Knowledge Inference Research Articles

RDSSMM: A FUZZY EXPERT SYSTEM FOR ENHANCED MALARIA DIAGNOSIS AND MANAGEMENT IN ENDEMIC REGIONS

Malaria continues to be a pressing public health issue in tropical regions, with its deleterious effects on human health well documented. While a minority of cases may be life threatening, delayed diagnosis can significantly worsen the severity of malaria. This paper presents the Recommender and Decision Support System for Malaria Management (RDSSMM), designed to assist researchers, physicians and healthcare professionals in malaria endemic areas. RDSSMM is made up of four main components: a knowledge base, fuzzification module, inference engine, and defuzzification module. Mayo Hospital Lahore, Pakistan was used to develop the fuzzy expert system based on experts’ opinions in the field of medicine. For ease of access, a mobile application has been included which allows patients to get primary diagnosis by keying in their physical signs that are then transformed into fuzzy membership functions. The data was collected from the outpatient department of Mayo Hospital Lahore as one way of evaluating RDSSMM’s effectiveness. Comparing its predictions to expert medical diagnoses and reports pointed out this system’s performance. Malaria risk was correctly predicted 80% of the time by RDSSMM. These findings show that RDSSMM can greatly improve malaria management in rural areas through provision of quick and accurate results, which would facilitate timely decisions regarding malaria testing and monitoring.

Modeling and simulation of Fuzzy-rule based WBAN using multi-level fuzzy colored petri-nets and reinforcement learning

By developing IoTs (Internet of Things), a new concept is arising in WBANs (Wireless Body Area Networks), called IoBs (Internet of Bodies). WBANs are the safety-critical devices that use rule-based knowledge with fuzzy variables for monitoring patients. There are two issues with the knowledgebase: (1) appropriate knowledge representation and inference and (2) handling the situations for which there is no rule in the knowledgebase. In this paper, for the first issue we present MFCPN (Multi-level Fuzzy Colored Petri-nets) and for the second one we present a RL (Reinforcement Learning) mechanism for decision making based on the past device’s feedback to the environment. Through a few scenarios and two datasets, the proposed approach has been applied to pacemakers to show the approach's effectiveness. The comparison between decision making by our approach and two real datasets indicates the proposed approach has 79.3 % accuracy.

Knowledge graph of agricultural engineering technology based on large language model

Agriculture is an industry that has evolved alongside human evolution and has faithfully fulfilled its core mission of food supply. With the reduction of rural labor, the progress of artificial intelligence and the development of Internet of Things technology, it is hoped that the efficiency and productivity of the agricultural industry can be improved. Recently, with the development of information and intelligent technology, agricultural production and management have been significantly enhanced. However, there is still a considerable challenge in effectively integrating the vast amount of fragmented information for downstream applications. An agricultural knowledge graph (AGKG) will serve as the foundation for achieving these goals. Knowledge graphs can be general or domain-specific, and are the basis for many applications, such as search engines, online question-and-answer services, and knowledge inference. Therefore, there are many knowledge graphs, including Wikidata and DBpedia, for accessing structured knowledge. Although some general knowledge graphs contain some entities and relationships related to agriculture, there are no domain-specific knowledge graphs specifically for agricultural applications. Therefore, this paper proposes an agricultural knowledge graph (AGKG) for automatically integrating large amounts of agricultural data from the Internet. By applying natural language processing and deep learning technologies, AGKG can automatically identify agricultural entities from unstructured text and connect them to form a knowledge graph. In addition, we have described the typical scenarios of our AGKG and validated it through real-world applications such as agricultural entity retrieval and agricultural question-answering.

Processing the Narrative: Innovative Graph Models and Queries for Textual Content Knowledge Extraction

The internet contains vast amounts of text-based information across various domains, such as commercial documents, medical records, scientific research, engineering tests, and events affecting urban and natural environments. Extracting knowledge from these texts requires a deep understanding of natural language nuances and accurately representing content while preserving essential information. This process enables effective knowledge extraction, inference, and discovery. This paper proposes a critical study of state-of-the-art contributions exploring the complexities and emerging trends in representing, querying, and analysing content extracted from textual data. This study’s hypothesis states that graph-based representations can be particularly effective when annotated with sophisticated querying and analytics techniques. This hypothesis is discussed through the lenses of contributions in linguistics, natural language processing, graph theory, databases, and artificial intelligence.

Inferential knowledge and epistemic dimensions

Abstract Knowledge representation is one way to exploit expertise in a given domain by logical means. But, what kind of knowledge does one acquire from an inference (or inference on a query result over a knowledge base)? Such a question may appear awkward since the answer seems so obvious: from an inference, one simply acquires knowledge. This is undoubtedly the case when only one type of knowledge (for instance, expert knowledge) is involved in an inference. What if several types of knowledge are involved? What type of knowledge can one deduce from a plurality of knowledge types? I claim that reasoning with different knowledge concepts requires a fine-grained representation of knowledge in which every knowledge type finds a singular expression in order to avoid some epistemic equivocity associated with a coarse-grained representation of knowledge. In the first part of the paper, I revisit the Muddy Children Puzzle, which usually serves to illustrate common knowledge in dynamic epistemic logic. I try to show that this problem also shows some sort of epistemic equivocity between concepts of knowledge and, consequently, that the problem calls for some epistemological refinements concerning the representation of the types of knowledge at play in an inference. In the second part, I address this issue from a semantic point of view, and I develop a fragment of epistemic logic capable of providing a solution to the problem of epistemic equivocity.

Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs

To address the limitations in capturing complex semantic features between entities and the incomplete acquisition of entity and relationship information by existing patent knowledge graph reasoning algorithms, we propose a reasoning method that integrates semantic and structural features for patent knowledge graphs, denoted as SS-DSA. Initially, to facilitate the model representation of patent information, a directed graph representation model based on the patent knowledge graph is designed. Subsequently, structural information within the knowledge graph is mined using inductive learning, which is combined with the learning of graph structural features. Finally, an attention mechanism is employed to integrate the scoring results, enhancing the accuracy of reasoning outcomes such as patent classification, latent inter-entity relationships, and new knowledge inference. Experimental results demonstrate that the improved algorithm achieves an up to approximately 30% increase in the MRR index compared to the ComplEx model in the public Dataset 1; in Dataset 2, the MRR and Hits@n indexes, respectively, saw maximal improvements of nearly 30% and 112% when compared with MLMLM and ComplEx models; in Dataset 3, the MRR and Hits@n indexes realized maximal enhancements of nearly 200% and 40% in comparison with the MLMLM model. This effectively proves the efficacy of the refined model in the reasoning process. Compared to recently widely applied reasoning algorithms, it offers a superior capability in addressing complex structures within the datasets and accomplishing the completion of existing patent knowledge graphs.

Reinforcement learning with predefined and inferred reward machines in stochastic games

This paper focuses on Multi-Agent Reinforcement Learning (MARL) in non-cooperative stochastic games, particularly addressing the challenge of task completion characterized by non-Markovian reward functions. We employ Reward Machines (RMs) to incorporate high-level task knowledge. Firstly, we introduce Q-learning with Reward Machines for Stochastic Games (QRM-SG), where RMs are predefined and available to agents. QRM-SG learns each agent’s best-response policy at Nash equilibrium by defining the Q-function in augmented state space that integrates the stochastic game and RM states. The Lemke-Howson method is utilized to compute the best-response policies for the stage game defined by the current Q-functions at each time step. Subsequently, we explore a more challenging scenario where RMs are unavailable and propose Multi-Agent Reinforcement learning with Concurrent High-level knowledge inference (MARCH). MARCH uses automata learning to learn RMs iteratively and combines this process with QRM-SG for learning the best-response policies. The RL episodes where the obtained rewards are inconsistent with the rewards from the current RMs trigger the inference of new RMs. We prove QRM-SG and MARCH converge to the best-response policies under certain conditions. Two scenarios are conducted to demonstrate the superior performance of QRM-SG and MARCH compared to baseline methods.

Prefrontal coding of learned and inferred knowledge during REM and NREM sleep

Idling brain activity has been proposed to facilitate inference, insight, and innovative problem-solving. However, it remains unclear how and when the idling brain can create novel ideas. Here, we show that cortical offline activity is both necessary and sufficient for building unlearned inferential knowledge from previously acquired information. In a transitive inference paradigm, male C57BL/6J mice gained the inference 1 day after, but not shortly after, complete training. Inhibiting the neuronal computations in the anterior cingulate cortex (ACC) during post-learning either non-rapid eye movement (NREM) or rapid eye movement (REM) sleep, but not wakefulness, disrupted the inference without affecting the learned knowledge. In vivo Ca2+ imaging suggests that NREM sleep organizes the scattered learned knowledge in a complete hierarchy, while REM sleep computes the inferential information from the organized hierarchy. Furthermore, after insufficient learning, artificial activation of medial entorhinal cortex-ACC dialog during only REM sleep created inferential knowledge. Collectively, our study provides a mechanistic insight on NREM and REM coordination in weaving inferential knowledge, thus highlighting the power of idling brain in cognitive flexibility.

The environment to the rescue: can physics help predict predator–prey interactions?

ABSTRACTUnderstanding the factors that determine the occurrence and strength of ecological interactions under specific abiotic and biotic conditions is fundamental since many aspects of ecological community stability and ecosystem functioning depend on patterns of interactions among species. Current approaches to mapping food webs are mostly based on traits, expert knowledge, experiments, and/or statistical inference. However, they do not offer clear mechanisms explaining how trophic interactions are affected by the interplay between organism characteristics and aspects of the physical environment, such as temperature, light intensity or viscosity. Hence, they cannot yet predict accurately how local food webs will respond to anthropogenic pressures, notably to climate change and species invasions. Herein, we propose a framework that synthesises recent developments in food‐web theory, integrating body size and metabolism with the physical properties of ecosystems. We advocate for combination of the movement paradigm with a modular definition of the predation sequence, because movement is central to predator–prey interactions, and a generic, modular model is needed to describe all the possible variation in predator–prey interactions. Pending sufficient empirical and theoretical knowledge, our framework will help predict the food‐web impacts of well‐studied physical factors, such as temperature and oxygen availability, as well as less commonly considered variables such as wind, turbidity or electrical conductivity. An improved predictive capability will facilitate a better understanding of ecosystem responses to a changing world.

Reac4Cat-Ontology: Harnessing the Power of Ontological Description Logic in Catalysis Research as a Practical Approach to Knowledge Inferences

Maximizing the use of digitally captured data is a key requirement for many of the late adopters of digital infrastructure. One of the newcomers is the chemical industry in the area of digitized laboratories. Here, tools and services that satisfy individual needs still need to be developed and distributed within the community. This work explores the potential of using graph databases — specifically those modeled via ontological knowledge graphs — to describe complex data linkages and draw logical conclusions. While knowledge graphs are not widely utilized in catalysis research, this study introduces a methodology to highlight their usability for semantic description and integration into diverse value chains with contact to the domain of (bio)chemistry and catalysis.A demonstration is performed how ontologies and their knowledge graphs can be applied to perform essential functions of semantic annotation to chemical reactions, which are difficult to model relational. Traditional data description methods can be neglected using description logic, showing how logical inferences at the machine level can enrich data. This work also illustrates the seamless integration of this enhanced data into process simulations, connecting semantic description with practical applications. The immediate benefits for catalysis research are emphasized and the development of new tools and services envisioned. By clarifying how these graphs can be integrated into existing workflows, researchers are empowered to make the most of digitally acquired data in catalytic processes. This practical methodology lays the foundation for improved decision-making and innovation, fostering advancements in the field of catalysis research.

Enhancing surgical expertise: 3D anatomy’s role in residency programs

Current virtual reality (VR) technology allows for the creation of instructional video formats that incorporate three-dimensional (3D) stereoscopic footage. Combined with 3D anatomic dissection, any surgical procedure or pathology can be represented virtually to supplement anatomical learning and surgical preoperative planning. The aim of this study was to evaluate the impact of virtual reality anatomical teaching. A prospective case-control study was performed. After a prerequisite anatomical knowledge assessment, participants were randomized in two groups: stereoscopic anatomical teaching program versus classic teaching with anatomical and surgical books of thoracic brachial outlet syndrome, and its related anatomy. Then, students completed a written anatomical test to assess their basic knowledge in vascular anatomy. Pre- and post-test performances were analyzed with independent t-tests for total score assessing basic anatomical knowledge, anatomical relationships and clinical inference. Before performing the teaching, the 20 students included were homogenous in term of total exam mark in abdominal aorta (mean 78,78% vs 76,34%) and carotid artery evaluations (mean 78,57% vs 74,76%). After the course, there statistical differences (p<0,05) between the stereoscopic-3D.video group (n= 10, 90%) and classical-teaching group concerning the total exam mark, descriptive anatomy, anatomical relationships and clinical inference skills. All the students thought this method seemed indispensable to their anatomical training course (100%). The teaching video with 3D stereoscopy seems to be a useful and complementary teaching tool that is approved by the residents themselves. In a future study, it will be necessary to evaluate the contribution of this teaching in the long term.

Subthalamic Nucleus Stimulation Modulates Cognitive Theory of Mind in Parkinson's Disease.

Theory of mind (ToM), the ability to infer others' mental state, is essential for social interaction among human beings. It has been widely reported that both cognitive (inference of knowledge) and affective (inference of emotion) components of ToM are disrupted in Parkinson's disease (PD). Previous studies usually focused on the involvement of the prefrontal cortex. This study investigated the causal role of the subthalamic nucleus (STN), a key hub of the fronto-basal ganglia loops, in ToM. Thirty-four patients with idiopathic PD (15 women, aged 62.2 ± 8.3 years) completed a Yoni task with deep brain stimulation (DBS) ON and OFF. The Yoni task was designed to separate the cognitive and affective components of ToM. Volumes of tissue activated (VTA) were computed for three subregions of the STN. DBS showed insignificant effects on ToM inference costs at the group level, which may be due to the large interindividual variability. The associative VTA correlated with the cognitive inference cost change but not the affective inference cost change. Patients with greater associative STN stimulation infer more slowly on cognitive ToM. Stimulating associative STN can adversely affect cognitive ToM in PD patients, especially in patients with a wide range of stimulation (≥0.157) or cognitive decline (Montreal Cognitive Assessment < 26). The associative STN plays a causal role in cognitive ToM in patients with PD. However, stimulating the associative STN likely impairs cognitive ToM and potentially leads to social interaction deficits in PD. © 2024 International Parkinson and Movement Disorder Society.

Knowledge Inference Combining Convolutional Feature Extraction and Path Semantics Integration

Many knowledge representation models extract local patterns or semantic features using fact embeddings but often overlook path semantics. There is room for improvement in pathbased approaches that rely solely on single paths. A customized convolutional neural network (CNN) architecture is proposed to encode multiple paths generated by random walks into vector sequences. For each path, the feature sequence is then merged into a single vector using bidirectional long short-term memory (LSTM) by concatenating both forward and backward hidden states. Semantic relevance between different paths and candidate relations is computed using the attention mechanism. The state vectors of the relations are calculated using weighted paths. These paths help determine the probabilities of the candidate relations, which are then used to assess the validity of the triples. Link prediction experiments on two benchmark datasets, NELL995 and FB15k-237, demonstrate the advantages of our solution. Our model shows a 7.19% improvement at Hits@3 on FB15k-237 compared to Att-Model + Type, another advanced model. The model is further applied to a large complex dataset, FC17, as well as a sparse dataset, NELL-One, for few-shot reasoning.

Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling

The Knowledge-Based Digital Twin System is a digital twin system developed on the foundation of a knowledge graph, aimed at serving the complex manufacturing process. This system embraces a knowledge-driven modeling approach, aspiring to construct a digital twin model for the manufacturing process, thereby enabling precise description, management, prediction, and optimization of the process. The core of this system lies in the comprehensive knowledge graph that encapsulates all pertinent information about the manufacturing process, facilitating dynamic modeling and iteration through knowledge matching and inference within the knowledge, geometry, and decision model. This approach not only ensures consistency across models but also addresses the challenge of coupling multi-source heterogeneous information, creating a holistic and precise information model. As the manufacturing process deepens and knowledge accumulates, the model's understanding of the process progressively enhances, promoting self-evolution and continuous optimization. The developed knowledge-decision-geometry model acts as the ontological layer within the digital twin framework, laying a foundational conceptual framework for the digital twin of the manufacturing process. Validated on an aero-engine blade production line in a factory, the results demonstrate that the knowledge model, as the core driver, enables continuous self-updating of the geometric model for an accurate depiction of the entire manufacturing process, while the decision model provides deep insights for decision-makers based on knowledge. The system not only effectively controls, predicts, and optimizes the manufacturing process but also continually evolves as the process advances. This research offers a new perspective on the realization of the digital twin for the manufacturing process, providing solid theoretical support with a knowledge-driven approach.

Degumming and bleaching process troubleshooting in a palm oil refining process using fuzzy expert system with thematic analysis

AbstractDegumming and bleaching are critical steps in the palm oil refining process, as they are the precursors to the qualities of refined, bleached, and deodorized palm oil. In practice, plant operators often face oil rejections in these processes and solve the problem by trial and error. Hence, a fuzzy expert system is developed to troubleshoot the degumming and bleaching process, for identifying failures and suggesting actions. However, developing the knowledge base and inference engine in the fuzzy expert system for troubleshooting the degumming and bleaching process is challenging because the data in the actual palm oil refining process are poorly documented and must be obtained from various sources, including field observation, document analysis, and interviews, and need to be analyzed using thematic analysis. The results from the thematic analysis were represented as input and output variables of the fuzzy expert system. The developed fuzzy expert system is tested and validated against different data sets and industrial data to identify faults and suggest necessary actions. To evaluate the robustness of the troubleshooting system, the membership functions of the fuzzy expert system are adjusted based on the distributed control system (DCS). The results show that the troubleshooting system can effectively diagnose potential faults and provide necessary actions and can serve as a useful guidance for failures in the degumming and bleaching process.

Houston Methodist cardiovascular learning health system (CVD-LHS) registry: Methods for development and implementation of an automated electronic medical record-based registry using an informatics framework approach

ObjectivesTo investigate the potential value and feasibility of creating a listing system-wide registry of patients with at-risk and established Atherosclerotic Cardiovascular Disease (ASCVD) within a large healthcare system using automated data extraction methods to systematically identify burden, determinants, and the spectrum of at-risk patients to inform population health management. Additionally, the Houston Methodist Cardiovascular Disease Learning Health System (HM CVD-LHS) registry intends to create high-quality data-driven analytical insights to assess, track, and promote cardiovascular research and care. MethodsWe conducted a retrospective multi-center, cohort analysis of adult patients who were seen in the outpatient settings of a large healthcare system between June 2016 - December 2022 to create an EMR-based registry. A common framework was developed to automatically extract clinical data from the EMR and then integrate it with the social determinants of health information retrieved from external sources. Microsoft's SQL Server Management Studio was used for creating multiple Extract-Transform-Load scripts and stored procedures for collecting, cleaning, storing, monitoring, reviewing, auto-updating, validating, and reporting the data based on the registry goals. ResultsA real-time, programmatically deidentified, auto-updated EMR-based HM CVD-LHS registry was developed with ∼450 variables stored in multiple tables each containing information related to patient's demographics, encounters, diagnoses, vitals, labs, medication use, and comorbidities. Out of 1,171,768 adult individuals in the registry, 113,022 (9.6%) ASCVD patients were identified between June 2016 and December 2022 (mean age was 69.2 ± 12.2 years, with 55% Men and 15% Black individuals). Further, multi-level groupings of patients with laboratory test results and medication use have been analyzed for evaluating the outcomes of interest. ConclusionsHM CVD-LHS registry database was developed successfully providing the listing registry of patients with established ASCVD and those at risk. This approach empowers knowledge inference and provides support for efforts to move away from manual patient chart abstraction by suggesting that a common registry framework with a concurrent design of data collection tools and reporting rapidly extracting useful structured clinical data from EMRs for creating patient or specialty population registries.

Knowledge-Related Policy Analysis in an Inference-Enabled Actor Model

People provide their information to distributed systems to receive the desired services. This information may be disclosed to the agents of the system as part of messages transmitted among them. As the agents of the system are smart, they can infer new information from their obtained information, that they may not be authorized to know. So preserving privacy in such systems is an important and yet challenging issue. We study the problem of analyzing the disclosure of private information in distributed asynchronous systems. Our approach to prevent private information disclosure is to require the system to follow knowledge-related policies defined for the system at design time. To achieve this, we construct a model of the system and assume the policies as the system properties and check whether these properties are satisfied in the system or not. In order to construct a model of the system, we extend the actor model, which is a well known reference model for distributed asynchronous systems, by enriching actors by the knowledge base and inference capability. As our knowledge-related policies should not be violated in any state of the system, we propose an efficient invariant model checking algorithm to verify the satisfaction of the policies in our actor model.

An ontology-driven model for hospital equipment maintenance management: a case study

Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

RiQ-KGC: Relation Instantiation Enhanced Quaternionic Attention for Complex-Relation Knowledge Graph Completion

A knowledge graph is a structured semantic network designed to describe physical entities and relations in the world. A comprehensive and accurate knowledge graph is essential for tasks such as knowledge inference and recommendation systems, making link prediction a popular problem for knowledge graph completion. However, existing approaches struggle to model complex relations among entities, which severely hampers their ability to complete knowledge graphs effectively. To address this challenge, we propose a novel hierarchical multi-head attention network embedding framework, called RiQ-KGC, which integrates different-grained contextual information of knowledge graph triples and models quaternion rotation relations between entities. Furthermore, we propose a relation instantiation method for alleviating the difficulty of expressing complex relations between entities. To enhance the expressiveness of relation representation, the relation is integrated by Transformer to obtain multi-hop neighbor information, so that one relation can be embedded into different embeddings according to different entities. Experimental results on four datasets demonstrate that RiQ-KGC exhibits strong competitiveness compared to state-of-the-art models in link prediction, while the ablation experiments reveal that the proposed relation instantiation method achieves great performance.

A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network

Knowledge Graphs (KGs) are widely used in many fields of application, and especially play an essential role in recommendation systems. KGs often need to be complete, missing relationships between users and items, data sparsity, weak associations, and difficulties in knowledge inference, resulting in low credibility of recommendation results. Therefore, we propose a novel Knowledge Graph (KG) recommendation algorithms. Due to the availability of interaction data across numerous events, KGs also exhibit dynamics over time. By taking into account the temporal variable, it is possible to organise well-structured external information to connect users and items, thereby expanding user preferences to a certain extent. The proposed algorithm employs GCNs to encode the heterogeneous graph, which includes user-item interactions and the KG. It addresses the challenge of high-dimensional data by using the inner product of users and items. The algorithm uncovers potential alignment relationships and learns the embedding of user-item and relationships by applying convolutional processing to the graph data's features and performing data fusion, the new algorithm uncovers potential alignment relationships, and learns embedding of user-item and relationships. The experimental results on the Mean Reciprocal Rank (MRR) and Hits@k demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in terms of the credibility and accuracy of recommendation results.