An Analysis of Description Logic Augmented with Domain Rules for the Development of Product Models

The languages and logical formalisms developed by information scientists and logicians concentrate on the theory of languages and logical theorem proving. These languages, when used by domain experts to represent their domain of discourse, most often have issues related to the level of expressiveness and need specific extensions. In this paper, we first analyze the requirements for the development of structured knowledge representation models for manufacturing products. We then explore how these requirements can be satisfied through the levels of logical formalisms and expressivity of a structured knowledge representation model. We report our analysis of description logic (DL) and domain-specific rules with respect to the requirements by giving an example of a product ontology developed with ontology web language-description logic (OWL) and augmented with semantic web rule language (SWRL) rules. Clearly, increasing the expressivity of a product ontology also improves that of domain-specific rules, but there exits the usual tradeoff between the expressivity of languages and the complexity of their reasoning tasks. We present a case study of an electromechanical product to validate the analysis and further show how the OWL-DL reasoner together with the rule engine can enable reasoning about the product ontology. We finally discuss the open issues such as capabilities and limitations related to the usage of DL, OWL, and SWRL for product modeling.

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Description Logic for Product Information Models

Volume 3: 28th Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2008-49348 ◽

2008 ◽

Cited By ~ 8

Author(s):

X. Fiorentini ◽

S. Rachuri ◽

M. Mahesh ◽

S. Fenves ◽

Ram D. Sriram

Keyword(s):

Theorem Proving ◽

Description Logic ◽

Product Information ◽

Rule Engine ◽

Product Model ◽

Domain Experts ◽

Information Models ◽

Structured Knowledge ◽

Product Assembly

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OntCheck: An Ontology-Driven Static Correctness Checking Tool for Component-Based Models

Journal of Applied Mathematics ◽

10.1155/2013/934349 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Xi Lin ◽

Hehua Zhang ◽

Ming Gu

Keyword(s):

Large Scale ◽

Description Logic ◽

Early Stage ◽

Semantic Knowledge ◽

Unified Framework ◽

Domain Specific ◽

Scale Models ◽

Correctness Checking ◽

Static Checking

Component-based models are widely used for embedded systems. The models consist of components with input and output ports linked to each other. However, mismatched links or assumptions among components may cause many failures, especially for large scale models. Binding semantic knowledge into models can enable domain-specific checking and help expose modeling errors in the early stage. Ontology is known as the formalization of semantic knowledge. In this paper we propose an ontology-driven tool for static correctness checking of domain-specific errors. two kinds of important static checking, semantic type and domain-restrcted rules, are fulfilled in a unified framework. We first propose a formal way to precisely describe the checking requirements by ontology and then separately check them by a lattice-based constraint solver and a description logic reasoner. Compared with other static checking methods, the ontology-based method we proposed is model-externally configurable and thus flexible and adaptable to the changes of requirements. The case study demonstrates the effectiveness of our method.

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Ontology based knowledge representation: Case study from agriculture domain

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-210055 ◽

2021 ◽

Vol 25 (1) ◽

pp. 97-108

Author(s):

Nidhi Malik ◽

Deena Hijam ◽

Aditi Sharan

Keyword(s):

Knowledge Representation ◽

Large Scale ◽

Relevant Information ◽

Ontology Development ◽

Common Data Model ◽

Domain Experts ◽

Agriculture Sector ◽

Time Usage ◽

Representation Scheme

The growth of web has touched everyone’s life from an economist, entrepreneur, and academician to a farmer. The agriculture sector is quite important for any country’s growth. Farmers can also be benefited if they are provided with relevant information they need. The reason that it is not happening on a large scale is due to many reasons. Information is available in different formats, platforms and it is highly unstructured. The availability and real time usage of this information is prohibited mainly by the way it is represented. Recently, Ontology has emerged as one of very expressive knowledge representation scheme which enables gathering information from heterogeneous sources and creates a common data model that is shared and agreed upon in diverse domains. The interoperability and deduction capabilities that Ontology offers are very useful for generating new knowledge. Most of the work pertaining to ontology development in agriculture domain is crop specific, where information about fertilizers is confined to a particular crop only. In this paper, we have designed and developed a generic ontology for fertilizers. Fertilizer itself is a concept which should be modeled independently. Even for crop specific ontology, fertilizer is one of the most important concept to be captured. So, it needs to be represented independently. Finding this research gap, an ontology in fertilizer subdomain is developed, taking into account the various issues that are faced while constructing an ontology and the enormous amount of data that is available but is not easy to structure and present in the form of ontology. We have also validated the Ontology using an available tool and domain experts have also validated the developed Ontology.

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From DCOM interfaces to domain-specific modeling language: A case study on the sequencer

Computer Science and Information Systems ◽

10.2298/csis101231009k ◽

2011 ◽

Vol 8 (2) ◽

pp. 361-378 ◽

Cited By ~ 7

Author(s):

Tomaz Kos ◽

Tomaz Kosar ◽

Jure Knez ◽

Marjan Mernik

Keyword(s):

Software Development ◽

Measurement System ◽

Modeling Language ◽

Domain Experts ◽

Measurement Systems ◽

Domain Specific ◽

Programming Library ◽

Domain Specific Modeling ◽

Specific Modeling

Software development is a demanding process, since it involves different parties to perform a desired task. The same case applies to the development of measurement systems - measurement system producers often provide interfaces to their products, after which the customers? programming engineers use them to build software according to the instructions and requirements of domain experts from the field of data acquisition. Until recently, the customers of the measurement system DEWESoft were building measuring applications, using prefabricated DCOM objects. However, a significant amount of interaction between customers? programming engineers and measurement system producers is necessary to use DCOM objects. Therefore, a domain-specific modeling language has been developed to enable domain experts to program or model their own measurement procedures without interacting with programming engineers. In this paper, experiences gained during the shift from using the DEWESoft product as a programming library to domain-specific modeling language are provided together with the details of a Sequencer, a domain-specific modeling language for the construction of measurement procedures.

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myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with No Programming

Future Internet ◽

10.3390/fi11030059 ◽

2019 ◽

Vol 11 (3) ◽

pp. 59 ◽

Cited By ~ 3

Author(s):

Mayank Kejriwal ◽

Pedro Szekely

Keyword(s):

Law Enforcement ◽

Knowledge Discovery ◽

Securities Fraud ◽

Specific Knowledge ◽

Domain Experts ◽

Domain Specific ◽

Programming Effort ◽

Domain Specific Knowledge ◽

Structured Knowledge ◽

Set Up

With advances in machine learning, knowledge discovery systems have become very complicated to set up, requiring extensive tuning and programming effort. Democratizing such technology so that non-technical domain experts can avail themselves of these advances in an interactive and personalized way is an important problem. We describe myDIG, a highly modular, open source pipeline-construction system that is specifically geared towards investigative users (e.g., law enforcement) with no programming abilities. The myDIG system allows users both to build a knowledge graph of entities, relationships, and attributes for illicit domains from a raw HTML corpus and also to set up a personalized search interface for analyzing the structured knowledge. We use qualitative and quantitative data from five case studies involving investigative experts from illicit domains such as securities fraud and illegal firearms sales to illustrate the potential of myDIG.

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QuenchML: A semantics-preserving markup language for knowledge representation in quenching

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060412000352 ◽

2013 ◽

Vol 27 (1) ◽

pp. 65-82

Author(s):

Aparna S. Varde ◽

Mohammed Maniruzzaman ◽

Richard D. Sisson

Keyword(s):

Knowledge Representation ◽

Domain Knowledge ◽

Materials Science ◽

Heat Treating ◽

Markup Language ◽

Domain Experts ◽

Domain Specific ◽

Extensible Markup ◽

The Given ◽

Representation Of Knowledge

AbstractKnowledge representation (KR) is an important area in artificial intelligence (AI) and is often related to specific domains. The representation of knowledge in domain-specific contexts makes it desirable to capture semantics as domain experts would. This motivates the development of semantics-preserving standards for KR within the given domain. In addition to the storage and analysis of information using such standards, the effect of globalization today necessitates the publishing of information on the Web. Thus, it is advisable to use formats that make the information easily publishable and accessible while developing KR standards. In this article, we propose such a standard called Quenching Markup Language (QuenchML). This follows the syntax of the eXtensible Markup Language and captures the semantics of the quenching domain within the heat treating of materials. We describe the development of QuenchML, a multidisciplinary effort spanning the realms of AI, database management, and materials science, considering various aspects such as ontology, data modeling, and domain-specific constraints. We also explain the usefulness of QuenchML in semantics-preserving information retrieval and in text mining guided by domain knowledge. Furthermore, we outline the significance of this work in software tools within the field of AI.

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Pragmatic reuse for DSML development

Software & Systems Modeling ◽

10.1007/s10270-020-00831-4 ◽

2020 ◽

Author(s):

Stefan Klikovits ◽

Didier Buchs

Keyword(s):

Operational Semantics ◽

Reactive Systems ◽

Lessons Learned ◽

Hybrid Automata ◽

Domain Experts ◽

Domain Specific ◽

Sound Simulation ◽

Architecture Description ◽

Description Languages

Abstract By bridging the semantic gap, domain-specific language (DSLs) serve an important role in the conquest to allow domain experts to model their systems themselves. In this publication we present a case study of the development of the Continuous REactive SysTems language (CREST), a DSL for hybrid systems modeling. The language focuses on the representation of continuous resource flows such as water, electricity, light or heat. Our methodology follows a very pragmatic approach, combining the syntactic and semantic principles of well-known modeling means such as hybrid automata, data-flow languages and architecture description languages into a coherent language. The borrowed aspects have been carefully combined and formalised in a well-defined operational semantics. The DSL provides two concrete syntaxes: CREST diagrams, a graphical language that is easily understandable and serves as a model basis, and , an internal DSL implementation that supports rapid prototyping—both are geared towards usability and clarity. We present the DSL’s semantics, which thoroughly connect the various language concerns into an executable formalism that enables sound simulation and formal verification in , and discuss the lessons learned throughout the project.

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INCEPTION OF HARMONISING DATA SILOS AND URBAN SIMULATION TOOLS USING 3D CITY MODELS FOR SUSTAINABLE MANAGEMENT OF THE URBAN FOOD WATER AND ENERGY RESOURCES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-viii-4-w1-2021-81-2021 ◽

2021 ◽

Vol VIII-4/W1-2021 ◽

pp. 81-88

Author(s):

R. Padsala ◽

E. Gebetsroither-Geringer ◽

J. Peters-Anders ◽

V. Coors

Keyword(s):

New York ◽

Action Plan ◽

The United States ◽

Rapid Expansion ◽

Domain Experts ◽

Simulation Tools ◽

Domain Specific ◽

Energy Domain ◽

Tools And Techniques

Abstract. This paper explains the first insights into the ongoing development of a CityGML based Food Water Energy Application Domain Extension (FWE ADE). Cities are undergoing rapid expansion throughout the globe. As a result, they face a common challenge to provide food, water and energy (FWE) supplies under healthy and economically productive conditions. Consequently, new tools and techniques must be developed to support decision-makers, such as governments, public or private infrastructure providers, investors and city developers, to understand, quantify and visualise multiple interdependent impacts for the sustainable supply of the FWE resources. However, a common practice amongst these stakeholders is to work in their data silos, which frequently results in a lack of data integration and communication between domain specific simulation tools belonging to different infrastructure departments. As a result, insights related to critical indicators showing inter-dependency amongst different urban infrastructure are missed and hence, not included in the cities’ redevelopment action plan. This paper documents the first ongoing attempt by an international group of domain experts from food, water, energy, urban design and geoinformatics to harmonise the data silos of food, water and energy domain for the case study regions of the County of Ludwigsburg in Germany, the city of Vienna in Austria and the neighbourhood of Gowanus in New York, the United States of America.

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A clarified typology of core-periphery structure in networks

Science Advances ◽

10.1126/sciadv.abc9800 ◽

2021 ◽

Vol 7 (12) ◽

pp. eabc9800

Author(s):

Ryan J. Gallagher ◽

Jean-Gabriel Young ◽

Brooke Foucault Welles

Keyword(s):

Dense Core ◽

Block Model ◽

Hub And Spoke ◽

Block Modeling ◽

Domain Specific ◽

The Core ◽

Detailed Case ◽

Rich Diversity ◽

Modeling Techniques

Core-periphery structure, the arrangement of a network into a dense core and sparse periphery, is a versatile descriptor of various social, biological, and technological networks. In practice, different core-periphery algorithms are often applied interchangeably despite the fact that they can yield inconsistent descriptions of core-periphery structure. For example, two of the most widely used algorithms, the k-cores decomposition and the classic two-block model of Borgatti and Everett, extract fundamentally different structures: The latter partitions a network into a binary hub-and-spoke layout, while the former divides it into a layered hierarchy. We introduce a core-periphery typology to clarify these differences, along with Bayesian stochastic block modeling techniques to classify networks in accordance with this typology. Empirically, we find a rich diversity of core-periphery structure among networks. Through a detailed case study, we demonstrate the importance of acknowledging this diversity and situating networks within the core-periphery typology when conducting domain-specific analyses.

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Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

Applied Sciences ◽

10.3390/app11125476 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5476

Author(s):

Ana Pajić Simović ◽

Slađan Babarogić ◽

Ognjen Pantelić ◽

Stefan Krstović

Keyword(s):

Process Mining ◽

Modeling Language ◽

Sufficient Information ◽

Event Data ◽

Erp Systems ◽

Domain Experts ◽

Domain Specific ◽

Event Logs ◽

Domain Specific Modeling ◽

Specific Modeling

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.

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