A Partial Semantics for Object Data Models with Static Binding

Ontology-based data management and knowledge graphs have emerged in recent years as efficient approaches for managing and utilizing diverse and large data sets. In this regard, research on algorithms for automatic semantic labeling and modeling as a prerequisite for both has made steady progress in the form of new approaches. The range of algorithms varies in the type of information used (data schema, values, or metadata), as well as in the underlying methodology (e.g., use of different machine learning methods or external knowledge bases). Approaches that have been established over the years, however, still come with various weaknesses. Most approaches are evaluated on few small data corpora specific to the approach. This reduces comparability and also limits statements for the general applicability and performance of those approaches. Other research areas, such as computer vision or natural language processing solve this problem by providing unified data corpora for the evaluation of specific algorithms and tasks. In this paper, we present and publish VC-SLAM to lay the necessary foundation for future research. This corpus allows the evaluation and comparison of semantic labeling and modeling approaches across different methodologies, and it is the first corpus that additionally allows to leverage textual data documentations for semantic labeling and modeling. Each of the contained 101 data sets consists of labels, data and metadata, as well as corresponding semantic labels and a semantic model that were manually created by human experts using an ontology that was explicitly built for the corpus. We provide statistical information about the corpus as well as a critical discussion of its strengths and shortcomings, and test the corpus with existing methods for labeling and modeling.

The Prognostic and Health Management (PHM) system of an aircraft has complex structures and diverse functions. It is highly coupled with other systems, such as the avionics system and flight management system. The Model-based Systems Engineering (MBSE) method is effective to support the design and verification of the aircraft PHM system. As a powerful semantic web construction method, ontology has been widely used to express design information, such as the concepts and the relationships between them. However, traditional graphical MBSE models have a natural weakness in transforming into ontology. In this paper, a semantic MBSE method is proposed to support the transform of the ontology model. Firstly, according to the design characteristics of the aircraft PHM system, a meta-model library of the aircraft PHM system is developed to support the design and evaluation. An MBSE modeling method based on requirement analysis, function analysis, logical architecture design, and physic architecture design is applied in the PHM design process. Secondly, the semantic system modeling language KARMA based on “graph, object, property, point, relationship, role, and extension” (GOPPRRE) is used to transfer the graphical MBSE model to the semantic MBSE model, which can be easily transformed to an ontology model. Finally, an ontology based on semantic modeling is developed to describe the MBSE entities and to support MBSE design. In this paper, a case study of an aircraft fuel PHM system is carried out to validate the proposed method. Based on the developed meta-model library, a complete MBSE design process for the aircraft PHM system is realized. And then an ontology model supporting PHM system design is generated from the semantic MBSE model. The MBSE ontology provides a shareable capability to help designers communicate effectively. Quantitative analysis based on ontology is also provided to verify the complexity and scale of the MBSE design process. Moreover, logical reasoning ability can also be provided to support the early requirement traceability for MBSE design. In general, the case study results show the feasibility and effectiveness of the proposed method for the aircraft PHM system design.

The management of social phenomena in conceptual modelling requires a novel understanding of the notion of representation. In particular, the principles for the existence and identification of objects need to be reconsidered. To do this, the paper draws on the current ontological discourse in information systems engineering and proposes a sociomaterial ontology for supporting conceptual modeling. The ontology shows how organisational entities are grounded in physical ones and how they can be understood in terms of deontic notions like privileges, duties and powers. The sociomaterial ontology is able to assist designers in creating understandable and robust conceptual models.

The complexity of a supply chain network (SCN) is rooted in its complex structures, multiple decision-making (DM) entities, adaptive behaviors, and open environments. Due to its unique advantages, computational experiment (CE) has been increasingly adopted as one of the most important methods for SCN complexity research. Data that are generated by computational experiments must be analyzed using effective tools. Depending on this analysis, DM acts as an important analysis and selection mechanism for the optimization and design of SCNs. This optimization and design rely on the combination of CE and DM. This combination inevitably involves multiple types of knowledge in the domains of SCNs, CE, and DM, which have been less comprehensively considered in recent studies. It remains a challenge for researchers and practitioners to clarify the knowledge system of SCNs and select the most suitable research perspectives, paradigms, and methods for CEs and DM of SCNs. To confront this challenge, it is necessary to systematically model the semantics of the knowledge that is involved in CE and DM to realize the consistency and interoperability of models, methods, and processes. Therefore, this paper uses a semantic network approach to construct a semantic model to clarify the knowledge framework of CEs and DM of SCNs. This knowledge framework is composed of the important knowledge elements that are extracted from the domains of SCNs, CE, and DM. The application procedure of the semantic model is demonstrated on a four-echelon SCN case. The semantic model’s understandability, consistency, reusability, procedure, systematization, and linkage analysis capability are evaluated. The results demonstrate that the semantic model is effective in providing a consistent, procedural, and systematic perspective for SCN complexity research and supporting linkage analysis among SCN modeling, CE, and DM.

A functional approach to conceptual modeling integrating recent semantic database models is presented. The notion of attribute (MAT78], [DKMS86], [ZLA85], [POK89]), [POK90]) as a basic modeling construct is used. The presented results show how to deal exactly with so called semantic relativism, i.e how to express that two different semantic database models have the same expressive power. We show that it is easy to reach the property for many of well-known semantic database models.KeywordsSemantic RelativismSemantic ModelConceptual SchemaExpressive PowerObject SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The algebra of stream processing functions

Disabled people can benefit greatly from assistive digital technologies. However, this increased human-machine symbiosis makes it important that systems are personalized and transparent to users. Existing work often uses data-oriented approaches. However, these approaches lack transparency and make it hard to influence the system’s behavior. In this paper, we use knowledge-based techniques for personalization, introducing the concept of Semantic User Models for representing the behavior, values and capabilities of users. To allow the system to construct such a user model, we investigate the use of a conversational agent which can elicit the relevant information from users through dialogue. A conversational interface is essential for our case study of navigation support for visually impaired people, but in general, has the potential to enhance transparency as users know what the system represents about them. For such a dialogue to be effective, it is crucial that the user understands what the conversational agent is asking, i.e., that misalignments that decrease the transparency are avoided or resolved. In this paper, we investigate whether we can use a conversational agent for Semantic User Model elicitation, which types of misalignments can occur in this process and how they are related, and how misalignments can be reduced. We investigate this in two (iterative) qualitative studies (n = 7 & n = 8) with visually impaired people in which a personalized user model for navigation support is elicited via a dialogue with a conversational agent. Our results show four hierarchically structured levels of human-agent misalignment. We identify several design solutions for reducing misalignments, which point to the need for restricting the generic user model to what is needed in the domain under consideration. With this research, we lay a foundation for conversational agents capable of eliciting Semantic User Models.

The formal description of the semantics of object-oriented data models is still a heavily debated research problem. This paper shows how evolving algebras provide an elegant way to specify the operational semantics of object-oriented data models. In particular, we define the formal semantics of a novice feature of object-oriented data models proposed recently by different authors: role hierarchies. As opposed to traditional class hierarchies, role hierarchies allow objects to dynamically acquire multiple independent types.Keywordsevolving algebraoperational semanticsobject-oriented data modelsinheritanceroles

Aircraft assembly is a complex process associated with different stakeholders, which strongly impacts the R&D cost for the entire aircraft lifecycle management. Traditional aircraft assembly process is designed based on a document-centric approach which cannot provide graphical notations for engineers to understand the entire assembly process. Moreover, it is difficult to make use of simulation to analyze the assembly process in an automatic way that increases the R&D cost for the assembly process design. In this paper, a semantic modeling approach is proposed to support aircraft assembly process formalism and performance analysis of the entire assembly process. First, semantic modeling language KARMA is used to develop meta-models based on the basic elements of discrete event simulation model for aircraft assembly process modeling. After KARMA models are developed, ontology model is generated from KARMA models for implementing the automatic simulation execution by using the discrete event simulation.KeywordsModel-based systems engineeringSemantic modelingKARMA languageDiscrete event simulationAircraft assembly

Analyzing of the Biodiesel Production From Cooking Oil

Modeling of Information System for Air Waste Management

Let g \mathfrak {g} be a simple finite-dimensional complex Lie algebra with a Cartan subalgebra h \mathfrak {h} and Weyl group W W . Let g n \mathfrak {g}_n denote the Lie algebra of n n -jets on g \mathfrak {g} . A theorem of Raïs and Tauvel and Geoffriau identifies the centre of the category of g n \mathfrak {g}_n -modules with the algebra of functions on the variety of n n -jets on the affine space h ∗ / W \mathfrak {h}^*/W . On the other hand, a theorem of Feigin and Frenkel identifies the centre of the category of critical level smooth modules of the corresponding affine Kac-Moody algebra with the algebra of functions on the ind-scheme of opers for the Langlands dual group. We prove that these two isomorphisms are compatible by defining the higher residue of opers with irregular singularities. We also define generalized Verma and Wakimoto modules and relate them by a nontrivial morphism.

Several agent-oriented software engineering methodologies address the emerging challenges posed by the increasing need of adaptive software. A common denominator of such methodologies is the paramount importance of the concept of goal model in order to understand the requirements of a software system. Goal models consist of goal graphs representing AND/OR-decomposition of abstract goals down to operationalisable leaf-level goals. Goal models are used primarily in the earlier phases of software engineering, for social modelling, requirements elicitation and analysis, to concretise abstract objectives, to detail them and to capture alternatives for their satisfaction. Although various agent programming languages incorporate the notion of (leaf-level) goal as a language construct, none of them natively support the definition of goal models. However, the semantic gap between goal models used at design-time and the concept of goal used at implementation and execution time represent a limitation especially in the development of self-adaptive and fault-tolerant systems. In such systems, design-time knowledge on goals and variability becomes relevant at run-time, to take autonomous decisions for achieving high level objectives correctly. Recently, unifying operational semantics for (leaf) goals have been proposed [15]. We extend this work to define an operational semantics for the behaviour of goals in goal models, maintaining the flexibility of using different goal types and conditions. We use a simple example to illustrate how the proposed approach effectively deals with the semantic gap between design-time goal models and run-time agent implementations.

Semantics is a research field that has gained an extensive interest recently. This survey describes recent works in the field of semantics, a part of the broader area of computational linguistics. One of the important aspects of computational linguistics is using proper methods to distribute semantics for obtaining representations of the meaning of words. This survey summarizes the latest state of the art approaches in semantics that use deep learning methods, datasets, and lexical databases, specifying semantics under two categories such as semantic similarity and sentence modeling.

The feature representation has significantly profound impact on the segmentation accuracy. This paper proposes a feature extract method for conditional random fields (CRF) to segment image into object and background. It divides the pixel into the different semantic model via Gaussian Mixture Model (GMM), then uses the mean values and covariance of every sub-component in GMM for CRF. The proposed method takes the probability distribution of the image as the feature representation for CRF learning. Then it use the CRF to segment the image into semantic parts with the homogeneous appearance based on the minimum energy, which is computed via data costs, label costs and smooth costs. We do performance evaluation experiments on the MSRC-21 benchmark. Experimental results show the proposed method is competitive to the state-of-the-art method for object segmentation.