TY - CHAP AU - Ma, Chuangtao AU - Molnár, Bálint AU - Tarcsi, Ádám AU - Benczúr, András, id ED - Fazekas, István TI - Knowledge Enriched Schema Matching Framework for Heterogeneous Data Integration T2 - 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9781665496520 PY - 2022 SP - 183 EP - 188 PG - 6 DO - 10.1109/CITDS54976.2022.9914350 UR - https://m2.mtmt.hu/api/publication/33191477 ID - 33191477 LA - English DB - MTMT ER - TY - JOUR AU - Ma, Chuangtao AU - Molnár, Bálint TI - Ontology Learning from Relational Database: Opportunities for Semantic Information Integration JF - VIETNAM JOURNAL OF COMPUTER SCIENCE J2 - VIETNAM J COMP SCI VL - 9 PY - 2022 IS - 1 SP - 31 EP - 57 PG - 27 SN - 2196-8888 DO - 10.1142/S219688882150024X UR - https://m2.mtmt.hu/api/publication/31788843 ID - 31788843 N1 - C. Ma and B. 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Ontology learning from RDB is a new paradigm to (semi- )automatically construct ontologies from RDB by borrowing the techniques of machine learning, it provides a potential opportunities for integrating heterogeneous data from various data sources efficiently. This paper surveys the recent methods and tools of the ontology learning from RDB, and highlights the potential opportunities and challenges of using ontology learning in semantic information integration. Initially, the previous surveys on the topic of the ontology-based integration and ontology learning were sum- marized, and then the limitations of previous surveys were identified and analyzed. Fur- thermore, the methods and techniques of ontology learning from RDB were investigated by classifying into three categories: reverse engineering, mapping, and machine learning. Accordingly, the opportunities and possibility of using ontology learning from RDB in semantic information integration were discussed based on the mapping results between the bottlenecks of ontology-based integration and the features of ontology learning LA - English DB - MTMT ER - TY - JOUR AU - Ma, Chuangtao AU - Molnár, Bálint AU - Benczúr, András, id TI - A Semi-automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database JF - INFORMATION (BASEL) J2 - INFORMATION-BASEL VL - 12 PY - 2021 IS - 5 PG - 17 SN - 2078-2489 DO - 10.3390/info12050188 UR - https://m2.mtmt.hu/api/publication/31989908 ID - 31989908 LA - English DB - MTMT ER - TY - GEN ED - Molnár, Bálint ED - Tarcsi, Ádám AU - Ma, Chuangtao TI - Knowledge-enriched Schema Mapping: A Preliminary Case Study of e-MedSolution System PY - 2020 UR - https://m2.mtmt.hu/api/publication/31830781 ID - 31830781 LA - English DB - MTMT ER - TY - CONF AU - Ma, Chuangtao AU - Molnár, Bálint ED - Horváth, Zoltán ED - Adrian, Petruşel TI - Semantic Consistency behind Ontology Learning and Schema Mapping for Heterogeneous Data Integration T2 - Collection of Abstracts PB - Babes-Bolyai Tudományegyetem C1 - Budapest PY - 2020 SP - 115 EP - 115 PG - 1 UR - https://m2.mtmt.hu/api/publication/31814813 ID - 31814813 N1 - Import hibák 2021-01-17 11:37 Category cannot be determined for the following item: ABST, default value is set. AB - Ontology-based data integration (OBDA) plays a critical role in heterogeneous data integration, due to the excellent semantic interoperability and rigidly mathematical foundations of ontologies. However, the traditional methods for constructing ontology are manual, in which a lot of effort and experience from domain experts are required. Accordingly, ontology learning (OL) was proposed to (semi-)automatically construct ontologies, in which entities are usually extracted and mapped into the lower-dimensional vector space, thereby, the relations could be learned based on the computation and inference. The techniques of ontology learning are classified into four categories: association rule mining (ARM), formal concept analysis(FCA), inductive logic programming (ILP), neural networks(NN) and machine learning. However, the semantic collision will inevitably occur while learning and constructing ontologies from multi-source and heterogenous database (semi-)automatically. Consequently, it will induce the mismatching and inconsistency while mapping between source schema and target schema. To address the above issues, the semantic consistency behind ontology learning and schema mapping are investigated based on the formal representation language. Considering the semantic compatibility of OWL DL (description logic ontologies) and excellent expressivity of formal representation language, description logics(DLs) and W-graph are employed to formalize the process of ontology learning for (semi-)automatically constructing OWL DL. Furthermore, SOtags (Second-Order tuple-generating dependency tags) and database mapping graph are utilized to formalize the schema mapping for providing data-exchange between source schema and target schema, in which the semantics behind schema mapping is analyzed by introducing information fluxes. Moreover, the semantic consistency behind ontology learning and schema mapping are checked by using the model checking and ontology alignment. Besides, an example is presented to demonstrate the specific process of formalizing and checking the semantic consistency behind ontology learning and schema mapping for heterogeneous data integration. LA - English DB - MTMT ER - TY - CHAP AU - Ma, Chuangtao AU - Molnár, Bálint ED - Srinilta, Chutimet ED - Krótkiewicz, Marek ED - Pietranik, Marcin ED - Sitek, Paweł TI - Use of Ontology Learning in Information System Integration: A Literature Survey T2 - Intelligent Information and Database Systems PB - Springer-Verlag Singapore CY - Singapore SN - 9789811533792 T3 - Communications in Computer and Information Science, ISSN 1865-0929 ; 1178. PY - 2020 SP - 342 EP - 353 PG - 12 DO - 10.1007/978-981-15-3380-8_30 UR - https://m2.mtmt.hu/api/publication/31206561 ID - 31206561 AB - Ontology-based information integration is a useful method to integrate heterogeneous data at the semantic level. However, there are some bottlenecks of the traditional method for constructing ontology, i.e., time-consuming, error-prone, and semantic loss. Ontology learning is a kind of ontology construction approach based on machine learning, it provides a new opportunity to tackle the above bottlenecks. Especially, it could be employed to construct ontologies and integrate large-scale and heterogeneous data from various information systems. This paper surveys the latest developments of ontology learning and highlights how they could be adopted and play a vital role in the integration of information systems. The recent techniques and tools of ontology learning from text and relational database are reviewed, the possibility of using ontology learning in information integration were discussed based on the mapping results of the aforementioned bottlenecks and features of ontology learning. The potential directions for using ontology learning in information systems integration were given. LA - English DB - MTMT ER - TY - CHAP AU - Ma, Chuangtao ED - Kamišalić Latifić, Aida ED - Darmont, Jérôme ED - Tzouramanis, Theodoros ED - Morzy, Mikoƚaj ED - Gamper, Johann ED - Ivanović, Mirjana ED - Wrembel, Robert ED - Podgorelec, Vili ED - Eder, Johann ED - Welzer, Tatjana TI - Data Integration of Legacy ERP System Based on Ontology Learning from SQL Scripts T2 - New Trends in Databases and Information Systems PB - Springer Netherlands CY - Cham SN - 9783030302788 T3 - Communications in Computer and Information Science, ISSN 1865-0929 ; 1064. PY - 2019 SP - 546 EP - 551 PG - 6 DO - 10.1007/978-3-030-30278-8_52 UR - https://m2.mtmt.hu/api/publication/30787772 ID - 30787772 AB - To tackle the problem of low-efficiency integration of heterogeneous data from various legacy ERP systems, a data integration approach based on ontology learning are presented. Considering the unavailability of database interface and diversity of DBMS and naming conventions of legacy information systems, a data integration framework for legacy ERP systems based on ontology learning from structured query language (SQL) scripts (RDB) are proposed. The key steps and technicality of the proposed framework and the process of ontology-based semantic integration are depicted. LA - English DB - MTMT ER - TY - CHAP AU - Ma, Chuangtao AU - Molnár, Bálint TI - A Legacy ERP System Integration Framework based on Ontology Learning T2 - Proceedings of the 21st International Conference on Enterprise Information Systems PB - INSTICC Press​ CY - Heraklion SN - 9789897583728 PY - 2019 SP - 231 EP - 237 PG - 7 DO - 10.5220/0007740602310237 UR - https://m2.mtmt.hu/api/publication/30679504 ID - 30679504 AB - In the past decades, there are various legacy ERP systems that exist in different departments or suborganizations within the enterprise. The majority of the legacy ERP systems are heterogeneous systems, that may be developed by different software companies under different development framework, which create a big challenge for organizations to develop and implement centralized and integrated management systems based on their existing legacy ERP systems to respond the dynamic business environment with agility. Ontologies are viewed as an effective technology to integrate different data from multiple heterogeneous sources, the ontology learning methods were proposed to achieve (semi-)automated construction of ontologies. This paper proposes a general framework for legacy ERP system integration based on ontology learning to tackle this challenge. Initially, the related literature is reviewed from the perspective of system integration and ontology learning, then an integration framework based on ontology learning is given, and the basic workflow and ontology learning process are analysed and illustrated. LA - English DB - MTMT ER -