Dr. Michael Galkin

Dr. Michael Galkin left SDA. The profile below reflects the status at the point of his departure and is no longer updated.

Senior Research Scientist
Enterprise Information System
Fraunhofer IAIS

Profiles: LinkedIn, Google Scholar, DBLP

Room 4.33.3
Zwickauer Str. 46, 01069 Dresden
Fraunhofer IAIS

Short CV

Michael is a senior research scientist at the Dresden branch of Fraunhofer IAIS and SDA where we build conversational AI platforms based on knowledge graphs. He obtained a double-degree PhD with magna cum laude at the University of Bonn (Germany) and ITMO University (Russia) in 2018 studying knowledge graphs, their data integration strategies and query optimization routines. At Fraunhofer IAIS he participated in many successful industrial implementations of knowledge graphs for various use-cases. Currently, he focuses on incorporating background knowledge in its graph representation into dialogue assistants.

His research interests include graphs and networks representations, knowledge-driven dialogue systems, NLP as well as semantic data integration, graph databases, and artificial intelligence in general.

Research Interests

Knowledge Graphs
Conversational AI and Dialogue Systems
Semantic Data Management
Artificial Intelligence

Projects

SPEAKER
ScaDS.AI
Knowledge-Driven Dialogue System (KDDS)
Past: IASIS, BigDataOcean, LinDA

Publications

2022

Galkin, Mikhail; Denis, Etienne G.; Wu, Jiapeng; Hamilton, William L.

NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs Inproceedings

In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenReview.net, 2022.

Alivanistos, Dimitrios; Berrendorf, Max; Cochez, Michael; Galkin, Mikhail

Query Embedding on Hyper-Relational Knowledge Graphs Inproceedings

In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenReview.net, 2022.

Ali, Mehdi; Berrendorf, Max; Galkin, Mikhail; Thost, Veronika; Ma, Tengfei; Tresp, Volker; Lehmann, Jens

Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract) Inproceedings

In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pp. 5259–5263, ijcai.org, 2022.

Schneider, Phillip; Schopf, Tim; Vladika, Juraj; Galkin, Michael; Simperl, Elena; Matthes, Florian

A Decade of Knowledge Graphs in Natural Language Processing: A Survey Inproceedings

In: Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, AACL/IJCNLP 2022 - Volume 1: Long Papers, Online Only, November 20-23, 2022, pp. 601–614, Association for Computational Linguistics, 2022.

Ali, Mehdi; Berrendorf, Max; Hoyt, Charles Tapley; Vermue, Laurent; Galkin, Mikhail; Sharifzadeh, Sahand; Fischer, Asja; Tresp, Volker; Lehmann, Jens

Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework Journal Article

In: IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 12, pp. 8825–8845, 2022.

2021

Ali, Mehdi; Berrendorf, Max; Galkin, Mikhail; Thost, Veronika; Ma, Tengfei; Tresp, Volker; Lehmann, Jens

Improving Inductive Link Prediction Using Hyper-relational Facts Inproceedings

In: The Semantic Web - ISWC 2021 - 20th International Semantic Web Conference, ISWC 2021, Virtual Event, October 24-28, 2021, Proceedings, pp. 74–92, Springer, 2021.

2020

Khvalchik, Maria; Galkin, Mikhail

Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks Inproceedings

In: Proceedings of the Workshop on Hybrid Intelligence for Natural Language Processing Tasks (HI4NLP 2020) co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, August 29, 2020, pp. 29–33, CEUR-WS.org, 2020.

Galkin, Mikhail; Trivedi, Priyansh; Maheshwari, Gaurav; Usbeck, Ricardo; Lehmann, Jens

Message Passing for Hyper-Relational Knowledge Graphs Inproceedings

In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 7346–7359, Association for Computational Linguistics, 2020.

2019

Wiens, Vitalis; Galkin, Michael; Lohmann, Steffen; Auer, Sören

Demonstration of a Customizable Representation Model for Graph-Based Visualizations of Ontologies - GizMO Inproceedings

In: Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 26-30, 2019, pp. 225–228, CEUR-WS.org, 2019.

Tasnim, Mayesha; Collarana, Diego; Graux, Damien; Galkin, Mikhail; Vidal, Maria-Esther

COMET: A Contextualized Molecule-Based Matching Technique Inproceedings

In: Database and Expert Systems Applications - 30th International Conference, DEXA 2019, Linz, Austria, August 26-29, 2019, Proceedings, Part I, pp. 175–185, Springer, 2019.

2018

Al-Safi, Omar; Mader, Christian; Lytra, Ioanna; Galkin, Mikhail; Endris, Kemele M.; Vidal, Maria-Esther; Auer, Sören

Shipping Knowledge Graph Management Capabilities to Data Providers and Consumers Inproceedings

In: 12th IEEE International Conference on Semantic Computing, ICSC 2018, Laguna Hills, CA, USA, January 31 - February 2, 2018, pp. 9–16, IEEE Computer Society, 2018.

Galkin, Mikhail; Collarana, Diego; Vidal, Maria-Esther; Tasnim, Mayesha

Synthesizing a Knowledge Graph of Data Scientist Job Offers with MINTE+ Inproceedings

In: Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, October 8th - to - 12th, 2018, CEUR-WS.org, 2018.

Galkin, Mikhail

Strategies for Managing Linked Enterprise Data PhD Thesis

University of Bonn, 2018.

Abstract | Links | BibTeX

@phdthesis{GalkinThesis,

title = {Strategies for Managing Linked Enterprise Data},

author = {Mikhail Galkin},

url = {http://hdl.handle.net/20.500.11811/7856},

year  = {2018},

date = {2018-01-01},

school = {University of Bonn},

abstract = {Data, information and knowledge become key assets of our 21st century economy. As a result, data and knowledge management become key tasks with regard to sustainable development and business success. Often, knowledge is not explicitly represented residing in the minds of people or scattered among a variety of data sources. Knowledge is inherently associated with semantics that conveys its meaning to a human or machine agent. The Linked Data concept facilitates the semantic integration of heterogeneous data sources. However, we still lack an effective knowledge integration strategy applicable to enterprise scenarios, which balances between large amounts of data stored in legacy information systems and data lakes as well as tailored domain specific ontologies that formally describe real-world concepts. In this thesis we investigate strategies for managing linked enterprise data analyzing how actionable knowledge can be derived from enterprise data leveraging knowledge graphs. Actionable knowledge provides valuable insights, supports decision makers with clear interpretable arguments, and keeps its inference processes explainable. The benefits of employing actionable knowledge and its coherent management strategy span from a holistic semantic representation layer of enterprise data, i.e., representing numerous data sources as one, consistent, and integrated knowledge source, to unified interaction mechanisms with other systems that are able to effectively and efficiently leverage such an actionable knowledge. Several challenges have to be addressed on different conceptual levels pursuing this goal, i.e., means for representing knowledge, semantic data integration of raw data sources and subsequent knowledge extraction, communication interfaces, and implementation. In order to tackle those challenges we present the concept of Enterprise Knowledge Graphs (EKGs), describe their characteristics and advantages compared to existing approaches. We study each challenge with regard to using EKGs and demonstrate their efficiency. In particular, EKGs are able to reduce the semantic data integration effort when processing large-scale heterogeneous datasets. Then, having built a consistent logical integration layer with heterogeneity behind the scenes, EKGs unify query processing and enable effective communication interfaces for other enterprise systems. The achieved results allow us to conclude that strategies for managing linked enterprise data based on EKGs exhibit reasonable performance, comply with enterprise requirements, and ensure integrated data and knowledge management throughout its life cycle.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Data, information and knowledge become key assets of our 21st century economy. As a result, data and knowledge management become key tasks with regard to sustainable development and business success. Often, knowledge is not explicitly represented residing in the minds of people or scattered among a variety of data sources. Knowledge is inherently associated with semantics that conveys its meaning to a human or machine agent. The Linked Data concept facilitates the semantic integration of heterogeneous data sources. However, we still lack an effective knowledge integration strategy applicable to enterprise scenarios, which balances between large amounts of data stored in legacy information systems and data lakes as well as tailored domain specific ontologies that formally describe real-world concepts. In this thesis we investigate strategies for managing linked enterprise data analyzing how actionable knowledge can be derived from enterprise data leveraging knowledge graphs. Actionable knowledge provides valuable insights, supports decision makers with clear interpretable arguments, and keeps its inference processes explainable. The benefits of employing actionable knowledge and its coherent management strategy span from a holistic semantic representation layer of enterprise data, i.e., representing numerous data sources as one, consistent, and integrated knowledge source, to unified interaction mechanisms with other systems that are able to effectively and efficiently leverage such an actionable knowledge. Several challenges have to be addressed on different conceptual levels pursuing this goal, i.e., means for representing knowledge, semantic data integration of raw data sources and subsequent knowledge extraction, communication interfaces, and implementation. In order to tackle those challenges we present the concept of Enterprise Knowledge Graphs (EKGs), describe their characteristics and advantages compared to existing approaches. We study each challenge with regard to using EKGs and demonstrate their efficiency. In particular, EKGs are able to reduce the semantic data integration effort when processing large-scale heterogeneous datasets. Then, having built a consistent logical integration layer with heterogeneity behind the scenes, EKGs unify query processing and enable effective communication interfaces for other enterprise systems. The achieved results allow us to conclude that strategies for managing linked enterprise data based on EKGs exhibit reasonable performance, comply with enterprise requirements, and ensure integrated data and knowledge management throughout its life cycle.

Collarana, Diego; Galkin, Mikhail; Lange, Christoph; Scerri, Simon; Auer, Sören; Vidal, Maria-Esther

Synthesizing Knowledge Graphs from Web Sources with the MINTE ^+ + Framework Inproceedings

In: The Semantic Web - ISWC 2018 - 17th International Semantic Web Conference, Monterey, CA, USA, October 8-12, 2018, Proceedings, Part II, pp. 359–375, Springer, 2018.

Endris, Kemele M.; Galkin, Mikhail; Lytra, Ioanna; Mami, Mohamed Nadjib; Vidal, Maria-Esther; Auer, Sören

Querying Interlinked Data by Bridging RDF Molecule Templates Journal Article

In: Trans. Large Scale Data Knowl. Centered Syst., vol. 39, pp. 1–42, 2018.

2017

Galkin, Mikhail; Vidal, Maria-Esther; Auer, Sören

Towards a Multi-way Similarity Join Operator Inproceedings

In: New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops, AMSD, BigNovelTI, DAS, SW4CH, DC, Nicosia, Cyprus, September 24-27, 2017, Proceedings, pp. 267–274, Springer, 2017.

Endris, Kemele M.; Galkin, Mikhail; Lytra, Ioanna; Mami, Mohamed Nadjib; Vidal, Maria-Esther; Auer, Sören

MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates Inproceedings

In: Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Lyon, France, August 28-31, 2017, Proceedings, Part I, pp. 3–18, Springer, 2017.

Galkin, Mikhail; Collarana, Diego; Ribón, Ignacio Traverso; Vidal, Maria-Esther; Auer, Sören

SJoin: A Semantic Join Operator to Integrate Heterogeneous RDF Graphs Inproceedings

In: Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Lyon, France, August 28-31, 2017, Proceedings, Part I, pp. 206–221, Springer, 2017.

Galkin, Mikhail; Endris, Kemele M.; Acosta, Maribel; Collarana, Diego; Vidal, Maria-Esther; Auer, Sören

SMJoin: A Multi-way Join Operator for SPARQL Queries Inproceedings

In: Proceedings of the 13th International Conference on Semantic Systems, SEMANTiCS 2017, Amsterdam, The Netherlands, September 11-14, 2017, pp. 104–111, ACM, 2017.

Galkin, Mikhail; Auer, Sören; Vidal, Maria-Esther; Scerri, Simon

Enterprise Knowledge Graphs: A Semantic Approach for Knowledge Management in the Next Generation of Enterprise Information Systems Inproceedings

In: ICEIS 2017 - Proceedings of the 19th International Conference on Enterprise Information Systems, Volume 2, Porto, Portugal, April 26-29, 2017, pp. 88–98, SciTePress, 2017.

Collarana, Diego; Galkin, Mikhail; Ribón, Ignacio Traverso; Lange, Christoph; Vidal, Maria-Esther; Auer, Sören

Semantic Data Integration for Knowledge Graph Construction at Query Time Inproceedings

In: 11th IEEE International Conference on Semantic Computing, ICSC 2017, San Diego, CA, USA, January 30 - February 1, 2017, pp. 109–116, IEEE Computer Society, 2017.

Similea, Alexandra; Petersen, Niklas; Lange, Christoph; Lohmann, Steffen; Galkin, Michael

TurtleEditor 2.0: A Synchronized Graphical and Text Editor for RDF Vocabularies Inproceedings

In: 2017 IEEE Tenth International Conference on Semantic Computing (ICSC, IEEE Computer Society, 2017.

Galkin, Mikhail; Vidal, Maria-Esther

BatWAn: A Binary and Multi-Way Query Plan Analyzer Inproceedings

In: Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks co-located with 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, October 23rd - to - 25th, 2017, CEUR-WS.org, 2017.

Collarana, Diego; Galkin, Mikhail; Ribón, Ignacio Traverso; Vidal, Maria-Esther; Lange, Christoph; Auer, Sören

MINTE: semantically integrating RDF graphs Inproceedings

In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, WIMS 2017, Amantea, Italy, June 19-22, 2017, pp. 22:1–22:11, ACM, 2017.

2016

Petersen, Niklas; Galkin, Michael; Lange, Christoph; Lohmann, Steffen; Auer, Sören

Monitoring and Automating Factories Using Semantic Models Inproceedings

In: Semantic Technology - 6th Joint International Conference, JIST 2016, Singapore, Singapore, November 2-4, 2016, Revised Selected Papers, pp. 315–330, Springer, 2016.

Collarana, Diego; Galkin, Mikhail; Lange, Christoph; Grangel-González, Irlán; Vidal, Maria-Esther; Auer, Sören

FuhSen: A Federated Hybrid Search Engine for Building a Knowledge Graph On-Demand (Short Paper) Inproceedings

In: On the Move to Meaningful Internet Systems: OTM 2016 Conferences - Confederated International Conferences: CoopIS, C&TC, and ODBASE 2016, Rhodes, Greece, October 24-28, 2016, Proceedings, pp. 752–761, 2016.

Galkin, Mikhail; Auer, Sören; Kim, Hak Lae; Scerri, Simon

Integration Strategies for Enterprise Knowledge Graphs Inproceedings

In: Tenth IEEE International Conference on Semantic Computing, ICSC 2016, Laguna Hills, CA, USA, February 4-6, 2016, pp. 242–245, IEEE Computer Society, 2016.

Galkin, Mikhail; Auer, Sören; Scerri, Simon

Enterprise Knowledge Graphs: A Backbone of Linked Enterprise Data Inproceedings

In: 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016, Omaha, NE, USA, October 13-16, 2016, pp. 497–502, IEEE Computer Society, 2016.

2015

Galkin, Mikhail; Mouromtsev, Dmitry; Auer, Sören

Identifying Web Tables: Supporting a Neglected Type of Content on the Web Inproceedings

In: Knowledge Engineering and Semantic Web - 6th International Conference, KESW 2015, Moscow, Russia, September 30 - October 2, 2015, Proceedings, pp. 48–62, Springer, 2015.

Mouromtsev, Dmitry; Pavlov, Dmitry S.; Emelyanov, Yury; Morozov, Alexey V.; Razdyakonov, Daniil S.; Galkin, Mikhail

The Simple Web-based Tool for Visualization and Sharing of Semantic Data and Ontologies Inproceedings

In: Proceedings of the ISWC 2015 Posters & Demonstrations Track co-located with the 14th International Semantic Web Conference (ISWC-2015), Bethlehem, PA, USA, October 11, 2015, CEUR-WS.org, 2015.

Thellmann, Klaudia; Galkin, Michael; Orlandi, Fabrizio; Auer, Sören

LinkDaViz - Automatic Binding of Linked Data to Visualizations Inproceedings

In: The Semantic Web - ISWC 2015 - 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part I, pp. 147–162, Springer, 2015.

2013

Mouromtsev, Dmitry; Vlasov, Vitaly; Parkhimovich, Olga; Galkin, Mikhail; Knyazev, Vitaly

Development of the St. Petersburg's linked open data site using Information Workbench Inproceedings

In: 14th Conference of Open Innovations Association FRUCT, Espoo, Finland, November 11-15, 2013, pp. 77–82, IEEE, 2013.