Gathering and Combining Semantic Concepts from Multiple Knowledge Bases

Paulus, Alexander; Pomp, André; Poth, Lucian; Lipp, Johannes; Meisen, Tobias

doi:10.5220/0006800700690080

Cited by 10 publications

(9 citation statements)

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“…We thus also detect two concepts from dierent participants as the same, whenever they match one of the above rules. Paulus et al describe in [34] how one can unify these possible inconsistencies for names and data attributes.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…If a concept is not present in the knowledge graph, it needs to be added. To propose concepts suggestions to the user, which are thoroughly specied according to the above criteria, the user wizard queries external knowledge bases based on the framework provided by Paulus et al [34] as depicted in Figure 2. This framework already gathers and combines semantic concepts from multiple knowledge bases.…”

Section: User Wizardmentioning

confidence: 99%

“…As soon as the user nishes the modeling process, the semantic model is submitted to the ESKAPE platform and is validated before the actual evolution of the knowledge graph's ontology takes place. [34]. This is an exemplary setup with two knowledge bases activated, BabelNet and WordNet.…”

Section: User Wizardmentioning

confidence: 99%

“…We prepare a dedicated room with a laptop that runs ESKAPE. We limit the external knowledge bases in our evaluation to BabelNet to circumvent possible race conditions resulting from the merge algorithm of the underlying semantic concept gathering framework of Paulus et al [34]. The attendees do not execute the steps of this study isolated, but have an evaluation expert sitting next to them for two purposes.…”

Section: Autonomous Evolution From External Knowledge Basesmentioning

confidence: 99%

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Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management

Pomp

Lipp

Meisen

2019

IJRC

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Companies suer from heterogeneous data sources that are distributed across all business units and locations. Accessing, discovering, and understanding these data sources is challenging because data scientists have to deal with dierent protocols, data formats, and even company-specic organizational issues such as rewalls and privacy policies. Ontology-Based Data Management (OBDM) provides dierent aspects for reducing the barriers of integrating, accessing and managing heterogeneous data sources by using ontologies. For that, we establish a mapping between data sources and one or multiple target ontologies. However, the biggest challenges in ODBM are creation and administration of required ontologies. Usually, these ontologies are created in advance, for instance, by ontology engineers and domain experts that closely work together for manually designing and even maintaining the ontology. In order to enhance the process of designing and maintaining an ontology, we propose an approach consisting of an evolving knowledge graph that includes an internal ontology, which continuously evolves on demand as domain experts add new data sources and dene the mapping between the ontology and that data source. For this purpose, we develop an intuitive, user-oriented wizard and combine it with a semi-supervised evolution strategy that supports the user with the help of external knowledge databases. Moreover, we equip the system with additional logic that allows to automatically link related concepts of the ontology. We evaluate the accuracy and usability of our approach by conducting a user 20 André Pomp et al. study. The results show that mappings become more objective and consistent with our provided user wizard, resulting in a knowledge graph with higher connectivity and stability.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: User Wizardmentioning

confidence: 99%

Section: User Wizardmentioning

confidence: 99%

Section: Autonomous Evolution From External Knowledge Basesmentioning

confidence: 99%

See 2 more Smart Citations

Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management

Pomp

Lipp

Meisen

2019

IJRC

Self Cite

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“…Nevertheless, this on-demand expansion has the disadvantage that it may lead to inconsistencies or undefined relationships. Therefore, our research in ESKAPE also focuses on automatically detecting and aligning of vocabularies, e.g., by detecting synonyms based on the framework provided by Paulus et al [21], whereas current tagging systems or the automatic collection of meta-data do not deal with the alignment of the used terms/labels of the vocabulary. This reduces the effort for manual alignment of vocabularies.…”

Section: A T L Rmentioning

confidence: 99%

Applying Semantics to Reduce the Time to Analytics within Complex Heterogeneous Infrastructures

et al. 2018

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In today’s age of modern information technology, large amounts of data are generated every second to enable subsequent data aggregation and analysis. However, the IT infrastructures that have been set up over the last few decades and which should now be used for this purpose are very heterogeneous and complex. As a result, tasks for analyzing data, such as collecting, searching, understanding and processing data, become very time-consuming. This makes it difficult to realize visions, such as the Internet of Production, which pursues the goal of guaranteeing the availability of real-time information at any time and place in an industrial setting. To reduce the time to analytics in such scenarios, we present a data ingestion, integration and processing approach consisting of a flexible and configurable data ingestion pipeline as well as a semantic data platform named ESKAPE. The ingestion pipeline provides an abstraction to all tasks related to data acquisition. The main goal is, therefore, the controllable access to data and meta information contained in machines and other systems on the shop floor. Additionally, it provides the possibility to forward the collected data to a configurable endpoint, such as a data lake. ESKAPE acts as one of those endpoints enabling semantic data integration and processing. By annotating data sets with semantic models originating from the Semantic Web, data analysts are able to understand, process and discover these data sets more efficiently. ESKAPE features a three-layered information storage architecture consisting of a data layer for storing integrated raw data sets, a layer containing user-defined semantic models to describe the contextual knowledge necessary to interpret the stored data and a top layer formed by a continuously evolving knowledge graph, combining semantic information from all present semantic models. Based on this storage system, ESKAPE enables the flexible annotation as well as efficient search and processing of data sources without losing the ability of analyzing and querying the underlying raw data with analytic tools. We present and discuss our approach and its benefits and limitations based on a real-world industrial use case.

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