SMOL: a systemic methodology for ontology learning from heterogeneous sources

Gil, Richard; Martín-Bautista, María J.

doi:10.1007/s10844-013-0296-x

Cited by 16 publications

(8 citation statements)

References 37 publications

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“…For the design of the semantic model, we adopted a methodology that aims to facilitate the development of production process and data analytics ontologies so that the ontology developer can justify the rationale behind the involved decisions. The base of the proposed methodology is SMOL [35], which was extended using the concepts introduced by Grüninger and Fox [36]. The final proposed methodology emphasizes conceptualization based on the usage scenarios for the knowledge model.…”

Section: Methodsmentioning

confidence: 99%

Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

2019

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The process industries rely on various software systems and use a wide range of technologies. Predictive modeling techniques are often applied to data obtained from these systems to build the predictive functions used to optimize the production processes. Therefore, there is a need to provide a proper representation of knowledge and data and to improve the communication between the data scientists who develop the predictive functions and domain experts who possess the expert knowledge of the domain. This can be achieved by developing a semantic model that focuses on cross-sectorial aspects rather than concepts for specific industries, and that specifies the meta-classes for the formal description of these specific concepts. This model should cover the most important areas including modeling the production processes, data analysis methods, and evaluation using the performance indicators. In this paper, our primary objective was to introduce the specifications of the Cross-sectorial domain model and to present a set of tools that support data analysts and domain experts in the creation of process models and predictive functions. The model and the tools were used to design a knowledge base that could support the development of predictive functions in the green anode production in the aluminum production domain.

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Section: Methodsmentioning

confidence: 99%

Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

2019

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“…OL&P researchers have used natural language processing, machine learning, logic-based, and various other techniques to achieve their respective goals [18] [20] [21] [22]. Some of the OL&P systems are [23], [24], [25], [26].…”

Section: Ontology Learning From Textmentioning

confidence: 99%

Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle

Sharma¹,

Sodhi²

2019

Preprint

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Domain-specific knowledge graphs constructed from natural language text are ubiquitous in today's world. In many such scenarios the base text, from which the knowledge graph is constructed, concerns itself with practical, on-hand, actual or ground-reality information about the domain. Product documentation in software engineering domain are one example of such base texts. Other examples include blogs and texts related to digital artifacts, reports on emerging markets and business models, patient medical records, etc. Though the above sources contain a wealth of knowledge about their respective domains, the conceptual knowledge on which they are based is often missing or unclear. Access to this conceptual knowledge can enormously increase the utility of available data and assist in several tasks such as knowledge graph completion, grounding, querying, etc. Additionally, this conceptual knowledge can also be used to find common semantics across different knowledge graphs. Our contributions in this paper are twofold. First, we propose a novel Markovian stochastic model for document generation from conceptual knowledge. This model is based on the assumption that a writer generates successive sentences of a text only based on her current context as well as the domain-specific conceptual knowledge in her mind. The uniqueness of our approach lies in the fact that the conceptual knowledge in the writer's mind forms a component of the parameter set λ of our stochastic model. Secondly, we solve the inverse problem of learning the best conceptual knowledge from a given document O, by finding model parameters λ * which maximize the likelihood of generating document O over all possible parameter values. This likelihood maximization is done using an application of Baum-Welch algorithm, which is a known special case of Expectation-Maximization (EM) algorithm. We run our conceptualization algorithm on several well-known natural language sources and obtain very encouraging results. The results of our extensive experiments concur with the hypothesis that the information contained in these sources has a well-defined and rigorous underlying conceptual structure, which can be discovered using our method.

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“…Kumar and Harding () proposed an ontology mapping approach, employing description logic based on bridging axioms between the ontologies to achieve an interoperability of knowledge and data sharing among small‐ and medium‐sized enterprises to promote the form of virtual enterprises. Gil and Martin‐Bautista () further proposed the Systemic Methodology for Ontology Learning from heterogeneous source ontologies to support data integration and the complementary knowledge acquisition processes. Ngamnij and Somjit (Arch‐int et al ., ) proposed structural conflict detection and resolution techniques to resolve ontology heterogeneity, enabling interoperability between existing shared learning resource systems through the common ontology of learning resources.…”

Section: Literature Reviewmentioning

confidence: 99%

A semantic interoperability approach to health‐care data: Resolving data‐level conflicts

Sonsilphong

Arch-int

et al. 2016

Expert Systems

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The integration of data from various electronic health record (EHR) systems presents a critical conflict in the sharing and exchanging of patient information across a diverse group of health‐oriented organizations. Patient health records in each system are annotated with ontologies utilizing different health‐care standards, creating ontology conflicts both at the schema and data levels. In this study, we introduce the concept of semantic ontology mapping for the facilitation and interoperability of heterogeneous EHR systems. This approach proposes a means of detecting and resolving the data‐level conflicts that generally exist in the ontology mapping process. We have extended the semantic bridge ontology in support of ontology mapping at the data level and generated the required mapping rules to reconcile data from different ontological sources into a canonical format. As a result, linked‐patient data are generated and made available in a semantic query engine to facilitate user queries of patient data across heterogeneous EHR systems.

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SMOL: a systemic methodology for ontology learning from heterogeneous sources

Cited by 16 publications

References 37 publications

Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle

A semantic interoperability approach to health‐care data: Resolving data‐level conflicts

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