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semantic Concept Recommendation for Continuously Evolving Knowledge Graphs 1

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21st International Conference on Enterprise Information Systems (ICEIS)

作者： Pomp, Andre Kraus, Vadim Poth, Lucian Meisen, Tobias Univ Wuppertal Chair Technol & Management Digital Transformat D-42119 Wuppertal Germany

ISBN: (数字)9783030407834

ISBN: (纸本)9783030407834;9783030407827

With the digitalization of many industrial processes and the increasing interconnection of devices, the number of data sources and associated data sets is constantly increasing. Due to the heterogeneity of these large amounts of data sources, finding, accessing and understanding them is a major challenge for data consumers who want to work with the data. In order to make these data sources searchable and understandable, the paradigms of Ontology-Based data Access (OBDA) or Ontology-Based data Integration (OBDI) are used today. An important part of these paradigms is the creation of a mapping, such as a semantic model, between a previously defined ontology and the existing data sources. Although there are already many approaches that automate the creation of this mapping by using data-driven or data-structure-driven approaches, none of them focuses on the fact that the underlying ontology evolves over time. However, this is essential in today's age of large amounts of data and ever-growing number of data sources. In this paper, we propose an approach that allows the recommendation of semantic concepts for data attributes based on a constantly evolving knowledge graph. The approach allows the knowledge graph to learn data-driven representations for any concept that is available in the knowledge graph and that is already mapped to at least one data attribute. Instead of supporting a single method for recommending semantic concepts, we design the approach to be able to learn multiple data representatives per semantic concept, with each representative being trained on a different method, such as machine learning classifiers, rules, or statistical methods. In this way, for example, we are able to distinguish between different data types and data distributions. In order to evaluate our approach, we have trained it on several different publicly available data sets. In comparison to existing approaches, our evaluation shows that the accuracy of the recommendation improves th

关键词： semantic model Knowledge graph Ontologies Machine learning ESKAPE semantic recommendation semantic labeling semantic data platform

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semantic Internet of Things 10

Semantic Internet of Things

引用

10th IEEE International Conference on semantic Computing (ICSC)

作者： Palavalli, Amarnath Karri, Durgaprasad Pasupuleti, Swarnalatha VMware Palo Alto CA 94304 USA

ISBN: (纸本)9781509006625

semantic IoT Framework is a flexible data management and processing platform for any type of data, built on semantic data platform (SDP). SDP is based on declarative fact oriented approach for modeling, that enables the models to be executable themselves. The declarative nature of data semantics enables defining or modifying of data models dynamically with new concepts, relationships or constraints without having to change any other parts of the systems. The Event Manager, data Processing and Analytics engine forms the core part of the proposed semantic IoT framework, which enables consumers to listen to metadata and data changes, derive analytics and fire domain specific business rules or policies. Therefore, semantic IoT approach provides great agility in defining, modifying and interpreting metadata. A simple home automation usecase is presented to demonstrate how a device can publish its metadata as semantic facts including business rules or policies as constraints as well as publish the data to semantic data service running on the cloud infrastructure. The future work presented includes extending the semantic approach to collaboratively evolve IoT data model standard among device vendors.

关键词： IoT Ontology Object Role Modeling Fact Oriented Modeling semantic data platform Cloud Computing

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xDaaS: Any data as a Service 10

xDaaS: Any Data as a Service

引用

10th IEEE International Conference on semantic Computing (ICSC)

作者： Palavalli, Amarnath Karri, Durgaprasad Pasupuleti, Swarnalatha VMware Palo Alto CA 94304 USA

ISBN: (纸本)9781509006625

Integration of products in enterprises comes with hard challenges due to several factors such as products developed in house, off the shelf, developed over different time lines, available as services over internet, ever changing product APIs, disconnected data models among the products, extensions developed by partners and customers and many more. We propose semantic data platform (SDP), built around the semantics of the information derived out of product integration. This approach of semantic model based data sharing provides extreme loose coupling among products, makes extension of information from a given product possible with zero impact on rest of the system. Easy certification of integration enables evolution of products fairly independent of each other. SDP offers semantic federation and querying, therefore it can fetch any data irrespective of the product where the data lives. Thus, 'Any data from a product can be offered as a Service'.

关键词： Cloud Computing Enterprise data Integration Fact Oriented Modeling Object Role Modeling Ontology Ontology Design Pattern ORM semantic data platform

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