Conceptual Graphs Research Articles

Neurodegenerative diseases associated antibody repertoire signatures in mimotope arrays based on cyclic versus linear peptides

The role of peptide probes’ conformational flexibility in extracting immunosignatures has not been sufficiently studied. Immunosignatures profile the antibody diversity and prove promising for early cancer detection and multi-disease diagnostics. A novel tool for modeling antibody repertoires, the concept of antibody reactivity graphs, proved instrumental in this respect. Serum samples from patients with Alzheimer’s disease (AD), frontotemporal dementia (FTD), dementia of unknown etiology (DUE), and healthy controls were probed using a set of 130 7-mer peptides relevant to neurodegenerative diseases. Results show that linear peptides probed with IgM yielded higher graph density compared to IgG, indicating different levels of polyspecificities. Additionally, the impact of peptide topology and antibody isotype on feature selection was studied using recursive feature elimination. Findings reveal that IgM assays on linear peptides offer superior diagnostic differentiation of neurodegenerative diseases and define the degree of agreement between IgG and IgM immunosignatures with linear or cyclic peptides.

A Novel Concept of Level Graph in Interval-Valued Fuzzy Graphs with Application

Many problems of practical interest can be modeled and solved by using interval-valued fuzzy graph (IVFG) algorithms. An IVFG is a very useful and effective tool for studying various calculations, fields of intelligence, and computer science, such as networking, imaging, and other fields, such as biological sciences. In different applications, they present an appropriate construction means. There were limitations in the definition of fuzzy graphs (FGs), which prompted us to propose a new definition for IVFGs. Some interesting properties related to the new IVFGs are investigated, and enough conditions under which the level graph on IVFGs is equivalent are obtained. Therefore, in this study, we present the properties of a level graph (LG) of an IVFG, and four operations, the Cartesian product (CP), composition (CO), union, and join, are investigated on it. Today, in a treatment system, one of the issues that can be very valuable and important to the quality of service to patients is finding qualified and efficient people in each department, which is not an easy task. But the interval-valued fuzzy graph, as an important fuzzy graph, can help us by considering the ability of each person in the form of intervals of numbers and the effectiveness of each one on the other (according to the relationships between them) in order to find the most worthy people. So, an application of IVFG to find the most effective person in a hospital information system has been introduced.

A Construction Method of a Sequential Decision Chain for Unmanned-Ship Autonomous Collision Avoidance Based on Human-Like Thinking

As one of the key technologies restricting the development of intelligent ships, autonomous collision avoidance has attracted the attention of many scholars all over the world. Existing research on collision-avoidance behavior focuses more on collision risk assessment and local path-planning methods for studies on the human-like sequential logic of the whole collision-avoidance process, as well as the decision-making process of various stages. Further in-depth thinking is needed urgently. Based on this, a construction method of a human-like sequential decision chain for the autonomous collision avoidance of unmanned ships is proposed through the construction of a collision-avoidance rule base and strategy set, efficient data access based on the Knowledge Graph concept, global collision risk assessment considering sequential decision process, and the construction of a complete collision-avoidance logic process to simulate the decision-making process of humans in complex multi-ship encounters in open waters. For multi-ship encounter scenarios, considering the sequential decision-making process of collision avoidance, a method was proposed to divide the collision risk of the target ship into direct collision risk and potential collision risk. The validity and reliability of the constructed sequential decision chain are verified by simulation experimental results. The results show that the method is effective for collision avoidance (especially multi-ship collision avoidance) in open waters and can provide a theoretical basis and technical support with good interpretability for the decision-making process of an unmanned ship’s autonomous collision avoidance.

Encapsulation structure and dynamics in hypergraphs

Hypergraphs have emerged as a powerful modeling framework to represent systems with multiway interactions, that is systems where interactions may involve an arbitrary number of agents. Here we explore the properties of real-world hypergraphs, focusing on the encapsulation of their hyperedges, which is the extent that smaller hyperedges are subsets of larger hyperedges. Building on the concept of line graphs, our measures quantify the relations existing between hyperedges of different sizes and, as a byproduct, the compatibility of the data with a simplicial complex representation–whose encapsulation would be maximum. We then turn to the impact of the observed structural patterns on diffusive dynamics, focusing on a variant of threshold models, called encapsulation dynamics, and demonstrate that non-random patterns can accelerate the spreading in the system.

A Review of Causal Identifiability Techniques across Different Observational Datasets

We present an aggregation of the causal identifiability solutions techniques and their assumptions as advanced in extant literatures with datasets of odd origins, which do not necessarily conform to the independent and identically distributed (i.i.d) dataset, multinomial datasets and the Gaussian datasets settings; alongside their concomitant assumptions. The transformation process in data generation can sometimes be a desideratum of datasets of the following forms: linear and non-Gaussian, nonlinear & non-Gaussian, datasets with missing values, datasets tainted with selection biases, datasets with whose variables forms cycles, datasets with heterogeneous/nonstationary variables, datasets with confounding or latent variables, time-series datasets, deterministic datasets, etc. The study begins proper in section 2 after the introduction with the basic background into the concept of causality with observational data. The concept of graph as an embodiment of the background knowledge with structural causal model (SCM) is explicated in section 3; followed by the basic assumptions employed especially with common observational data settings in section 4. An exposition into the categorization of the algorithms used in causality is presented in section 4. Section 5 aggregates and expounds the causal identifiability techniques and their associated assumptions athwart varying datasets; which is the crux of the study and a recapitulation of same is presented in table 1. This study’s main contribution is to present an aggregate review of the causal techniques and their assumptions across different data settings especially in data settings of odd origins, as reviews such as this are grossly lacking in extant literatures.

Ant Metaheuristic Algorithm for the Frequency Assignment Problem

This study proposes the ant metaheuristic algorithm to solve the frequency assignment problem. In this approach, the descent method constructed as three ants which each of which appling a metaheuristics algorithm. Several novel and existing techniques are used to improve the performance of this algorithm. One of these techniques is applying the concept of a well-known graph colouring algorithm, namely recursive largest first. Furthermore, this study compares this algorithm using two visibility definitions. The first definition is based on the number of feasible frequencies and the second one is based on the degree. Additionally, we compare this algorithm using two trail definitions. The first one is between requests and frequencies. The second is between requests and requests. This study considers real and randomly generated benchmark datasets of the static problem and our algorithm achieved competitive results comparing with other ant colony optimization algorithms in the literature.

A Dynamic Ant Colony Optimization for Solving the Static Frequency Assignment Problem

This study proposes a dynamic ant colony optimization algorithm to solve the static frequency assignment problem. This approach solves the static problem by modeling it as a dynamic problem through dividing this static problem into smaller sub-problems, which are then solved in turn in a dynamic process. Several novel and existing techniques are used to improve the performance of this algorithm. One of these techniques is applying the concept of a well-known graph colouring algorithm, namely recursive largest first for each sub-problem. Furthermore, this study compares this algorithm using two visibility definitions. The first definition is based on the number of feasible frequencies and the second one is based on the degree. Additionally, we compare this algorithm using two trail definitions. The first one is between requests and frequencies. The second is between requests and requests. This study considers real and randomly generated benchmark datasets of the static problem and our algorithm achieved competitive results comparing with other ant colony optimization algorithms in the literature.

Minimal Determinization Algorithm for Fuzzy Automata

The determinization of fuzzy automata is a well-studied problem in theoretical computer science celebrated for its practical applications. Indeed, in the fields of fuzzy discrete event systems, fault diagnosis, clinical monitoring, decision-making systems, and model checking, when a suitable model of a fuzzy automaton is employed, it is desirable to find its language-equivalent deterministic version because of its computational efficiency. Although many methods have been developed to convert a fuzzy automaton to its language equivalent fuzzy deterministic finite automaton (FDfA), they can be applied only for fuzzy automata defined over specific underlying sets of truth values. For example, recently developed determinization methods employ the concept of maximal factorization, which can be defined only on non-locally finite lattices or the Boolean lattice. In addition, not all such determinization methods result in a minimal FDfA. On the other hand, even though such determinization methods have been developed for fuzzy automata over specific underlying structures, these methods cannot be generalized for fuzzy automata over locally finite lattices. This article focuses on filling this gap and develops a novel method for computing a minimal FDfA for a fuzzy automaton defined over a locally finite and divisible residuated lattice. Our method uses the new concept of a reduction graph that emerges from the strict order relation on the resulting fuzzy states, according to which we can construct all minimal FDfAs equivalent to a given fuzzy automaton.

Some novel concepts of interval-valued picture fuzzy graphs with applications toward the Transmission Control Protocol and social networks

The Transmission Control Protocol usually involves incomplete and imperfect network states for which sophisticated analysis is needed. Fuzzy logic could be more helpful for the analysis of network state more accurately. The interval-valued picture fuzzy set being the most generalized form of fuzzy set has more capacity to analyze the network state more intelligently. In this manuscript, we present the concepts of interval-valued picture fuzzy graphs (IVPFGs) as an extension of interval-valued fuzzy graphs and picture fuzzy graphs. Since interval-valued picture fuzzy sets are the most advanced form of fuzzy sets, IVPFGs would be a more efficient tool for handling data containing uncertainties. First, basic concepts such as degree, order, and size are discussed, followed by operations such as union, intersection, Cartesian product, composition, and the ring sum of IVPFGs. Then, we provide a few relationships between the ring sum and edge deletion of IVPFGs. Special types of IVPFGs including complete IVPFGs, regular IVPFGs, complement IVPFGs, and strong IVPFGs are introduced. Concepts such as the strength of arcs, path sequence, strength of the path, and connectedness are explored in IVPFGs. Different types of strengths of connectedness are discussed based on specific types of arcs. We also provide a few structural properties of IVPFGs through these arcs. Finally, we give a clue about the potential implementation of IVPFGs, an extension of the fuzzy logic-based Transmission Control Protocol and toward social networking.

Enforcing legal information extraction through context-aware techniques: The ASKE approach

To cope with the growing volume, complexity, and articulation of legal documents as well as to foster digital justice and digital law, increasing effort is being devoted to legal knowledge extraction and digital transformation processes. In this paper, we present the ASKE (Automated System for Knowledge Extraction) approach to legal knowledge extraction, based on a combination of context-aware embedding models and zero-shot learning techniques into a three-phase extraction cycle, which is executed a number of times (called generations) to progressively extract concepts representative of the different meanings of terminology used in legal documents chunks. A graph-based data structure called ASKE Conceptual Graph is initially populated through a data preparation step, and it is continuously enriched at each ASKE generation with results of document chunk classification, new extracted terminology, and newly derived concepts. A quantitative evaluation of ASKE knowledge extraction and document classification is provided by considering the EurLex dataset. Furthermore, we present the results of applying ASKE to a real case-study of Italian case law decisions with qualitative feedback from legal experts in the framework of an ongoing national research project.

Integrating heterogeneous structures and community semantics for unsupervised community detection in heterogeneous networks

Community detection aims to discover hidden communities or groups in complex networks and is essentially unsupervised clustering behavior. However, most of the existing unsupervised methods are designed for homogeneous networks; therefore, they cannot effectively handle heterogeneous structures and rich semantic information. Under such a situation, it is difficult to accurately detect communities in heterogeneous networks that better reflect the real world. Therefore, this work aims to design an unsupervised framework to fuse heterogeneous structure information and interpret the rich semantics of the network in the form of community semantics. Thus, a heterogeneous network community detection method, called HAESF, is introduced. It includes two modules: the Heterogeneous Auto-Encoder (HAE) and the Semantic Factorization (SF) modules. In more detail, the HAE module adopts a hierarchical attention scheme to represent and aggregate the heterogeneous structure of the network. And it proposes the concept of heterogeneous information combinatorial graphs for structural reconstruction to achieve unsupervised detection. Concerning the SF module, it focuses on learning the semantic information in the network from the community point of view. It uses nonnegative matrix factorization to decompose the network features for obtaining community semantics. Once both modules are implemented, the objective of restricting community segmentation based on these semantics is achieved. The constraint is based on community semantic homogeneity to correct inaccurate node delineation. Furthermore, to improve the algorithm efficiency, a unified framework is designed to optimize the HAE and SF modules jointly. Within this new framework, the SF loss is innovatively used as a judgmental loss for selective segmentation optimizations, helping to obtain more reliable community detection results. As for the results, extensive experiments are performed on three public datasets. The findings show that HAESF outperforms the other popular unsupervised methods, where the composite score of HAESF is 11.73% ahead of the next best, demonstrating the proposed method’s effectiveness.

Application of Knowledge Graph Technology with Integrated Feature Data in Spacecraft Anomaly Detection

Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, this paper proposes a method for applying knowledge graph technology with integrated feature data in spacecraft anomaly detection. First, the ontology concepts of the spacecraft equipment knowledge graph are designed according to expert knowledge, and then feature data are extracted from the historical operation data of the spacecraft in various states to build a rich spacecraft equipment knowledge graph. Next, spacecraft anomaly event knowledge graphs are constructed based on various types of anomaly features. During spacecraft operation, telemetry data are matched with the feature data in the knowledge graph, enabling anomaly device location and anomaly cause judgment. Experimental results show that this method, which utilizes spacecraft anomaly prior knowledge for anomaly detection and causes interpretation, has high practicality and efficiency. This research demonstrates the promising application prospects of knowledge graph technology in the field of spacecraft anomaly detection.

Concept and dependencies enhanced graph convolutional networks for short text classification

Short text classification task is a special kind of text classification task in that the text to be classified is generally short, typically generating a sparse text representation that lacks rich semantic information. Given this shortcoming, scholars worldwide have explored improved short text classification methods based on deep learning. However, existing methods cannot effectively use concept knowledge and long-distance word dependencies. Therefore, based on graph neural networks from the perspective of text composition, we propose concept and dependencies enhanced graph convolutional networks for short text classification. First, the co-occurrence relationship between words is obtained by sliding window, the inclusion relationship between documents and words is obtained by TF-IDF, long-distance word dependencies is obtained by Stanford CoreNLP, and the association relationship between concepts in the concept graph with entities in the text is obtained through Microsoft Concept Graph. Then, a text graph is constructed for an entire text corpus based on the four relationships. Finally, the text graph is input into graph convolutional neural networks, and the category of each document node is predicted after two layers of convolution. Experimental results demonstrate that our proposed method overall best on multiple classical English text classification datasets.

Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge Graph

Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as prerequisite relations (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation.

Graceful Labeling of Prime Index Graph of Group Zp × Zpn

The prime index graph π(G) of a finite group G is a special type of undirected simple graph whose vertex set is set of subgroups of G, in which two distinct vertices are adjacent if one has prime index in the other. Let p and q be distinct primes. In this paper, we establish that prime index graph of a finite cyclic p-group Zpn, a finite abelian group Zpn × Zq and a finite abelian p-group Zp × Zpn always have graceful labeling without any condition on n using the concept of path graph or p-layer ladder graph of size n + 1.

Determinants of using AI-powered discovery services

In 2023 the scholarly communities are witnessing a spring of Artificial Intelligence (AI) powered tools for scientific work. Scholars are tempted to integrate various time-saving AI applications in their workflow, from data analysis to disseminating research results. Among these novel “research assistants”, several enhanced discovery services apply machine learning to identify the most relevant results for the information seeker and visualize them to the user in innovative ways. The rapid emergence of these tools has raised concerns about the impact of AI technology on scientific research and led to requirements of transparency, accountability, and explainability of the new AI tools. From the systems viewpoint, responsibility for the impact of technology extends beyond developers to the broader society. The user communities, including librarians providing services for academia, are considered counterparts in the effects of AI technology systems. Individuals decide how they behave with the new information technology, for example, whether they trust the system and its outcome. Thus, an individual user is also part of the socio-technical evolution of building transparent, accountable, and explainable AI. In this study, we explore the challenges of adopting AI tools in scientific research on the level of an individual librarian working for academia. We aim to detect poorly addressed mindsets around explainability, fairness, and privacy, named “blind spots” in AI ethics (Hagendorff, 2022). The goal is to understand the “determinants” of librarians’ information behavior with novel AI tools. We focus on two AI-powered visual discovery services: openknowledgemaps.org and www.litmaps.com. These tools help users to navigate and analyze research articles as concept graphs. In this poster, our primary research question is: What are the determinants of librarians’ intentions when they adopt/use new AI-powered tools? We conducted an expert evaluation (Tessmer, 1993) on these two discovery services using the Theory of Planned Behavior (TPB) as a theoretical framework that explains human behavior through three individual beliefs: attitudes, norms, and control. This framework helped us detect new “blind spots” in the behavioral determinants that have remained unnoticed in the recent discourses about AI ethics in libraries. Our study indicated a lack in the area of normative beliefs, a “blind spot”: The social pressure to quickly adopt the newest technology and the lack of library-specific norms for using AI in academia may become a handicap for an individual librarian who contemplates whether or not to use an AI tool.

Concept Graphs: A Novel Approach for Textual Analysis of Medical Documents.

The task of automatically analyzing the textual content of documents faces a number of challenges in general but even more so when dealing with the medical domain. Here, we can't normally rely on specifically pre-trained NLP models or even, due to data privacy reasons, (massive) amounts of training material to generate said models. We, therefore, propose a method that utilizes general-purpose basic text analysis components and state-of-the-art transformer models to represent a corpus of documents as multiple graphs, wherein important conceptually related phrases from documents constitute the nodes and their semantic relation form the edges. This method could serve as a basis for several explorative procedures and is able to draw on a plethora of publicly available resources. We test it by comparing the effectiveness of these so-called Concept Graphs with another recently suggested approach for a common use case in information retrieval, document clustering.

Planarity in cubic intuitionistic graphs and their application to control air traffic on a runway

Fuzzy modeling plays a pivotal role in various fields, including science, engineering, and medicine. In comparison to conventional models, fuzzy models offer enhanced accuracy, adaptability, and resemblance to real-world systems and help researchers to always make the best choice in complex problems. A type of fuzzy graph that is widely used in medical and psychological sciences is the cubic intuitionistic fuzzy graph, which plays an important role in various fields such as computer science, psychology, medicine, and political sciences. It is also used to find effective people in an organization or social institution. In this research endeavor, we embark upon elucidating the innovative notion of a cubic intuitionistic planar graph, delving into its intricate properties and attributes. Additionally, we unveil the novel concept of a cubic intuitionistic dual graph, thus enriching the realm of graph theory with further profundity. Furthermore, our exploration encompasses the elucidation of other pertinent terminologies, such as cubic intuitionistic multi-graphs, along with the categorization of edges into the distinct classifications of strong and weak edges. Moreover, we discern the concept of the degree of planarity within the context of CIPG and unveil the notion of strong and weak faces. Additionally, we delve into the construction of cubic intuitionistic dual graphs, which can be realized in cases where the initial graph is planar or possesses a degree of planarity ≥0.67. Notably, we furnish the exposition with a comprehensive discussion on noteworthy findings and substantial results pertaining to these captivating topics, contributing valuable insights on the field of graph theory. Last, we shall endeavor to exemplify the practical relevance and importance of our research by presenting an illuminating real-world application, thus demonstrating the tangible impact and significance of our endeavors in this research article.

Facilitating Knowledge Transfer during Code Compliance Checking Using Conceptual Graphs

The rules in building codes are composed of language, logic structure, and the embedded knowledge that requires further articulation from rule experts. This greatly increases the complexity of rule interpretation. The conceptual graph (CG) is used as a graph-based representation method to capture such knowledge and transfer it to other actors in the compliance checking process. However, due to its subjectivity, it is difficult to ensure the quality of the CG to achieve minimal knowledge loss. This paper proposes a method using reusable CG patterns to reduce the knowledge loss caused by the opacity of concepts in rules, hence better facilitating knowledge transfer using CGs. The rules and their corresponding CGs are analyzed and compared. An assessment framework is used to assess CGs for correctness, completeness, complexity, and comprehensibility. A group of reusable CG patterns is then generated based on the analysis results of concepts in existing CGs. In a case study, rules selected from two types of building codes are used to validate the proposed methods. The results successfully demonstrate the effectiveness of the CG in facilitating knowledge transfer. Moreover, the comparison of quality between original CGs and improved CGs shows that using reusable CG patterns can effectively ease the creation of new CGs and improve its consistency.

A dynamic visualization based on conceptual graphs to capture the knowledge for disaster education on floods

A dynamic visualization based on conceptual graphs to capture the knowledge for disaster education on floods