-the-art models by an average of 4.50% in 1-shot learning and 4.43% in 5-shot learning after conducting extensive benchmarking on the COVCT, SARSCOV2, and SIRM datasets. Across all datasets studied, TPFSL outperforms the ProtoNet benchmark in 1-shot classification by 12.966% and in 5-shot classification by 11.033%. The importance of TP density, the structure of the network, and placement in improving model performance have been shown by a thorough set of ablation investigations; with its revolutionary TPFSL model, which tackles the shortcomings of current meta-learning methods head-on, COVID-19 diagnostic procedures can be made more accurate and valid in clinical situations.

Stochastic neuro-fuzzy system implemented in memristor crossbar arrays.

Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaOx/HfOx/TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.

Automatic Knowledge Graph matching via Self-adaptive Designed Genetic Programming

Knowledge Graph (KG) provides a structured representation of domain knowledge by formally defining entities and their relationships. However, distinct communities tend to employ different terminologies and granularity levels to describe the same entity, leading to the KG heterogeneity issue that hampers their communications. KG matching can identify semantically similar entities in two KGs, which is an effective solution to this problem. Similarity Measures (SMs) are the foundation of the KG matching technique, and due to the complexity of entity heterogeneity, it is necessary to construct a high-level SM by selecting and combining the basic SMs. However, the large number of SMs and their intricate relationships make SM construction an open challenge. Inspired by the success of Evolutionary Algorithms (EA) in addressing the entity matching problem, this work further proposes a novel Self-adaptive Designed Genetic Programming (SDGP) to automatically construct the SM for KG matching. To overcome the drawbacks of the classic EA-based matching methods, a new individual representation and a novel fitness function are proposed to enable SDGP automatically explore the SM selection and combination. Then, a new Adaptive Automatic Design (AAD) method is introduced to adaptively trade off SDGP’s exploration and exploitation, which can determine the timing of AAD and efficiently determine the suitable breeding operators and control parameters for SDGP. The experiment uses the Ontology Alignment Evaluation Initiative’s Knowledge Graph (KG) data set to test the performance of SDGP. The experimental results show that SDGP can effectively determine high-quality KG alignments, which significantly outperform state-of-the-art KG matching methods.

CDRGN-SDE: Cross-Dimensional Recurrent Graph Network with neural Stochastic Differential Equation for temporal knowledge graph embedding

The temporal knowledge graph builds upon the static knowledge graph by introducing the time dimension and finds extensive applications in real artificial intelligence scenarios. However, the temporal knowledge graph’s incompleteness restricts the applicability of numerous temporal knowledge graph tasks. Temporal knowledge graph embedding (TKGE) is a representation learning technique that effectively addresses the temporal knowledge graph’s incompleteness. These methods aim to map entities and relations in the knowledge graph to a low-dimensional vector space while capturing features over time. However, the current TKGE methods encounter three main challenges: (1) how to resolve the continuity problem of dynamic features in the continuous time domain; (2) how to simultaneously model the dependence of structural knowledge representation on time and dimension; (3) how to address the heterogeneity of time representation in a temporal knowledge graph. We propose the CDRGN-SDE, a Cross-Dimensional Recurrent Graph Network with a neural Stochastic Differential Equation framework, to tackle these challenges. The CDRGN-SDE model effectively addresses the above challenges and establishes a unified framework: (1) we employ a neural ordinary differential equation with stochastic noise to simulate the continuity of dynamic systems in the continuous time domain; (2) we propose a Dimension-Segment-Wise (DSW) embedding method for TKGE, which effectively integrates time and dimensional information; (3) we introduce a simple and effective time representation method that integrates complex time features into the model. Experimental evaluations on benchmark datasets demonstrate that the CDRGN-SDE model outperforms the state-of-the-art temporal knowledge graph reasoning models.

Research on Knowledge Graph Multiple Environment-Aware Recommendation Algorithm for Teaching Course Recommendation

Abstract With the rapid development of education informatization and intelligence, personalized teaching and course recommendations have gradually become an important research direction in the field of education technology. A knowledge graph, as a structured knowledge representation and organization, provides rich semantic information and a relationship network for teaching course recommendations. Based on this research background, this paper focuses on the research of knowledge graph multivariate environment-aware recommendation algorithms for teaching courses. The article firstly constructs the ontology of course resources, researches and analyzes the methods and steps of acquiring and processing data in order to build the knowledge graph, and then constructs the framework of the recommendation model combining multivariate perception in order to further analyze and explore the user’s course-aware representation learning and the user’s course-aware embedded learning. Embedded Learning, and finally, the teaching course of a school as an empirical study, it is shown that the performance of Ripple_mlp+ and Ripple_mlp in this article are higher than the benchmark algorithms through the experimental class and the control class of the pre and post-test scores have been sorted out and counted. The experimental students’ scores are steadily increasing, and the overall scores are better than the control class. In the experimental class, the significance value is 0.013, indicating that there is a significant difference between the midterm and final grades of the experimental class. Through the paired samples t-test on the midterm and final grades of the experimental class, after analyzing and comparing the midterm and final grades of students at different levels, it can be concluded that the t-value of the superior students is −3.326. The value of significance is 0.049, indicating that there is a significant difference between the midterm and final grades of the exceptional students. The T-value for intermediate students is −0.134, and the value of significance is 0.045, which indicates that there is a significant difference in the midterm final grades of intermediate students. The T-value of −1.623 and the value of significance is 0.534 for advanced students indicates that the difference in midterm final grades of advanced students is not significant.

THE FRAME STRUCTURE OF THE CONCEPT “SECRET” IN ENGLISH LITERARY DISCOURSE

The article is focused on the study of the frame structure of the concept of secret based on English-language literary fiction. A concept is a mental representation of knowledge about a certain object, phenomenon, feature, or action. There are different types of knowledge representation structures: mental model, scheme, image, frame, scenario, gestalt. It is the frame that allows us to cognitively represent knowledge about the world. A frame is a structure of information used to represent a stereotypical situation, which is why this study focuses on the frame representation of the analyzed concept. The analysis of the dictionary definitions of the concept of secret has enabled us to identify the following conceptual features: information known to a narrow circle, confidentiality (verbalized by the lexemes — known by few people; shared confidentially with a few; should not be told to others; kept hidden); method for achieving a goal/success (method for doing sth well; the best way of achieving something); something mysterious, incomprehensible/something that is difficult to explain (mystery; cannot be explained/understood; unrevealed). Various aspects of the concept of secret are represented by subject frames (possessing one’s own or someone else’s secret; concealing or disclosing a secret; the existence of a secret in a certain social setting; discussing a secret; a secret as a surprise, or, by contrast, as shameful information), action frames (to report a secret; to hide a secret; to demand to reveal a secret; to promise to keep a secret), as well as by possessive frames (to possess a secret). The analysis of the characters’ speech in English-language novels has been helpful to unveil the following frame units of the concept secret: on the part of the subject: keeping a secret (the need to keep a secret, the protection of a secret by lying) and disclosure of a secret (due to situational necessity, one’s own emotional nature, the desire to show off one’s own knowledge, accidental disclosure of a secret, the use of “secret” knowledge as hints or warnings); on the part of the object: the demand to disclose a secret (asking the interlocutor for a secret and the requirement to avoid secrets) and the promise to keep a secret.

The cultural-social nucleus of an open community: A multi-level community knowledge graph and NASA application

The challenges faced by science, engineering, and society are increasingly complex, requiring broad, cross-disciplinary teams to contribute to collective knowledge, cooperation, and sensemaking efforts. However, existing approaches to collaboration and knowledge sharing are largely manual, inadequate to meet the needs of teams that are not closely connected through personal ties or which lack the time to respond to dynamic requests for contextual information sharing. Nonetheless, in the current remote-first, complexity-driven, time-constrained workplace, such teams are both more common and more necessary. For example, the NASA Center for HelioAnalytics (CfHA) is a growing and cross-disciplinary community that is dedicated to aiding the application of emerging data science techniques and technologies, including AI/ML, to increase the speed, rigor, and depth of space physics scientific discovery. The members of that community possess innumerable skills and competencies and are involved in hundreds of projects, including proposals, committees, papers, presentations, conferences, groups, and missions. Traditional structures for information and knowledge representation do not permit the community to search and discover activities that are ongoing across the Center, nor to understand where skills and knowledge exist. The approaches that do exist are burdensome and result in inefficient use of resources, reinvention of solutions, and missed important connections. The challenge faced by the CfHA is a common one across modern groups and one that must be solved if we are to respond to the grand challenges that face our society, such as complex scientific phenomena, global pandemics and climate change. We present a solution to the problem: a community knowledge graph (KG) that aids an organization to better understand the resources (people, capabilities, affiliations, assets, content, data, models) available across its membership base, and thus supports a more cohesive community and more capable teams, enables robust and responsible application of new technologies, and provides the foundation for all members of the community to co-evolve the shared information space. We call this the Community Action and Understanding via Semantic Enrichment (CAUSE) ontology. We demonstrate the efficacy of KGs that can be instantiated from the ontology together with data from a given community (shown here for the CfHA). Finally, we discuss the implications, including the importance of the community KG for open science.

Knowledge representation of mathematical optimization problems and constructs for modeling

We introduce two new knowledge bases that we have developed to categorize mixed-integer linear programming (MILP) problems and standardize the element definitions of MILP models. MILP is a commonly used mathematical programming technique for modeling and solving real-life scheduling, routing, planning, resource allocation, and timetabling optimization problems providing optimized business solutions for industry sectors such as manufacturing, agriculture, defense, healthcare, medicine, energy, finance, and transportation among others. Despite the numerous real-life combinatorial optimization problems (COPs) found and solved, and many yet to be discovered and formulated, the number of types of constraints (the building blocks of a MILP) is relatively small. In the search for a uniform categorization of MILP problems and a machine-readable knowledge representation structure for MILP models, we have developed an optimization modeling tree (OMT) and a MILP model ontology. The two knowledge structures can serve as a standardized, uniform representation in understanding MILP problems and developing MILP models for combinatorial business optimization problems. While there are several algebraic modeling languages (AMLs) for developing and solving MILP models, the semantic correctness of such models cannot be guaranteed using the syntactic grammar of such AMLs. The MILP model ontology will act as the main resource for semantic validation of MILP models through ontology instantiations and axiom assertions.

Online Incremental Learning Based on Crowdsourcing For Indonesian Ontology Relation Extraction

Ontology is one form of structured representation of knowledge. Ontology is widely used and developed in information retrieval because of its ability to represent knowledge in a form that machines and humans can understand. With the increasing scale and complexity of ontology, there are more significant challenges in identifying extra-logical errors. Ontological development methods mostly use machine learning, which is at risk of missed extra-logical errors. To handle it, crowdsourcing is used, i.e. dividing a large job into several small jobs and hiring the masses to complete it. Data processing is usually done offline to take advantage of crowdsourcing, and batches are converted into online and incremental. Online incremental learning directly arranges an iterative model after a change is made by ensuring that the knowledge that has been obtained before is maintained. This study built an interactive medium to present the initial relationship between concept pairs. Crowdsourcing participants were asked to validate the relationship repeatedly until a specified accuracy value was reached. This study found that the crowdsourcing process was able to improve the model used in the relationship extraction process, from F1-Score 87.2% to 89.8%. Improvements using crowdsourcing achieve the same result as improvements by experts. Thus, crowdsourcing can correct extra-logical errors appropriately as an expert. In addition, it was also found that offline incremental learning using Random Forest resulted in higher model accuracy than incremental online learning using Mondrian Forest. The accuracy of the Random Forest model has a final accuracy of 90.6%, while the accuracy of the Mondrian Forest model is 89.7%. From these results, it was concluded that incremental online learning cannot provide better results than offline incremental learning to improve the meronymy relationship extraction process.

Course map learning with graph convolutional network based on AuCM

Concept map provides a concise structured representation of knowledge in the educational scenario. It consists of various concepts connected by prerequisite dependencies. With the abundance of educational resources available through MOOCs, encyclopedias, and electronic textbooks, extracting prerequisite dependencies and building concept maps becomes feasible. However, publicly accessible taxonomies or learning object information that can help identify prerequisites are rare. To address this, we have constructed a comprehensive dataset called the Australian Course Map data (AuCM), specifically tailored for training concept maps in the IT/CS field. The dataset comprises course descriptions from 14 different Australian universities. To identify prerequisite relationships between course concepts, we have employed an embedding-based approach that combines the Graph Convolutional Network (GCN) with pairwise features of concepts. We have evaluated the performance of our model with non-neural classifiers and neural networks for extracting these prerequisite relations.

Topological properties and organizing principles of semantic networks

Interpreting natural language is an increasingly important task in computer algorithms due to the growing availability of unstructured textual data. Natural Language Processing (NLP) applications rely on semantic networks for structured knowledge representation. The fundamental properties of semantic networks must be taken into account when designing NLP algorithms, yet they remain to be structurally investigated. We study the properties of semantic networks from ConceptNet, defined by 7 semantic relations from 11 different languages. We find that semantic networks have universal basic properties: they are sparse, highly clustered, and many exhibit power-law degree distributions. Our findings show that the majority of the considered networks are scale-free. Some networks exhibit language-specific properties determined by grammatical rules, for example networks from highly inflected languages, such as e.g. Latin, German, French and Spanish, show peaks in the degree distribution that deviate from a power law. We find that depending on the semantic relation type and the language, the link formation in semantic networks is guided by different principles. In some networks the connections are similarity-based, while in others the connections are more complementarity-based. Finally, we demonstrate how knowledge of similarity and complementarity in semantic networks can improve NLP algorithms in missing link inference.

A Survey on Event-Based News Narrative Extraction

Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened more than 900 articles, which yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.

A user-guided reduction concept lattice and its algebraic structure

Concept lattice has been widely studied and it is still attracting researchers in many fields by virtue of its well structured knowledge representation. However, it is more obvious that the number of concept lattice nodes generated from the initial formal context is enormous and results in more storage and lower efficiency of building, especially with the increasing of data to deal with. Meanwhile, users are not interested in the whole intents composed of all the attribute sets and it is a time consuming and troublesome job for them to acquire interesting knowledge. In order to deal with these deficiencies, we propose a constrained concept lattice structure referred to as a reduction concept lattice (RCL) guided by user preferences. This type of lattice structure not only can effectively reduce storage space and construction time, but also help to extract the knowledge that users are interested in. In this study, the definition of RCL is first given based on users’ three types of interesting background knowledge described by predicate logic, together with some RCL’s examples to illustrate this type of structure. Next, an algebraic structure of RCL is defined and its completeness is proved, and accordingly a new way is provided for reduction of concept lattice based on user preferences.

Representation of structural knowledge in stylistic techniques

Representation of structural knowledge in stylistic techniques

Integrating Qualitative and Quantitative Methods to Evaluate Performance of the Brazilian Network for Continuous Global Navigation Satellite System (GNSS) Monitoring (RBMC)

We discuss a qualitative analysis of the efficiency of the Brazilian Network for Continuous Global Navigation Satellite System Monitoring (RBMC) using the Complex Holographic Assessment of Paradoxical Problems method (CHAP²), whose premise is the organization of intersubjectivity facilitated by the use of visual representation of structured knowledge (conceptual mapping) to increase the degree of consciousness to manage the paradoxes resulting from the complexity of living systems. We also describe a quantitative evaluation using Data Envelopment Analysis (DEA) allowing a ranking in terms of efficiency for further assessment by a professional of the area. The outcome of this study would be useful for IBGE (Brazilian Institute of Geography and Statistics) managers to be aware, for instance, of the suppliers and manufacturers of equipment available (receiver and geodetic antenna, Internet connection and constant supply of electricity) in efficient and ineffective stations, to guide new acquisitions, among other applications.

Leveraging a pharmacogenomics knowledgebase to formulate a drug response phenotype terminology for genomic medicine.

Despite the increasing evidence of utility of genomic medicine in clinical practice, systematically integrating genomic medicine information and knowledge into clinical systems with a high-level of consistency, scalability and computability remains challenging. A comprehensive terminology is required for relevant concepts and the associated knowledge model for representing relationships. In this study, we leveraged PharmGKB, a comprehensive pharmacogenomics (PGx) knowledgebase, to formulate a terminology for drug response phenotypes that can represent relationships between genetic variants and treatments. We evaluated coverage of the terminology through manual review of a randomly selected subset of 200 sentences extracted from genetic reports that contained concepts for 'Genes and Gene Products' and 'Treatments'. Results showed that our proposed drug response phenotype terminology could cover 96% of the drug response phenotypes in genetic reports. Among 18 653 sentences that contained both 'Genes and Gene Products' and 'Treatments', 3011 sentences were able to be mapped to a drug response phenotype in our proposed terminology, among which the most discussed drug response phenotypes were response (994), sensitivity (829) and survival (332). In addition, we were able to re-analyze genetic report context incorporating the proposed terminology and enrich our previously proposed PGx knowledge model to reveal relationships between genetic variants and treatments. In conclusion, we proposed a drug response phenotype terminology that enhanced structured knowledge representation of genomic medicine. Supplementary data are available at Bioinformatics online.

Neural network based successor representations to form cognitive maps of space and language

How does the mind organize thoughts? The hippocampal-entorhinal complex is thought to support domain-general representation and processing of structural knowledge of arbitrary state, feature and concept spaces. In particular, it enables the formation of cognitive maps, and navigation on these maps, thereby broadly contributing to cognition. It has been proposed that the concept of multi-scale successor representations provides an explanation of the underlying computations performed by place and grid cells. Here, we present a neural network based approach to learn such representations, and its application to different scenarios: a spatial exploration task based on supervised learning, a spatial navigation task based on reinforcement learning, and a non-spatial task where linguistic constructions have to be inferred by observing sample sentences. In all scenarios, the neural network correctly learns and approximates the underlying structure by building successor representations. Furthermore, the resulting neural firing patterns are strikingly similar to experimentally observed place and grid cell firing patterns. We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence.

Construction and Visualization of the Ontology of the Educational Course for the Digital Information Space of the University

Despite the significant volume of educational courses published on the university's information portals, there is no full or partial mechanism for obtaining the necessary information in one place and an accessible form. Each course is a separate resource, without the ability to build logical connections (navigational transitions) between adjacent or sequential courses and courses of the same thematic cluster or subject area. The lack of systematization of e-courses does not allow realizing a full-fledged contextual search and navigation between related resources.
 Within the framework of this study, the problem of constructing and presenting an ontology of an electronic course is considered. Applied solutions to this problem will significantly increase the constructiveness of existing services for working with training courses and provide a new set of opportunities based on the systematization of knowledge. As a structure of knowledge representation, it is proposed to use a structured set of concepts (terms) that describe the theoretical and practical aspects of the electronic course. Such a domain ontology is represented as a semantic network, which can be visualized by a specialized software tool or using a graphical library, for example, d3 for the JavaScript language.