Jieying Chen

• Koopmann, Patrick & Chen, Jieying (2020). Deductive module extraction for expressive description logics. CEUR Workshop Proceedings. ISSN 1613-0073. 2663.

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• Zhou, Dongzhuoran; Zhou, Baifan; Chen, Jieying; Cheng, Gong; Kostylev, Egor & Kharlamov, Evgeny (2021). Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding. Vis sammendrag

  Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology. However, the automatic generation of such ontology is demanding and existing solutions are often not satisfactory. An important challenge is a trade-off between two principles of ontology engineering: knowledge-orientation and data-orientation. The former one prescribes that an ontology should model the general knowledge of a domain, while the latter one emphasises on reflecting the data specificities to ensure good usability. We address this challenge by our method of ontology reshaping, which automates the process of converting a given domain ontology to a smaller ontology that serves as the KG schema. The domain ontology can be designed to be knowledge-oriented and the KG schema covers the data specificities. In addition, our approach allows the option of including user preferences in the loop. We demonstrate our on-going research on ontology reshaping and present an evaluation using real industrial data, with promising results.

• Zhou, Baifan; Zhou, Dongzhuoran; Chen, Jieying; Svetashova, Yulia; Cheng, Gong & Kharlamov, Evgeny (2021). Scaling Usability of ML Analytics with Knowledge Graphs: Exemplified with A BoschWelding Case. Vis sammendrag

  Automated welding is heavily used in the automotive industry to produce car bodies by connecting metal parts with welding spots. Modern welding solutions and manufacturing environments produce a high volume of heterogeneous data. Analytics of these data with machine learning (ML) can help to ensure high quality of welding operations. However, due to heterogeneity of data and application scenarios, it is challenging to scale usability of such ML analytics is challenging, namely saving time in developing new solutions and reusing already developed solutions.We address this challenge by relying on knowledge graphs (KG) that not only conveniently allow to integrate welding data, but also to serve as a basis for layering ML-based analytical applications, thus enabling quality monitoring of welding operations. In this work we focus on the construction of a KG for welding that is tailored towards further use for ML applications. Furthermore, we demonstrate how selected ML analytical tasks are supported by this KG.

• Chen, Jieying; Ma, Yue; Peñaloza, Rafael & Hui, Yang (2021). Union and Intersection of all Justifications (Extended Abstract).
• Koopmann, Patrick & Chen, Jieying (2021). Deductive Module Extraction for Expressive Description Logics. Vis sammendrag

  In deductive module extraction, we determine a small subset of an ontology for a given vocabulary that preserves all logical entailments that can be expressed in that vocabulary. While in the literature stronger module notions have been discussed, we argue that for applications in ontology analysis and ontology reuse, deductive modules, which are decidable and potentially smaller, are often sufficient. We present methods based on uniform interpolation for extracting different variants of deductive modules, satisfying properties such as completeness, minimality and robustness under replacements, the latter being particularly relevant for ontology reuse. An evaluation of our implementation shows that the modules computed by our method are often significantly smaller than those computed by existing methods.

• Koopmann, Patrick & Chen, Jieying (2020). Deductive Module Extraction for Expressive Description Logics (Extended Abstract).
• Chen, Jieying; Alghamdi, Ghadah; Schmidt, Renate; Walther, Dirk & Gao, Yongsheng (2019). Ontology Extraction for Large Ontologies via Modularity and Forgetting. Vis sammendrag

  We are interested in the computation of ontology extracts based on forgetting from large ontologies in real-world scenarios. Such scenarios require nearly all of the terms in the ontology to be forgotten, which poses a significant challenge to forgetting tools. In this paper we show that modularization and forgetting can be combined beneficially in order to compute ontology extracts. While a module is a subset of axioms of a given ontology, the solution of forgetting (also known as a uniform interpolant) is a compact representation of the ontology limited to a subset of the signature. The approach introduced in this paper uses an iterative workflow of four stages: (i)extension of the given signature and, if needed partitioning, (ii)modularization, (iii)forgetting, and (iv)evaluation by domain expert. For modularization we use three kinds of modules: locality-based, semantic and minimal subsumption modules. For forgetting three tools are used: NUI, LETHE and FAME. An evaluation on the SNOMED CT and NCIt ontologies for standard concept name lists showed that precomputing ontology modules reduces the number of terms that need to be forgotten. An advantage of the presented approach is high precision of the computed ontology extracts.

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