Dr. Harsh Thakkar

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 The profile below reflects the status at the point of his departure and is no longer updated.

Associated Researcher
OSTHUS GmbH

Profiles: LinkedIn, Google Scholar, DBLP, Website

harsh9t@gmail.com

Short CV

I am a subject matter expert in Semantics, Knowledge Graphs, and Graph Data Management, currently working as a consultant in large-scale Life Science projects with OSTHUS GmbH. I help organisations solve complex real-world data-centric and data-driven problems in order to realise their full potential by using a combination of Semantic Web, Graph Technologies, and Team leading skills.

Check my website for more information.

Research Interests

Semantics
Knowledge Graphs
Graph Data Management

Projects

WDAqua

Publications

2021

Kacupaj, Endri; Plepi, Joan; Singh, Kuldeep; Thakkar, Harsh; Lehmann, Jens; Maleshkova, Maria

Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks Inproceedings

In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021, pp. 850–862, Association for Computational Linguistics, 2021.

Plepi, Joan; Kacupaj, Endri; Singh, Kuldeep; Thakkar, Harsh; Lehmann, Jens

Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs Inproceedings

In: The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings, pp. 356–371, Springer, 2021.

Thakker, Harsh Vrajeshkumar

On Supporting Interoperability between RDF and Property Graph Databases PhD Thesis

University of Bonn, Germany, 2021.

Abstract | Links | BibTeX

@phdthesis{DBLP:phd/dnb/Thakker21,

title = {On Supporting Interoperability between RDF and Property Graph Databases},

author = {Harsh Vrajeshkumar Thakker},

url = {https://hdl.handle.net/20.500.11811/9083},

year = {2021},

date = {2021-01-01},

school = {University of Bonn, Germany},

abstract = {Over the last few years, the amount and availability of machine-readable Open, Linked, and Big data on the web has increased. Simultaneously, several data management systems have emerged to deal with the increased amounts of this structured data. RDF and Graph databases are two popular approaches for data management based on modeling, storing, and querying graph-like data. RDF database systems are based on the W3C standard RDF data model and use the W3C standard SPARQL as their defacto query language. Most graph database systems are based on the Property Graph (PG) data model and use the Gremlin language as their query language due to its popularity amongst vendors. Given that both of these approaches have distinct and complementary characteristics – RDF is suited for distributed data integration with built-in world-wide unique identifiers and vocabularies; PGs, on the other hand, support horizontally scalable storage and querying, and are widely used for modern data analytics applications, – it becomes necessary to support interoperability amongst them. The main objective of this dissertation is to study and address this interoperability issue. We identified three research challenges that are concerned with the data interoperability, query interoperability, and benchmarking of these databases. First, we tackle the data interoperability problem. We propose three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database. We show that the proposed mappings satisfy the desired properties of semantics preservation and information preservation. Based on our analysis (both formal and empirical), we argue that any RDF database can be transformed into a PG database using our approach. Second, we propose a novel approach for querying PG databases using SPARQL using Gremlin traversals – GREMLINATOR to tackle the query interoperability problem. In doing so, we first formalize the declarative constructs of Gremlin language using a consolidated graph relational algebra and define mappings to translate SPARQL queries into Gremlin traversals. GREMLINATOR has been officially integrated as a plugin for the Apache TinkerPop graph computing framework (as sparql-gremlin), which enables users to execute SPARQL queries over a wide variety of OLTP graph databases and OLAP graph processing frameworks. Finally, we tackle the third, benchmarking (performance evaluation), problem. We propose a novel framework – LITMUS Benchmark Suite that allows a choke-point driven performance comparison and analysis of various databases (PG and RDF-based) using various third-party real and synthetic datasets and queries. We also studied a variety of intrinsic and extrinsic factors – data and system-specific metrics and Key Performance Indicators (KPIs) that influence a given system’s performance. LITMUS incorporates various memory, processor, data quality, indexing, query typology, and data-based metrics for providing a fine-grained evaluation of the benchmark. In conclusion, by filling the research gaps, addressed by this dissertation, we have laid a solid formal and practical foundation for supporting interoperability between the RDF and Property graph database technology stacks. The artifacts produced during the term of this dissertation have been integrated into various academic and industrial projects.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Over the last few years, the amount and availability of machine-readable Open, Linked, and Big data on the web has increased. Simultaneously, several data management systems have emerged to deal with the increased amounts of this structured data. RDF and Graph databases are two popular approaches for data management based on modeling, storing, and querying graph-like data. RDF database systems are based on the W3C standard RDF data model and use the W3C standard SPARQL as their defacto query language. Most graph database systems are based on the Property Graph (PG) data model and use the Gremlin language as their query language due to its popularity amongst vendors. Given that both of these approaches have distinct and complementary characteristics – RDF is suited for distributed data integration with built-in world-wide unique identifiers and vocabularies; PGs, on the other hand, support horizontally scalable storage and querying, and are widely used for modern data analytics applications, – it becomes necessary to support interoperability amongst them. The main objective of this dissertation is to study and address this interoperability issue. We identified three research challenges that are concerned with the data interoperability, query interoperability, and benchmarking of these databases. First, we tackle the data interoperability problem. We propose three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database. We show that the proposed mappings satisfy the desired properties of semantics preservation and information preservation. Based on our analysis (both formal and empirical), we argue that any RDF database can be transformed into a PG database using our approach. Second, we propose a novel approach for querying PG databases using SPARQL using Gremlin traversals – GREMLINATOR to tackle the query interoperability problem. In doing so, we first formalize the declarative constructs of Gremlin language using a consolidated graph relational algebra and define mappings to translate SPARQL queries into Gremlin traversals. GREMLINATOR has been officially integrated as a plugin for the Apache TinkerPop graph computing framework (as sparql-gremlin), which enables users to execute SPARQL queries over a wide variety of OLTP graph databases and OLAP graph processing frameworks. Finally, we tackle the third, benchmarking (performance evaluation), problem. We propose a novel framework – LITMUS Benchmark Suite that allows a choke-point driven performance comparison and analysis of various databases (PG and RDF-based) using various third-party real and synthetic datasets and queries. We also studied a variety of intrinsic and extrinsic factors – data and system-specific metrics and Key Performance Indicators (KPIs) that influence a given system’s performance. LITMUS incorporates various memory, processor, data quality, indexing, query typology, and data-based metrics for providing a fine-grained evaluation of the benchmark. In conclusion, by filling the research gaps, addressed by this dissertation, we have laid a solid formal and practical foundation for supporting interoperability between the RDF and Property graph database technology stacks. The artifacts produced during the term of this dissertation have been integrated into various academic and industrial projects.

2020

Thakkar, Harsh; Angles, Renzo; Rodriguez, Marko; Mallette, Stephen; Lehmann, Jens

Let's build Bridges, not Walls: SPARQL Querying of TinkerPop Graph Databases with Sparql-Gremlin Inproceedings

In: IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020, pp. 408–415, IEEE, 2020.

Angles, Renzo; Thakkar, Harsh; Tomaszuk, Dominik

Mapping RDF Databases to Property Graph Databases Journal Article

In: IEEE Access, vol. 8, pp. 86091–86110, 2020.

Tomaszuk, Dominik; Angles, Renzo; Thakkar, Harsh

PGO: Describing Property Graphs in RDF Journal Article

In: IEEE Access, vol. 8, pp. 118355–118369, 2020.

2019

Angles, Renzo; Thakkar, Harsh; Tomaszuk, Dominik

RDF and Property Graphs Interoperability: Status and Issues Inproceedings

In: Proceedings of the 13th Alberto Mendelzon International Workshop on Foundations of Data Management, Asunción, Paraguay, June 3-7, 2019, CEUR-WS.org, 2019.

Nguyen, Vinh; Yip, Hong Yung; Thakkar, Harsh; Li, Qingliang; Bolton, Evan; Bodenreider, Olivier

Singleton Property Graph: Adding A Semantic Web Abstraction Layer to Graph Databases Inproceedings

In: Proceedings of the Blockchain enabled Semantic Web Workshop (BlockSW) and Contextualized Knowledge Graphs (CKG) Workshop co-located with the 18th International Semantic Web Conference, BlockSW/CKG@ISWC 2019, Auckland, New Zealand, October 27, 2019, CEUR-WS.org, 2019.

Thakkar, Harsh; Auer, Sören; Vidal, Maria-Esther

Formalizing Gremlin Pattern Matching Traversals in an Integrated Graph Algebra Inproceedings

In: Proceedings of the Blockchain enabled Semantic Web Workshop (BlockSW) and Contextualized Knowledge Graphs (CKG) Workshop co-located with the 18th International Semantic Web Conference, BlockSW/CKG@ISWC 2019, Auckland, New Zealand, October 27, 2019, CEUR-WS.org, 2019.

2018

Thakkar, Harsh; Punjani, Dharmen; Lehmann, Jens; Auer, Sören

Two for one: querying property graph databases using SPARQL via gremlinator Inproceedings

In: Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), Houston, TX, USA, June 10, 2018, pp. 12:1–12:5, ACM, 2018.

Quécole, Felipe; ao Martines, Rom; Giménez-Garc'ia, José M.; Thakkar, Harsh

Towards Capturing Contextual Semantic Information About Statements in Web Tables Inproceedings

In: Joint Proceedings of the International Workshops on Contextualized Knowledge Graphs, and Semantic Statistics co-located with 17th International Semantic Web Conference (ISWC 2018), CEUR-WS.org, 2018.

2017

Singh, Kuldeep; Lytra, Ioanna; Vidal, Maria-Esther; Punjani, Dharmen; Thakkar, Harsh; Lange, Christoph; Auer, Sören

QAestro - Semantic-Based Composition of Question Answering Pipelines Inproceedings

In: Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Lyon, France, August 28-31, 2017, Proceedings, Part I, pp. 19–34, Springer, 2017.

Thakkar, Harsh; Punjani, Dharmen; Auer, Sören; Vidal, Maria-Esther

Towards an Integrated Graph Algebra for Graph Pattern Matching with Gremlin Inproceedings

In: Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Lyon, France, August 28-31, 2017, Proceedings, Part I, pp. 81–91, Springer, 2017.

Thakkar, Harsh

Towards an Open Extensible Framework for Empirical Benchmarking of Data Management Solutions: LITMUS Inproceedings

In: The Semantic Web - 14th International Conference, ESWC 2017, Portorov z, Slovenia, May 28 - June 1, 2017, Proceedings, Part II, pp. 256–266, 2017.

Thakkar, Harsh; Keswani, Yashwant; Dubey, Mohnish; Lehmann, Jens; Auer, Sören

Trying Not to Die Benchmarking: Orchestrating RDF and Graph Data Management Solution Benchmarks Using LITMUS Inproceedings

In: Proceedings of the 13th International Conference on Semantic Systems, SEMANTiCS 2017, Amsterdam, The Netherlands, September 11-14, 2017, pp. 120–127, ACM, 2017.

Endris, Kemele M.; Giménez-Garc'ia, José M.; Thakkar, Harsh; Demidova, Elena; Zimmermann, Antoine; Lange, Christoph; Simperl, Elena

Dataset Reuse: An Analysis of References in Community Discussions, Publications and Data Inproceedings

In: Proceedings of the Knowledge Capture Conference, K-CAP 2017, Austin, TX, USA, December 4-6, 2017, pp. 5:1–5:4, ACM, 2017.

2016

Giménez-Garc'ia, José M.; Thakkar, Harsh; Zimmermann, Antoine

Assessing Trust with PageRank in the Web of Data Inproceedings

In: The Semantic Web - ESWC 2016 Satellite Events, Heraklion, Crete, Greece, May 29 - June 2, 2016, Revised Selected Papers, pp. 293–307, CEUR-WS.org, 2016.

Thakkar, Harsh; Endris, Kemele M.; Giménez-Garc'ia, José M.; Debattista, Jeremy; Lange, Christoph; Auer, Sören

Are Linked Datasets fit for Open-domain Question Answering? A Quality Assessment Inproceedings

In: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016, N^imes, France, June 13-15, 2016, pp. 19:1–19:12, ACM, 2016.

Shekarpour, Saeedeh; Endris, Kemele M.; Kumar, Ashwini Jaya; Lukovnikov, Denis; Singh, Kuldeep; Thakkar, Harsh; Lange, Christoph

Question Answering on Linked Data: Challenges and Future Directions Inproceedings

In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11-15, 2016, Companion Volume, pp. 693–698, ACM, 2016.

2014

Thakkar, Harsh; Iyer, Ganesh; Shah, Kesha; Majumder, Prasenjit

Team IRLabDAIICT at ShARe/CLEF eHealth 2014 Task 3: User-centered Information Retrieval System for Clinical Documents Inproceedings

In: Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014, pp. 248–259, CEUR-WS.org, 2014.

2012

Gurubani, Jitendra B.; Thakkar, Harsh; Patel, Dhiren R.

Improvements over Extended LMAP+: RFID Authentication Protocol Inproceedings

In: Trust Management VI - 6th IFIP WG 11.11 International Conference, IFIPTM 2012, Surat, India, May 21-25, 2012. Proceedings, pp. 225–231, Springer, 2012.