Ensuring the logical coherence of subsumption hierarchies is essential for high-quality ontology engineering. While validation frameworks such as Onto-Clean provide formal mechanisms for checking taxonomic consistency in endurant entities, there remains a critical gap in validation tools for perduring entities such as processes, events, and states. This paper introduces a principled framework for validating perduring entity subsumption hierarchies by leveraging meta-properties, including cumulativity, homeomericity, atomicity, and agentivity. The framework defines a set of meta-property constraints that enforce consistency in the subsumption hierarchy of perdurants. Case studies demonstrate its utility across both foundational ontologies and domain-specific levels. Notably, when applied to the uppertier structure of DOLCE, the framework preserves its internal stratification while exposing classification gaps. Furthermore, applying the framework to the MAVEN event-type hierarchy reveals ontological inconsistencies introduced by human developers, demonstrating the framework’s effectiveness as a verification tool. This work establishes a foundation for the meta-property-driven construction and validation of perdurant taxonomies, addressing a long-standing gap in formal ontology validation methodologies.

The dependency on extensive expert knowledge for defining subgoals in hierarchical reinforcement learning (HRL) restricts the training efficiency and adaptability of HRL agents in complex, dynamic environments. Inspired by human-guided causal discovery skills, we proposed a novel method, Human Causal Perception and Inference-driven Hierarchical Reinforcement Learning (HCPI-HRL), designed to infer diverse, effective subgoal structures as intrinsic rewards and incorporate critical objects from dynamic environmental states using stable causal relationships. The HCPI-HRL method is supposed to guide an agent's exploration direction and promote the reuse of learned subgoal structures across different tasks. Our designed HCPI-HRL comprises two levels: the top level operates as a meta controller, assigning subgoals discovered based on human-driven causal critical object perception and causal structure inference; the bottom level employs the Proximal Policy Optimisation (PPO) algorithm to accomplish the assigned subgoals. Experiments conducted across discrete and continuous control environments demonstrated that HCPI-HRL outperforms benchmark methods such as hierarchical and adjacency PPO in terms of training efficiency, exploration capability, and transferability. Our research extends the potential of HRL methods incorporating human-guided causal modelling to infer the effective relationships across subgoals, enhancing the agent's capability to learn efficient policies in dynamic environments with sparse reward signals.

Few-Shot Class-Incremental Learning (FSCIL)presents an extension of the Class Incremental Learning (CIL)problem where a model is faced with the problem of data scarcity while addressing the Catastrophic Forgetting (CF)problem. This problem remains an open problem because all recent works are built upon the Convolutional Neural Networks (CNNs)performing sub-optimally compared to the transformer approaches. Our paper presents Robust Transformer Approach (ROBUSTA)built upon the Compact Convolutional Transformer (CCT). The issue of overfitting due to few samples is overcome with the notion of the stochastic classifier, where the classifier's weights are sampled from a distribution with mean and variance vectors, thus increasing the likelihood of correct classifications, and the batch-norm layer to stabilize the training process. The issue of CFis dealt with the idea of delta parameters, small task-specific trainable parameters while keeping the backbone networks frozen. A non-parametric approach is developed to infer the delta parameters for the model's predictions. The prototype rectification approach is applied to avoid biased prototype calculations due to the issue of data scarcity. The advantage of ROBUSTAis demonstrated through a series of experiments in the benchmark problems where it is capable of outperforming prior arts with big margins without any data augmentation protocols.

Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications. This paper proposes an adaptive transformer network (ADAPTER), a simple but effective solution for cross-domain few-shot learning where there exist large domain shifts between the base task and the target task. ADAPTER is built upon the idea of bidirectional cross-attention to learn transferable features between the two domains. The proposed architecture is trained with DINO to produce diverse, and less biased features to avoid the supervision collapse problem. Furthermore, the label smoothing approach is proposed to improve the consistency and reliability of the predictions by also considering the predicted labels of the close samples in the embedding space. The performance of ADAPTER is rigorously evaluated in the BSCD-FSL benchmarks in which it outperforms prior arts with significant margins.

Achieving real-time agility and adaptation with respect to changing requirements in existing IT infrastructure can pose a complex challenge. We describe a goal-oriented approach to manage this complexity. We argue that a goal-oriented perspective can form an effective basis for devising and deploying responses to changed requirements at runtime. We offer an extended vocabulary of goal types by presenting two novel conceptions: differential goals and integral goals, which we formalize in both linear-time and branching-time settings. We describe goal lifecycles and interactions and the extended notion of context for the representation of rapidly changing, complex operating environments. We then illustrate the working of the approach by presenting a detailed scenario of adaptation in a Kubernetes setting, in the face of a Distributed Denial-of-Service (DDoS) attack.

The symbiotic relationship between gut microbes and their hosts has been widely investigated by scientists. Currently, there exists lots of useful data dispersed in different gut microbiota databases. However, it is not easy for biologists to obtain valuable global information directly from these multi-sources and heterogeneous data for further research. Therefore, an ontology-mediated Gut Microbiota Federated Query Platform (GMFQP) is presented in this paper to solve this problem. Firstly, a Gut Microbiota Ontology (GMO) is built to provide a unified domain view for biologists, hiding the heterogeneity of the underlying data sources. Secondly, a three-phased federated query approach including query reasoning, rewriting, and execution based on Datalog+ rules is proposed to transform users’ queries expressed over the GMO to concrete queries executed over the actual data sources. Thirdly, a federated query prototype is implemented based on the approach mentioned above, providing a user-friendly query interface and returning results in both tabular and graphical formats. Several typical biological query examples with performance analysis are illustrated to demonstrate the utility of our approach and the platform.

Linked Open Data (LOD) is an ongoing effort in the Semantic Web community to build a massive public knowledge graph. The goal is to extend the Web by publishing various open datasets as RDF on the Web and then linking data items to other useful information from different data sources. With linked data, starting from a certain point in the graph, a person or machine can explore the graph to find other related data. In this paper, we develop a novel pipeline for graph-based biological data integration. By using our pipeline, users can easily glue heterogeneous biological ontologies, annotate sources with multiple join tables effectively, obtain a high-quality biological knowledge graph automatically, and enrich the knowledge graph with public biological ontologies finally. We implement a platform that realizes the proposed approach and conduct two case studies to evaluate the effectiveness and efficiency of our approach.

The integrated management of industrial systems in future environments like Industry 4.0 requires the effective management of information throughout the engineering life cycle. As systems pass through phases of design, construction, operation, maintenance, renewal or replacement, they will be administered via different information ecosystems, requiring changing perspectives on their descriptive information. A central role in the interplay of software and hardware artefacts, functions, documentation and managing software is played by the descriptions of concepts (i.e. formalised definitions of concepts within the domain of quantification). In this paper we propose a unified formalisation of descriptions that permits consistent analysis of the relationships between the designs, types, products, and concrete artefacts that can be found in the industrial engineering life-cycle. The approach is consistent with our earlier framework that describes artefacts, requirements and functional roles in the context of the DOLCE foundational ontology.

Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is an approach that is inflexible, incurs high costs, and leads to “silos” that prevent sharing data across different agencies or tasks. A standard approach to tackling this problem is to design a common ontology and to construct source descriptions which specify mappings between the sources and the ontology. Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Automatic semantic model has been gaining attention in data integration [5], federated data query [14] and knowledge graph construction [6]. This paper proposes an service-oriented architecture to create a correct semantic model, including annotating training data, training the machine learning model, and predict an accurate semantic model for new data source. Moreover, a holistic process for automatic semantic modeling is presented. By the usage of ASMaaS, historical semantic annotations for training machine learning model used in automatic semantic modeling can be shared, reducing costs of human resources from users. By specifying a well defined interface, users are able to have access to automatic semantic modeling process at any time, from anywhere. In addition, users must not be concerned with machine learning technologies and pipeline used in automatic semantic modeling, focusing mainly on the business itself.

In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated. The investigation showed (1) that the Faithfulness assumption can be weakened in various ways that in an important sense preserve its power, and (2) that weakening of Faithfulness may help to speed up methods based on Answer Set Programming. However, this line of work has so far only considered the discovery of causal models without latent variables. In this paper, we study weakenings of Faithfulness for constraint-based discovery of semi-Markovian causal models, which accommodate the possibility of latent variables, and show that both (1) and (2) remain the case in this more realistic setting.

In the era of big data, new challenges occur in the field of data federation research. New types of data sources with new formats of data have emerged, and end users need to conduct complex search and data analytical tasks, which impose requirements such flexible data federation, customized security mechanism and high-performance processing (for example, near real time query). To address these challenges, this paper proposes a data federation platform named FEDSA and reports on an initial implementation. Distinctive features of the platform include process-driven data federation, Data Federation as a Service, a simple query language over a high-level common data model, data security protection over all federation services, query re-writing and full distribution. We demonstrate how these features address the challenges, discuss the performance of the current implementation, and outline future extensions.

Due to the volume, variety, and veracity of network data available, information fusion and reasoning techniques are needed to support network analysts’ cyber-situational awareness. These techniques rely on formal knowledge representation to define the network semantics with data provenance at various levels of granularity. To this end, this paper proposes the Communication Network Topology and Forwarding Ontology, a state-of-the-art ontology that enables the formal, unified representation of complex network concepts regardless of the type of the data source. The implementation of this ontology allows network analysts to represent expert knowledge and query network data fused from disparate data sources.

In large organizations, multiple stakeholders may modify the same business process. This paper addresses the problem when stakeholders perform changes on process views which become inconsistent with the business process and other views. Related work addressing this problem is based on execution trace analysis which is performed in a post-analysis phase and can be complex when dealing with large business process models. In this paper, we propose a design-based approach that can efficiently check consistency criteria and propagate changes on-the-fly from a process view to its reference process and related process views. The technique is based on consistent specialization of business processes and supports the control flow aspect of processes. Consistency checks can be performed during the design time by checking simple rules which support an efficient change propagation between views and reference process.

Effective exchange of information about processes and industrial plants, their design, construction, operation, and maintenance requires sophisticated information modelling and exchange mechanisms that enable the transfer of semantically meaningful information between a vast pool of heterogeneous information systems. In order to represent entities relevant to the engineering life-cycle, social concepts, descriptions, roles, artefacts, functions, and information objects must be integrated in a coherent whole. Forming the basis of this integration in our framework is the DOLCE foundational ontology. In this paper we propose an ontologically well-founded approach to modelling artefacts, their requirement specifications and functional roles, such that consistency of their relationships in the data model can be verified. Specifically, we discuss the modelling of engineering artefacts, roles and role-filling capacity in the context of data modelling for industrial information exchange.

This paper addresses the problem of transforming natural language business specifications into formal models suitable for use in information systems. In particular, the Semantics of Business Vocabulary and Business Rules (SBVR) standard is used as a starting point for both the natural language specifications and the formal representation. In recent years, SBVR-based approaches have been proposed for transforming natural language business rules into models, such as UML Class Diagrams, however, most focus on the transformations performed after the SBVR models have been created and, therefore, simplify or entirely neglect the natural language to SBVR transformation. There are a number of difficulties in transforming natural language into formal models, such as ambiguity and inconsistency. This paper presents a solution based on a unique combination of techniques from Cognitive Linguistics and Knowledge-based Configuration in order to transform natural language business specifications into SBVR models. We present a comparative survey of state-of-the-art approaches and argue that current solutions do not fulfil the criteria necessary to meet our goals. We demonstrate our approach and show how it improves translation of natural language business specifications into formal models and increases the level of automation for Model-Driven Engineering.

The ability to build new (complex) services by composing existing services is one of the key benefits of the Service Oriented Architecture paradigm. Existing approaches to automate composition requires pre-planning or prediction of the number of required services, making them unsuitable in dynamic composition scenarios. To address this gap, we present a consistency-based service composition approach, where composition problems are modeled in a generative constraint-based formalism. We illustrate how the configuration of service processes differs from established constraint-based configuration techniques and develop an algorithm to synthesis valid service process compositions. We also show that our technique scales well to non-trivial problems.

Automated fault diagnosis has significant practical impact by improving reliability and facilitating maintenance of systems [1]. Given a monitor continuously receiving observations from a dynamic eventdriven system, diagnosis algorithms infer possible fault events that

Developing model-based automatic debugging strategies has been an active research area for several years, with the aim of locating defects in a program by utilising fully automated generation of a model of the program from its source code. We provide an overview of current techniques in model-based debugging and assess strengths and weaknesses of the individual approaches. An empirical comparison is presented that investigates the relative accuracy of different models on a set of test programs and fault assumptions, showing that our abstract interpretation based model provides high accuracy at significantly less computational effort than slightly more accurate techniques. We compare a range of model-based debugging techniques with other state-of-the-art automated debugging approaches and outline possible future developments in automatic debugging using model-based reasoning as the central unifying component in a comprehensive framework.

Modelling complex processes in the service oriented architecture paradigm typically requires the composition of a number of simpler services to achieve a desired goal. We present a semantic service composition approach that is based on the use of UML activity diagrams as abstract specification language and propose a service selection process that combines both conceptual and instance-level analysis to locate suitable services. Model-driven principles allow to adhere to design requirements that would otherwise be difficult to incorporate informal composition frameworks. Explicit modelling of behaviour in the presence of failures permit to create workflows that are robust in case of service execution errors. This approach may allow to synthesise concrete service orchestrations that are superior to similar approaches purely based on type-based or conceptual matchmaking.

Even with modern software development methodologies, the actual debugging of source code, i.e., location and identification of errors in the program when errant behavior is encountered during testing, remains a crucial part of software development. To apply model-based diagnosis techniques which have long been state of the art in hardware diagnosis, for automatic debugging a model of a given program must be automatically created from the source code. This work describes a model that reflects the sequential execution semantics of the Java language, including exceptions and unstructured control flow, thereby providing unprecedented scope in the application of model-based diagnosis to programs. Notably, this approach omits the strict view of a component representing one statement of earlier work and provides a more flexible mapping from code to model.