Dr. Mohnish Dubey – Smart Data Analytics

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 The profile below reflects the status at the point of his departure and is no longer updated.

Senior Researcher at Smart Data Analytics,
Work Package Leader at InfAI Dresden, Germany.

Profiles: LinkedIn and Google Scholar
Personal Webpage: https://dubeymohnish.github.io

Short CV

Mohnish Dubey is a Senior Researcher at Smart Data Analytics and InfAI. Currently, Mohnish is leading the e-VITA project at InfAI, working towards designing a knowledge graph-based dialogue system to support active and healthy ageing.

Previously, he worked as a Dialogue Engineer and Researcher at Fraunhofer IAIS for more than three years (2017 October – 2021 May).

In 2021, Mohnish completed his PhD at the University of Bonn, Germany, titled “Towards Complex Question Answering over Knowledge Graphs“.

Research Interests

Question Answering
Natural Language Processing
Semantic Web
Machine Learning
Knowledge Graph

Demos and Website

LC-QuAD dataset website
Check our EARL api and demo

Publications

2021

Dubey, Mohnish

Towards Complex Question Answering over Knowledge Graphs PhD Thesis

University of Bonn, Germany, 2021.

Abstract | Links | BibTeX

@phdthesis{DBLP:phd/dnb/Dubey21,

title = {Towards Complex Question Answering over Knowledge Graphs},

author = {Mohnish Dubey},

url = {https://hdl.handle.net/20.500.11811/9122},

year = {2021},

date = {2021-01-19},

school = {University of Bonn, Germany},

abstract = {Over the past decade, Knowledge Graphs (KG) have emerged as a prominent repository for storing facts about the world in a linked data architecture. Providing machines with the capability of exploring such Knowledge Graphs and answering natural language questions over them, has been an active area of research. The purpose of this work, is to delve further into the research of retrieving information stored in KGs, based on the natural language questions posed by the user. Knowledge Graph Question Answering (KGQA) aims to produce a concise answer to a user question, such that the user is exempt from using KG vocabulary and overheads of learning a formal query language. Existing KGQA systems have achieved excellent results over Simple Questions, where the information required is limited to a single triple and a single formal query pattern. Our motivation is to improve the performance of KGQA over Complex Questions, where formal query patterns significantly vary, and a single triple is not confining for all the required information. Complex KGQA provides several challenges such as understanding semantics and syntactic structure of questions, Entity Linking, Relation Linking and Answer Representation. Lack of suitable datasets for complex question answering further adds to research gaps. Hence, in this thesis, we focus the research objective of laying the foundations for the advancement of the state-of-the-art for Complex Question Answering over Knowledge Graphs, by providing techniques to solve various challenges and provide resources to fill the research gaps.

First, we propose Normalized Query Structure (NQS), which is a linguistic analyzer module that helps the QA system to detect inputs and intents and the relation between them in the users’ question. NQS acts like an intermediate language between natural language questions and formal expressions to ease the process of query formulation for complex questions. We then developed a framework named LC-QuAD to generate large scale question answering dataset by reversing the process of question answering, thereby translating natural language questions from the formal query using intermediate templates. Our goal is to use this framework for high variations in the query patterns and create a large size dataset with minimum human effort. The first version of the dataset consists of 5,000 complex questions. By extending the LC-QuAD framework to support Reified KGs and crowd-sourcing, we published the second version of the dataset as LC-QuAD 2.0, consisting of 30,000 questions with their paraphrases and has higher complexity and new variations in the questions. To overcome the problem of Entity Linking and Relation Linking in KGQA, we develop EARL, a module performing these two tasks as a single joint task for complex question answering. We develop approaches for this module, first by formalizing the task as an instance of the Generalized Traveling Salesman Problem (GTSP) and the second approach uses machine learning to exploit the connection density between nodes in the Knowledge Graph. Lastly, we create another large scale dataset to answer verbalization and provide results for multiple baseline systems on it. The Verbalization dataset is introduced to make the system’s response more human-like. The NQS based KGQA system was next to the best system in terms of accuracy on the QALD-5 dataset. We empirically prove that NQS is robust to tackle paraphrases of the questions. EARL achieves the state of the art results in Entity Linking and Relation Linking for question answering on several KGQA datasets. The dataset curated in this thesis has helped the research community to move forward in the direction of improving the accuracy of complex question answering as a task as other researchers too developed several KGQA systems and modules around these published datasets. With the large-scale datasets, we have encouraged the use of large scale machine learning, deep learning and emergence of new techniques to advance the state-of-the-art in complex question answering over knowledge graphs. We further developed core components for the KGQA pipeline to overcome the challenges of Question Understanding, Entity-Relation Linking and Answer Verbalization and thus achieve our research objective. All the proposed approaches mentioned in this thesis and the published resources are available at https://github.com/AskNowQA and are released under the umbrella project AskNow.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Over the past decade, Knowledge Graphs (KG) have emerged as a prominent repository for storing facts about the world in a linked data architecture. Providing machines with the capability of exploring such Knowledge Graphs and answering natural language questions over them, has been an active area of research. The purpose of this work, is to delve further into the research of retrieving information stored in KGs, based on the natural language questions posed by the user. Knowledge Graph Question Answering (KGQA) aims to produce a concise answer to a user question, such that the user is exempt from using KG vocabulary and overheads of learning a formal query language. Existing KGQA systems have achieved excellent results over Simple Questions, where the information required is limited to a single triple and a single formal query pattern. Our motivation is to improve the performance of KGQA over Complex Questions, where formal query patterns significantly vary, and a single triple is not confining for all the required information. Complex KGQA provides several challenges such as understanding semantics and syntactic structure of questions, Entity Linking, Relation Linking and Answer Representation. Lack of suitable datasets for complex question answering further adds to research gaps. Hence, in this thesis, we focus the research objective of laying the foundations for the advancement of the state-of-the-art for Complex Question Answering over Knowledge Graphs, by providing techniques to solve various challenges and provide resources to fill the research gaps.
First, we propose Normalized Query Structure (NQS), which is a linguistic analyzer module that helps the QA system to detect inputs and intents and the relation between them in the users’ question. NQS acts like an intermediate language between natural language questions and formal expressions to ease the process of query formulation for complex questions. We then developed a framework named LC-QuAD to generate large scale question answering dataset by reversing the process of question answering, thereby translating natural language questions from the formal query using intermediate templates. Our goal is to use this framework for high variations in the query patterns and create a large size dataset with minimum human effort. The first version of the dataset consists of 5,000 complex questions. By extending the LC-QuAD framework to support Reified KGs and crowd-sourcing, we published the second version of the dataset as LC-QuAD 2.0, consisting of 30,000 questions with their paraphrases and has higher complexity and new variations in the questions. To overcome the problem of Entity Linking and Relation Linking in KGQA, we develop EARL, a module performing these two tasks as a single joint task for complex question answering. We develop approaches for this module, first by formalizing the task as an instance of the Generalized Traveling Salesman Problem (GTSP) and the second approach uses machine learning to exploit the connection density between nodes in the Knowledge Graph. Lastly, we create another large scale dataset to answer verbalization and provide results for multiple baseline systems on it. The Verbalization dataset is introduced to make the system’s response more human-like. The NQS based KGQA system was next to the best system in terms of accuracy on the QALD-5 dataset. We empirically prove that NQS is robust to tackle paraphrases of the questions. EARL achieves the state of the art results in Entity Linking and Relation Linking for question answering on several KGQA datasets. The dataset curated in this thesis has helped the research community to move forward in the direction of improving the accuracy of complex question answering as a task as other researchers too developed several KGQA systems and modules around these published datasets. With the large-scale datasets, we have encouraged the use of large scale machine learning, deep learning and emergence of new techniques to advance the state-of-the-art in complex question answering over knowledge graphs. We further developed core components for the KGQA pipeline to overcome the challenges of Question Understanding, Entity-Relation Linking and Answer Verbalization and thus achieve our research objective. All the proposed approaches mentioned in this thesis and the published resources are available at https://github.com/AskNowQA and are released under the umbrella project AskNow.

2020

Banerjee, Debayan; Chaudhuri, Debanjan; Dubey, Mohnish; Lehmann, Jens

PNEL: Pointer Network Based End-To-End Entity Linking over Knowledge Graphs Inproceedings

In: The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I, pp. 21–38, Springer, 2020.

Links | BibTeX

Zafar, Hamid; Dubey, Mohnish; Lehmann, Jens; Demidova, Elena

IQA: Interactive query construction in semantic question answering systems Journal Article

In: J. Web Semant., vol. 64, pp. 100586, 2020.

Links | BibTeX

2019

Dubey, Mohnish; Banerjee, Debayan; Abdelkawi, Abdelrahman; Lehmann, Jens

LC-QĂD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia Inproceedings

In: The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019, Proceedings, Part II, pp. 69–78, Springer, 2019.

Links | BibTeX

2018

Banerjee, Debayan; Dubey, Mohnish; Chaudhuri, Debanjan; Lehmann, Jens

Joint Entity and Relation Linking Using EARL 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.

Links | BibTeX

Dubey, Mohnish; Banerjee, Debayan; Chaudhuri, Debanjan; Lehmann, Jens

EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs Inproceedings

In: The Semantic Web - ISWC 2018 - 17th International Semantic Web Conference, Monterey, CA, USA, October 8-12, 2018, Proceedings, Part I, pp. 108–126, Springer, 2018.

Links | BibTeX

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.

Links | BibTeX

Maheshwari, Gaurav; Dubey, Mohnish; Trivedi, Priyansh; Lehmann, Jens

How to Revert Question Answering on Knowledge Graphs 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.

Links | BibTeX

Trivedi, Priyansh; Maheshwari, Gaurav; Dubey, Mohnish; Lehmann, Jens

LC-QĂD: A Corpus for Complex Question Answering over Knowledge Graphs Inproceedings

In: The Semantic Web - ISWC 2017 - 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II, pp. 210–218, Springer, 2017.

Links | BibTeX

2016

Dubey, Mohnish; Dasgupta, Sourish; Sharma, Ankit; Höffner, Konrad; Lehmann, Jens

AskNow: A Framework for Natural Language Query Formalization in SPARQL Inproceedings

In: The Semantic Web. Latest Advances and New Domains - 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29 - June 2, 2016, Proceedings, pp. 300–316, Springer, 2016.

Links | BibTeX