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[1]
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Mayank Kejriwal.
Artificial Intelligence for Industries of the Future - Beyond
Facebook, Amazon, Microsoft and Google.
Springer, 2023.
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DOI |
http ]
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[2]
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Ke Shen and Mayank Kejriwal.
An experimental study measuring the generalization of fine-tuned
language representation models across commonsense reasoning benchmarks.
Expert Syst. J. Knowl. Eng., 40(5), 2023.
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DOI |
http ]
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[3]
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Mayank Kejriwal, Henrique Santos, Ke Shen, Alice M. Mulvehill, and Deborah L.
McGuinness.
Context-rich evaluation of machine common sense.
In Patrick Hammer, Marjan Alirezaie, and Claes Strannegård,
editors, Artificial General Intelligence - 16th International
Conference, AGI 2023, Stockholm, Sweden, June 16-19, 2023, Proceedings,
volume 13921 of Lecture Notes in Computer Science, pages 167-176.
Springer, 2023.
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DOI |
http ]
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[4]
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Zhisheng Tang and Mayank Kejriwal.
Can language models be used in multistep commonsense planning
domains?
In Patrick Hammer, Marjan Alirezaie, and Claes Strannegård,
editors, Artificial General Intelligence - 16th International
Conference, AGI 2023, Stockholm, Sweden, June 16-19, 2023, Proceedings,
volume 13921 of Lecture Notes in Computer Science, pages 276-285.
Springer, 2023.
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DOI |
http ]
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[5]
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Sanju Tiwari, Nandana Mihindukulasooriya, Francesco Osborne, Dimitris
Kontokostas, Jennifer D'Souza, Mayank Kejriwal, and Edgard Marx, editors.
Joint Proceedings of the Second International Workshop on
Knowledge Graph Generation From Text and the First International BiKE
Challenge co-located with 20th Extended Semantic Conference (ESWC 2023),
Hersonissos, Greece, May 29th, 2023, volume 3447 of CEUR Workshop
Proceedings. CEUR-WS.org, 2023.
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http ]
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[6]
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Zhisheng Tang and Mayank Kejriwal.
A pilot evaluation of chatgpt and DALL-E 2 on decision making and
spatial reasoning.
CoRR, abs/2302.09068, 2023.
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DOI |
arXiv |
http ]
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[7]
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Katarina Doctor, Christine Task, Eric J. Kildebeck, Mayank Kejriwal, Lawrence
Holder, and Russell Leong.
Toward defining a domain complexity measure across domains.
CoRR, abs/2303.04141, 2023.
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DOI |
arXiv |
http ]
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[8]
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Yidan Sun and Mayank Kejriwal.
A structural study of big tech firm-switching of inventors in the
post-recession era.
CoRR, abs/2307.07920, 2023.
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DOI |
arXiv |
http ]
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[9]
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Mayank Kejriwal.
Named entity resolution in personal knowledge graphs.
CoRR, abs/2307.12173, 2023.
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DOI |
arXiv |
http ]
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[10]
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Mayank Kejriwal, Ke Shen, Chien-Chun Ni, and Nicolas Torzec.
Transfer-based taxonomy induction over concept labels.
Eng. Appl. Artif. Intell., 108:104548, 2022.
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DOI |
http ]
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[11]
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Mayank Kejriwal.
Knowledge graphs: A practical review of the research landscape.
Inf., 13(4):161, 2022.
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DOI |
http ]
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[12]
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Minda Hu and Mayank Kejriwal.
Measuring spatio-textual affinities in twitter between two urban
metropolises.
J. Comput. Soc. Sci., 5(1):227-252, 2022.
[ bib |
DOI |
http ]
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[13]
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Mayank Kejriwal, Henrique Santos, Alice M. Mulvehill, and Deborah L.
McGuinness.
Designing a strong test for measuring true common-sense reasoning.
Nat. Mach. Intell., 4(4):318-322, 2022.
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DOI |
http ]
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[14]
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Mayank Kejriwal and Pedro A. Szekely.
Knowledge graphs for social good: An entity-centric search engine for
the human trafficking domain.
IEEE Trans. Big Data, 8(3):592-606, 2022.
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DOI |
http ]
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[15]
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Trevor Bonjour, Marina Haliem, Aala Oqab Alsalem, Shilpa Thomas, Hongyu Li,
Vaneet Aggarwal, Mayank Kejriwal, and Bharat K. Bhargava.
Decision making in monopoly using a hybrid deep reinforcement
learning approach.
IEEE Trans. Emerg. Top. Comput. Intell., 6(6):1335-1344,
2022.
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DOI |
http ]
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[16]
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Yuesheng Luo and Mayank Kejriwal.
Understanding COVID-19 vaccine reaction through comparative
analysis on twitter.
In Kohei Arai, editor, Intelligent Computing - Proceedings of
the 2022 Computing Conference, Volume 1, SAI 2022, Virtual Event, 14-15
July 2022, volume 506 of Lecture Notes in Networks and Systems, pages
846-864. Springer, 2022.
[ bib |
DOI |
http ]
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[17]
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Sanju Tiwari, Nandana Mihindukulasooriya, Francesco Osborne, Dimitris
Kontokostas, Jennifer D'Souza, Mayank Kejriwal, Loris Bozzato,
Valentina Anita Carriero, Torsten Hahmann, and Antoine Zimmermann, editors.
Proceedings of the 1st International Workshop on Knowledge Graph
Generation From Text and the 1st International Workshop on Modular Knowledge
co-located with 19th Extended Semantic Conference (ESWC 2022), Hersonissos,
Greece, May 30th, 2022, volume 3184 of CEUR Workshop Proceedings.
CEUR-WS.org, 2022.
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http ]
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[18]
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Mayank Kejriwal and Ke Shen.
Can scale-free network growth with triad formation capture simplicial
complex distributions in real communication networks?
CoRR, abs/2203.06491, 2022.
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DOI |
arXiv |
http ]
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[19]
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Henrique Santos, Ke Shen, Alice M. Mulvehill, Yasaman Razeghi, Deborah L.
McGuinness, and Mayank Kejriwal.
A theoretically grounded benchmark for evaluating machine
commonsense.
CoRR, abs/2203.12184, 2022.
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DOI |
arXiv |
http ]
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[20]
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Akarsh Nagaraj and Mayank Kejriwal.
Robust quantification of gender disparity in pre-modern english
literature using natural language processing.
CoRR, abs/2204.05872, 2022.
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DOI |
arXiv |
http ]
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[21]
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Ke Shen and Mayank Kejriwal.
Understanding prior bias and choice paralysis in transformer-based
language representation models through four experimental probes.
CoRR, abs/2210.01258, 2022.
[ bib |
DOI |
arXiv |
http ]
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[22]
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Ke Shen and Mayank Kejriwal.
Understanding substructures in commonsense relations in conceptnet.
CoRR, abs/2210.01263, 2022.
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DOI |
arXiv |
http ]
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[23]
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Zhisheng Tang and Mayank Kejriwal.
Can language representation models think in bets?
CoRR, abs/2210.07519, 2022.
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DOI |
arXiv |
http ]
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[24]
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Mayank Kejriwal and Yuesheng Luo.
On the empirical association between trade network complexity and
global gross domestic product.
CoRR, abs/2211.13117, 2022.
[ bib |
DOI |
arXiv |
http ]
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[25]
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Mayank Kejriwal, Ke Shen, Chien-Chun Ni, and Nicolas Torzec.
An evaluation and annotation methodology for product category
matching in e-commerce.
Comput. Ind., 131:103497, 2021.
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DOI |
http ]
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[26]
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Sara Melotte and Mayank Kejriwal.
A geo-tagged COVID-19 twitter dataset for 10 north american
metropolitan areas over a 255-day period.
Data, 6(6):64, 2021.
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DOI |
http ]
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[27]
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Mayank Kejriwal.
Link prediction between structured geopolitical events: Models and
experiments.
Frontiers Big Data, 4:779792, 2021.
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DOI |
http ]
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[28]
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Minda Hu, Ashwin Rao, Mayank Kejriwal, and Kristina Lerman.
Socioeconomic correlates of anti-science attitudes in the US.
Future Internet, 13(6):160, 2021.
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DOI |
http ]
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[29]
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Mayank Kejriwal.
Unsupervised DNF blocking for efficient linking of knowledge graphs
and tables.
Inf., 12(3):134, 2021.
[ bib |
DOI |
http ]
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[30]
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Mayank Kejriwal, Qile Wang, Hongyu Li, and Lu Wang.
An empirical study of emoji usage on twitter in linguistic and
national contexts.
Online Soc. Networks Media, 24:100149, 2021.
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DOI |
http ]
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[31]
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Mayank Kejriwal.
A meta-engine for building domain-specific search engines.
Softw. Impacts, 7:100052, 2021.
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DOI |
http ]
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[32]
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Mayank Kejriwal and Shilpa Thomas.
A multi-agent simulator for generating novelty in monopoly.
Simul. Model. Pract. Theory, 112:102364, 2021.
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DOI |
http ]
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[33]
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Mayank Kejriwal, Ravi Kiran Selvam, Chien-Chun Ni, and Nicolas Torzec.
Empirical best practices on using product-specific schema.org.
In Thirty-Fifth AAAI Conference on Artificial Intelligence,
AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in
Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021,
pages 15452-15457. AAAI Press, 2021.
[ bib |
http ]
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[34]
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Mayank Kejriwal and Ke Shen.
Unsupervised real-time induction and interactive visualization of
taxonomies over domain-specific concepts.
In Michele Coscia, Alfredo Cuzzocrea, Kai Shu, Ralf Klamma, Sharyn
O'Halloran, and Jon G. Rokne, editors, ASONAM '21: International
Conference on Advances in Social Networks Analysis and Mining, Virtual Event,
The Netherlands, November 8 - 11, 2021, pages 301-304. ACM, 2021.
[ bib |
DOI |
http ]
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[35]
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Ke Shen and Mayank Kejriwal.
On the generalization abilities of fine-tuned commonsense language
representation models.
In Max Bramer and Richard Ellis, editors, Artificial
Intelligence XXXVIII - 41st SGAI International Conference on Artificial
Intelligence, AI 2021, Cambridge, UK, December 14-16, 2021, Proceedings,
volume 13101 of Lecture Notes in Computer Science, pages 3-16.
Springer, 2021.
[ bib |
DOI |
http ]
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[36]
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Mayank Kejriwal, Ge Fang, and Ying Zhou.
A feasibility study of open-source sentiment analysis and text
classification systems on disaster-specific social media data.
In IEEE Symposium Series on Computational Intelligence, SSCI
2021, Orlando, FL, USA, December 5-7, 2021, pages 1-8. IEEE, 2021.
[ bib |
DOI |
http ]
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[37]
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Marina Haliem, Trevor Bonjour, Aala Oqab Alsalem, Shilpa Thomas, Hongyu Li,
Vaneet Aggarwal, Bharat K. Bhargava, and Mayank Kejriwal.
Learning monopoly gameplay: A hybrid model-free deep reinforcement
learning and imitation learning approach.
CoRR, abs/2103.00683, 2021.
[ bib |
arXiv |
http ]
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[38]
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Sara Melotte and Mayank Kejriwal.
Predicting zip code-level vaccine hesitancy in US metropolitan
areas using machine learning models on public tweets.
CoRR, abs/2108.01699, 2021.
[ bib |
arXiv |
http ]
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[39]
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Yuesheng Luo and Mayank Kejriwal.
Understanding COVID-19 vaccine reaction through comparative
analysis on twitter.
CoRR, abs/2111.05823, 2021.
[ bib |
arXiv |
http ]
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[40]
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Mayank Kejriwal and Yao Gu.
Network-theoretic modeling of complex activity using UK online sex
advertisements.
Appl. Netw. Sci., 5(1):30, 2020.
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DOI |
http ]
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[41]
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Mayank Kejriwal and Akarsh Dang.
Structural studies of the global networks exposed in the panama
papers.
Appl. Netw. Sci., 5(1):63, 2020.
[ bib |
DOI |
http ]
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[42]
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Mayank Kejriwal and Peilin Zhou.
On detecting urgency in short crisis messages using minimal
supervision and transfer learning.
Soc. Netw. Anal. Min., 10(1):58, 2020.
[ bib |
DOI |
http ]
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[43]
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Mayank Kejriwal, Ravi Kiran Selvam, Chien-Chun Ni, and Nicolas Torzec.
Locally constructing product taxonomies from scratch using
representation learning.
In Martin Atzmüller, Michele Coscia, and Rokia Missaoui,
editors, IEEE/ACM International Conference on Advances in Social
Networks Analysis and Mining, ASONAM 2020, The Hague, Netherlands, December
7-10, 2020, pages 507-514. IEEE, 2020.
[ bib |
DOI |
http ]
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[44]
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Ravi Kiran Selvam and Mayank Kejriwal.
On using product-specific schema.org from web data commons: An
empirical set of best practices.
CoRR, abs/2007.13829, 2020.
[ bib |
arXiv |
http ]
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[45]
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Jiayuan Ding and Mayank Kejriwal.
An experimental study of the effects of position bias on emotion
causeextraction.
CoRR, abs/2007.15066, 2020.
[ bib |
arXiv |
http ]
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[46]
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Mayank Kejriwal and Ke Shen.
Do fine-tuned commonsense language models really generalize?
CoRR, abs/2011.09159, 2020.
[ bib |
arXiv |
http ]
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[47]
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Ke Shen and Mayank Kejriwal.
A data-driven study of commonsense knowledge using the conceptnet
knowledge base.
CoRR, abs/2011.14084, 2020.
[ bib |
arXiv |
http ]
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[48]
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Mayank Kejriwal.
Domain-Specific Knowledge Graph Construction.
Springer Briefs in Computer Science. Springer, 2019.
[ bib |
DOI |
http ]
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[49]
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Mayank Kejriwal and Rahul Kapoor.
Network-theoretic information extraction quality assessment in the
human trafficking domain.
Appl. Netw. Sci., 4(1):44:1-44:26, 2019.
[ bib |
DOI |
http ]
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[50]
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Mayank Kejriwal and Pedro A. Szekely.
mydig: Personalized illicit domain-specific knowledge discovery with
no programming.
Future Internet, 11(3):59, 2019.
[ bib |
DOI |
http ]
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[51]
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Mayank Kejriwal, Juan F. Sequeda, and Vanessa Lopez.
Knowledge graphs: Construction, management and querying.
Semantic Web, 10(6):961-962, 2019.
[ bib |
DOI |
http ]
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[52]
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Mayank Kejriwal and Peilin Zhou.
Low-supervision urgency detection and transfer in short crisis
messages.
In Francesca Spezzano, Wei Chen, and Xiaokui Xiao, editors,
ASONAM '19: International Conference on Advances in Social Networks
Analysis and Mining, Vancouver, British Columbia, Canada, 27-30 August,
2019, pages 353-356. ACM, 2019.
[ bib |
DOI |
http ]
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[53]
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Shuo Zhang and Mayank Kejriwal.
Concept drift in bias and sensationalism detection: an experimental
study.
In Francesca Spezzano, Wei Chen, and Xiaokui Xiao, editors,
ASONAM '19: International Conference on Advances in Social Networks
Analysis and Mining, Vancouver, British Columbia, Canada, 27-30 August,
2019, pages 601-604. ACM, 2019.
[ bib |
DOI |
http ]
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[54]
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Mayank Kejriwal and Peilin Zhou.
SAVIZ: interactive exploration and visualization of situation
labeling classifiers over crisis social media data.
In Francesca Spezzano, Wei Chen, and Xiaokui Xiao, editors,
ASONAM '19: International Conference on Advances in Social Networks
Analysis and Mining, Vancouver, British Columbia, Canada, 27-30 August,
2019, pages 705-708. ACM, 2019.
[ bib |
DOI |
http ]
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[55]
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Mayank Kejriwal and Pedro A. Szekely.
Co-lod: Continuous space linked open data.
In Mari Carmen Suárez-Figueroa, Gong Cheng, Anna Lisa
Gentile, Christophe Guéret, C. Maria Keet, and Abraham Bernstein,
editors, 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, volume 2456 of CEUR Workshop Proceedings, pages
333-337. CEUR-WS.org, 2019.
[ bib |
.pdf ]
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[56]
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Mozhdeh Gheini and Mayank Kejriwal.
Unsupervised product entity resolution using graph representation
learning.
In Jon Degenhardt, Surya Kallumadi, Utkarsh Porwal, and Andrew
Trotman, editors, Proceedings of the SIGIR 2019 Workshop on eCommerce,
co-located with the 42st International ACM SIGIR Conference on Research
and Development in Information Retrieval, eCom@SIGIR 2019, Paris, France,
July 25, 2019, volume 2410 of CEUR Workshop Proceedings.
CEUR-WS.org, 2019.
[ bib |
.pdf ]
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[57]
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Mayank Kejriwal, Runqi Shao, and Pedro A. Szekely.
Expert-guided entity extraction using expressive rules.
In Benjamin Piwowarski, Max Chevalier, Éric Gaussier, Yoelle
Maarek, Jian-Yun Nie, and Falk Scholer, editors, Proceedings of the
42nd International ACM SIGIR Conference on Research and Development in
Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019, pages
1353-1356. ACM, 2019.
[ bib |
DOI |
http ]
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[58]
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Mayank Kejriwal, Pedro A. Szekely, and Raphaël Troncy, editors.
Proceedings of the 10th International Conference on Knowledge
Capture, K-CAP 2019, Marina Del Rey, CA, USA, November 19-21, 2019. ACM,
2019.
[ bib |
DOI |
http ]
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[59]
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Mayank Kejriwal and Peilin Zhou.
Low-supervision urgency detection and transfer in short crisis
messages.
CoRR, abs/1907.06745, 2019.
[ bib |
arXiv |
http ]
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[60]
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Daye Nam and Mayank Kejriwal.
How do organizations publish semantic markup? three case studies
using public schema.org crawls.
Computer, 51(6):42-51, 2018.
[ bib |
DOI |
http ]
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[61]
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Mayank Kejriwal, Pedro A. Szekely, and Craig A. Knoblock.
Investigative knowledge discovery for combating illicit activities.
IEEE Intell. Syst., 33(1):53-63, 2018.
[ bib |
DOI |
http ]
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[62]
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Mayank Kejriwal and Pedro A. Szekely.
Constructing domain-specific search engines with no programming.
In Sheila A. McIlraith and Kilian Q. Weinberger, editors,
Proceedings of the Thirty-Second AAAI Conference on Artificial
Intelligence, (AAAI-18), the 30th innovative Applications of Artificial
Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances
in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February
2-7, 2018, pages 8204-8205. AAAI Press, 2018.
[ bib |
http ]
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[63]
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Kyle Hundman, Thamme Gowda, Mayank Kejriwal, and Benedikt Boecking.
Always lurking: Understanding and mitigating bias in online human
trafficking detection.
In Jason Furman, Gary E. Marchant, Huw Price, and Francesca Rossi,
editors, Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics,
and Society, AIES 2018, New Orleans, LA, USA, February 02-03, 2018, pages
137-143. ACM, 2018.
[ bib |
DOI |
http ]
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[64]
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Mayank Kejriwal and Pedro A. Szekely.
Technology-assisted investigative search: A case study from an
illicit domain.
In Regan L. Mandryk, Mark Hancock, Mark Perry, and Anna L. Cox,
editors, Extended Abstracts of the 2018 CHI Conference on Human
Factors in Computing Systems, CHI 2018, Montreal, QC, Canada, April 21-26,
2018. ACM, 2018.
[ bib |
DOI |
http ]
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[65]
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Mayank Kejriwal, Jing Peng, Haotian Zhang, and Pedro A. Szekely.
Structured event entity resolution in humanitarian domains.
In Denny Vrandecic, Kalina Bontcheva, Mari Carmen
Suárez-Figueroa, Valentina Presutti, Irene Celino, Marta Sabou,
Lucie-Aimée Kaffee, and Elena Simperl, editors, The Semantic Web
- ISWC 2018 - 17th International Semantic Web Conference, Monterey, CA,
USA, October 8-12, 2018, Proceedings, Part I, volume 11136 of Lecture
Notes in Computer Science, pages 233-249. Springer, 2018.
[ bib |
DOI |
http ]
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[66]
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Mayank Kejriwal, Daniel Gilley, Pedro A. Szekely, and Jill Crisman.
THOR: text-enabled analytics for humanitarian operations.
In Pierre-Antoine Champin, Fabien Gandon, Mounia Lalmas, and
Panagiotis G. Ipeirotis, editors, Companion of the The Web Conference
2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27,
2018, pages 147-150. ACM, 2018.
[ bib |
DOI |
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[67]
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Pedro A. Szekely and Mayank Kejriwal.
Domain-specific insight graphs (DIG).
In Pierre-Antoine Champin, Fabien Gandon, Mounia Lalmas, and
Panagiotis G. Ipeirotis, editors, Companion of the The Web Conference
2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27,
2018, pages 433-434. ACM, 2018.
[ bib |
DOI |
http ]
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[68]
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Jie Tang, Michalis Vazirgiannis, Yuxiao Dong, Fragkiskos D. Malliaros, Michael
Cochez, Mayank Kejriwal, and Achim Rettinger.
Bignet 2018 chairs' welcome & organization.
In Pierre-Antoine Champin, Fabien Gandon, Mounia Lalmas, and
Panagiotis G. Ipeirotis, editors, Companion of the The Web Conference
2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27,
2018, pages 943-944. ACM, 2018.
[ bib |
DOI |
http ]
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[69]
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Michael Cochez, Thierry Declerck, Gerard de Melo, Luis Espinosa Anke, Besnik
Fetahu, Dagmar Gromann, Mayank Kejriwal, Maria Koutraki, Freddy
Lécué, Enrico Palumbo, and Harald Sack, editors.
Proceedings of the First Workshop on Deep Learning for Knowledge
Graphs and Semantic Technologies (DL4KGS) co-located with the 15th Extended
Semantic Web Conerence (ESWC 2018), Heraklion, Crete, Greece, June 4,
2018, volume 2106 of CEUR Workshop Proceedings. CEUR-WS.org, 2018.
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[70]
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Tongtao Zhang, Ananya Subburathinam, Ge Shi, Lifu Huang, Di Lu, Xiaoman Pan,
Manling Li, Boliang Zhang, Qingyun Wang, Spencer Whitehead, Heng Ji, Alireza
Zareian, Hassan Akbari, Brian Chen, Ruiqi Zhong, Steven Shao, Emily Allaway,
Shih-Fu Chang, Kathleen R. McKeown, Dongyu Li, Xin Huang, Kexuan Sun, Xujun
Peng, Ryan Gabbard, Marjorie Freedman, Mayank Kejriwal, Ram Nevatia, Pedro A.
Szekely, T. K. Satish Kumar, Ali Sadeghian, Giacomo Bergami, Sourav Dutta,
Miguel E. Rodríguez, and Daisy Zhe Wang.
GAIA - A multi-media multi-lingual knowledge extraction and
hypothesis generation system.
In Proceedings of the 2018 Text Analysis Conference, TAC 2018,
Gaithersburg, Maryland, USA, November 13-14, 2018. NIST, 2018.
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[71]
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Mayank Kejriwal and Yao Gu.
A pipeline for post-crisis twitter data acquisition.
CoRR, abs/1801.05881, 2018.
[ bib |
arXiv |
http ]
|
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[72]
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Yao Gu and Mayank Kejriwal.
Unsupervised hashtag retrieval and visualization for crisis
informatics.
CoRR, abs/1801.05906, 2018.
[ bib |
arXiv |
http ]
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[73]
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Mayank Kejriwal.
Populating a Linked Data Entity Name System - A Big Data
Solution to Unsupervised Instance Matching, volume 27 of Studies on the
Semantic Web.
IOS Press, 2017.
[ bib |
DOI |
http ]
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[74]
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Mayank Kejriwal.
Populating a linked data entity name system.
AI Matters, 3(2):22-23, 2017.
[ bib |
DOI |
http ]
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[75]
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Mayank Kejriwal and Pedro A. Szekely.
Scalable generation of type embeddings using the abox.
Open J. Semantic Web, 4(1):20-34, 2017.
[ bib |
.html ]
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[76]
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Mayank Kejriwal and Pedro A. Szekely.
Neural embeddings for populated geonames locations.
In Claudia d'Amato, Miriam Fernández, Valentina A. M. Tamma,
Freddy Lécué, Philippe Cudré-Mauroux, Juan F. Sequeda,
Christoph Lange, and Jeff Heflin, editors, The Semantic Web - ISWC
2017 - 16th International Semantic Web Conference, Vienna, Austria, October
21-25, 2017, Proceedings, Part II, volume 10588 of Lecture Notes in
Computer Science, pages 139-146. Springer, 2017.
[ bib |
DOI |
http ]
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[77]
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Mayank Kejriwal and Pedro A. Szekely.
An investigative search engine for the human trafficking domain.
In Claudia d'Amato, Miriam Fernández, Valentina A. M. Tamma,
Freddy Lécué, Philippe Cudré-Mauroux, Juan F. Sequeda,
Christoph Lange, and Jeff Heflin, editors, The Semantic Web - ISWC
2017 - 16th International Semantic Web Conference, Vienna, Austria, October
21-25, 2017, Proceedings, Part II, volume 10588 of Lecture Notes in
Computer Science, pages 247-262. Springer, 2017.
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Mayank Kejriwal, Thomas Schellenberg, and Pedro A. Szekely.
A semantic search engine for investigating human trafficking.
In Nadeschda Nikitina, Dezhao Song, Achille Fokoue, and Peter Haase,
editors, 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, volume 1963
of CEUR Workshop Proceedings. CEUR-WS.org, 2017.
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Rahul Kapoor, Mayank Kejriwal, and Pedro A. Szekely.
Using contexts and constraints for improved geotagging of human
trafficking webpages.
In Panagiotis Bouros and Mohamed Sarwat, editors, Proceedings of
the Fourth International ACM Workshop on Managing and Mining Enriched
Geo-Spatial Data, Chicago, IL, USA, May 14, 2017, pages 3:1-3:6. ACM,
2017.
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Mayank Kejriwal and Pedro A. Szekely.
Supervised typing of big graphs using semantic embeddings.
In Sven Groppe and Le Gruenwald, editors, Proceedings of The
International Workshop on Semantic Big Data, SBD@SIGMOD 2017, Chicago, IL,
USA, May 19, 2017, pages 3:1-3:6. ACM, 2017.
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Mayank Kejriwal.
Predicting role relevance with minimal domain expertise in a
financial domain.
In Proceedings of the 3rd International Workshop on Data Science
for Macro-Modeling with Financial and Economic Datasets, DSMM@SIGMOD 2017,
Chicago, IL, USA, May 14, 2017, pages 10:1-10:2. ACM, 2017.
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Mayank Kejriwal and Pedro A. Szekely.
Information extraction in illicit web domains.
In Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy
Gabrilovich, editors, Proceedings of the 26th International Conference
on World Wide Web, WWW 2017, Perth, Australia, April 3-7, 2017, pages
997-1006. ACM, 2017.
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[83]
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Sarven Capadisli, Franck Cotton, Xin Luna Dong, Ramanathan V. Guha, Armin
Haller, Pascal Hitzler, Evangelos Kalampokis, Mayank Kejriwal, Freddy
Lécué, D. Sivakumar, Pedro A. Szekely, Raphaël Troncy, and
Michael Witbrock, editors.
Joint Proceedings of the International Workshops on Hybrid
Statistical Semantic Understanding and Emerging Semantics, and Semantic
Statistics co-located with 16th International Semantic Web Conference,
HybridSemStats@ISWC 2017, Vienna, Austria October 22nd, 2017, CEUR
Workshop Proceedings. CEUR-WS.org, 2017.
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Mayank Kejriwal and Pedro A. Szekely.
Information extraction in illicit domains.
CoRR, abs/1703.03097, 2017.
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arXiv |
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Mayank Kejriwal and Pedro A. Szekely.
Supervised typing of big graphs using semantic embeddings.
CoRR, abs/1703.07805, 2017.
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arXiv |
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Rahul Kapoor, Mayank Kejriwal, and Pedro A. Szekely.
Using contexts and constraints for improved geotagging of human
trafficking webpages.
CoRR, abs/1704.05569, 2017.
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arXiv |
http ]
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Mayank Kejriwal.
Predicting role relevance with minimal domain expertise in a
financial domain.
CoRR, abs/1704.05571, 2017.
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arXiv |
http ]
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[88]
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Kyle Hundman, Thamme Gowda, Mayank Kejriwal, and Benedikt Boecking.
Always lurking: Understanding and mitigating bias in online human
trafficking detection.
CoRR, abs/1712.00846, 2017.
[ bib |
arXiv |
http ]
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[89]
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Mayank Kejriwal, Jiayuan Ding, Runqi Shao, Anoop Kumar, and Pedro A. Szekely.
Flagit: A system for minimally supervised human trafficking
indicator mining.
CoRR, abs/1712.03086, 2017.
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arXiv |
http ]
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[90]
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Mayank Kejriwal and Daniel P. Miranker.
Local, domain-independent heuristics for the FEIII challenge:
Lessons and observations.
In Proceedings of the Second International Workshop on Data
Science for Macro-Modeling, DSMM@SIGMOD 2016, San Francisco, CA, USA, June 26
- July 1, 2016, pages 17:1-17:2. ACM, 2016.
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[91]
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Mayank Kejriwal.
Disjunctive normal form schemes for heterogeneous attributed graphs.
CoRR, abs/1605.00686, 2016.
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arXiv |
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Mayank Kejriwal and Daniel P. Miranker.
Self-contained nosql resources for cross-domain RDF.
CoRR, abs/1608.04437, 2016.
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arXiv |
http ]
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[93]
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Mayank Kejriwal and Daniel P. Miranker.
Experience: Type alignment on dbpedia and freebase.
CoRR, abs/1608.04442, 2016.
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arXiv |
http ]
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[94]
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Janani Balaji, Faizan Javed, Mayank Kejriwal, Chris Min, Sam Sander, and Ozgur
Ozturk.
An ensemble blocking scheme for entity resolution of large and sparse
datasets.
CoRR, abs/1609.06265, 2016.
[ bib |
arXiv |
http ]
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[95]
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Mayank Kejriwal and Daniel P. Miranker.
An unsupervised instance matcher for schema-free RDF data.
J. Web Semant., 35:102-123, 2015.
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Mayank Kejriwal.
Sorted neighborhood for the semantic web.
In Blai Bonet and Sven Koenig, editors, Proceedings of the
Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30,
2015, Austin, Texas, USA, pages 4174-4175. AAAI Press, 2015.
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Mayank Kejriwal.
Entity resolution in a big data framework.
In Blai Bonet and Sven Koenig, editors, Proceedings of the
Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30,
2015, Austin, Texas, USA, pages 4243-4244. AAAI Press, 2015.
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Mayank Kejriwal, Qiaoling Liu, Ferosh Jacob, and Faizan Javed.
A pipeline for extracting and deduplicating domain-specific knowledge
bases.
In 2015 IEEE International Conference on Big Data (IEEE
BigData 2015), Santa Clara, CA, USA, October 29 - November 1, 2015, pages
1144-1153. IEEE Computer Society, 2015.
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Mayank Kejriwal and Daniel P. Miranker.
Sorted neighborhood for schema-free RDF data.
In Johanna Völker, Heiko Paulheim, Jens Lehmann, and Vojtech
Svátek, editors, Proceedings of the 4th Workshop on Knowledge
Discovery and Data Mining Meets Linked Open Data co-located with 12th
Extended Semantic Web Conference (ESWC 2015), Portoroz, Slovenia, May 31,
2015, volume 1365 of CEUR Workshop Proceedings. CEUR-WS.org, 2015.
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Mayank Kejriwal and Daniel P. Miranker.
Minimally supervised instance matching: An alternate approach.
In Fabien Gandon, Christophe Guéret, Serena Villata, John G.
Breslin, Catherine Faron-Zucker, and Antoine Zimmermann, editors, The
Semantic Web: ESWC 2015 Satellite Events - ESWC 2015 Satellite Events
Portorož, Slovenia, May 31 - June 4, 2015, Revised Selected Papers,
volume 9341 of Lecture Notes in Computer Science, pages 72-76.
Springer, 2015.
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Mayank Kejriwal and Daniel P. Miranker.
Sorted neighborhood for schema-free RDF data.
In Fabien Gandon, Christophe Guéret, Serena Villata, John G.
Breslin, Catherine Faron-Zucker, and Antoine Zimmermann, editors, The
Semantic Web: ESWC 2015 Satellite Events - ESWC 2015 Satellite Events
Portorož, Slovenia, May 31 - June 4, 2015, Revised Selected Papers,
volume 9341 of Lecture Notes in Computer Science, pages 217-229.
Springer, 2015.
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[102]
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Mayank Kejriwal and Daniel P. Miranker.
Semi-supervised instance matching using boosted classifiers.
In Fabien Gandon, Marta Sabou, Harald Sack, Claudia d'Amato, Philippe
Cudré-Mauroux, and Antoine Zimmermann, editors, The Semantic
Web. Latest Advances and New Domains - 12th European Semantic Web Conference,
ESWC 2015, Portoroz, Slovenia, May 31 - June 4, 2015. Proceedings, volume
9088 of Lecture Notes in Computer Science, pages 388-402. Springer,
2015.
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Mayank Kejriwal and Daniel P. Miranker.
Decision-making bias in instance matching model selection.
In Marcelo Arenas, Óscar Corcho, Elena Simperl, Markus
Strohmaier, Mathieu d'Aquin, Kavitha Srinivas, Paul Groth, Michel Dumontier,
Jeff Heflin, Krishnaprasad Thirunarayan, and Steffen Staab, editors, The
Semantic Web - ISWC 2015 - 14th International Semantic Web Conference,
Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part I, volume 9366
of Lecture Notes in Computer Science, pages 392-407. Springer, 2015.
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Mayank Kejriwal and Daniel P. Miranker.
A DNF blocking scheme learner for heterogeneous datasets.
CoRR, abs/1501.01694, 2015.
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[105]
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Mayank Kejriwal and Daniel P. Miranker.
On the complexity of sorted neighborhood.
CoRR, abs/1501.01696, 2015.
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Mayank Kejriwal and Daniel P. Miranker.
A two-step blocking scheme learner for scalable link discovery.
In Pavel Shvaiko, Jérôme Euzenat, Ming Mao, Ernesto
Jiménez-Ruiz, Juanzi Li, and Axel Ngonga, editors, Proceedings
of the 9th International Workshop on Ontology Matching collocated with the
13th International Semantic Web Conference (ISWC 2014), Riva del Garda,
Trentino, Italy, October 20, 2014, volume 1317 of CEUR Workshop
Proceedings, pages 49-60. CEUR-WS.org, 2014.
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Mayank Kejriwal and Daniel P. Miranker.
On linking heterogeneous dataset collections.
In Matthew Horridge, Marco Rospocher, and Jacco van Ossenbruggen,
editors, Proceedings of the ISWC 2014 Posters & Demonstrations
Track a track within the 13th International Semantic Web Conference, ISWC
2014, Riva del Garda, Italy, October 21, 2014, volume 1272 of CEUR
Workshop Proceedings, pages 217-220. CEUR-WS.org, 2014.
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Mayank Kejriwal.
Populating entity name systems for big data integration.
In Peter Mika, Tania Tudorache, Abraham Bernstein, Chris Welty,
Craig A. Knoblock, Denny Vrandecic, Paul Groth, Natasha F. Noy, Krzysztof
Janowicz, and Carole A. Goble, editors, The Semantic Web - ISWC 2014 -
13th International Semantic Web Conference, Riva del Garda, Italy, October
19-23, 2014. Proceedings, Part II, volume 8797 of Lecture Notes in
Computer Science, pages 521-528. Springer, 2014.
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[109]
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Aibo Tian, Mayank Kejriwal, and Daniel P. Miranker.
Schema matching over relations, attributes, and data values.
In Christian S. Jensen, Hua Lu, Torben Bach Pedersen, Christian
Thomsen, and Kristian Torp, editors, Conference on Scientific and
Statistical Database Management, SSDBM '14, Aalborg, Denmark, June 30 -
July 02, 2014, pages 28:1-28:12. ACM, 2014.
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[110]
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Mayank Kejriwal and Daniel P. Miranker.
An unsupervised algorithm for learning blocking schemes.
In Hui Xiong, George Karypis, Bhavani Thuraisingham, Diane J. Cook,
and Xindong Wu, editors, 2013 IEEE 13th International Conference on
Data Mining, Dallas, TX, USA, December 7-10, 2013, pages 340-349. IEEE
Computer Society, 2013.
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[111]
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Zihao Zhou, Mayank Kejriwal, and Risto Miikkulainen.
Extended scaled neural predictor for improved branch prediction.
In The 2013 International Joint Conference on Neural Networks,
IJCNN 2013, Dallas, TX, USA, August 4-9, 2013, pages 1-7. IEEE, 2013.
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[112]
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Liana Diesendruck, Luigi Marini, Rob Kooper, Mayank Kejriwal, and Kenton
McHenry.
A framework to access handwritten information within large digitized
paper collections.
In 8th IEEE International Conference on E-Science, e-Science
2012, Chicago, IL, USA, October 8-12, 2012, pages 1-10. IEEE Computer
Society, 2012.
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[113]
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Liana Diesendruck, Luigi Marini, Rob Kooper, Mayank Kejriwal, and Kenton
McHenry.
Digitization and search: A non-traditional use of HPC.
In 8th IEEE International Conference on E-Science, e-Science
2012, Chicago, IL, USA, October 8-12, 2012, pages 1-6. IEEE Computer
Society, 2012.
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[114]
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Liana Diesendruck, Luigi Marini, Rob Kooper, Mayank Kejriwal, and Kenton
McHenry.
Abstract: Digitization and search: A non-traditional use of HPC.
In 2012 SC Companion: High Performance Computing, Networking
Storage and Analysis, Salt Lake City, UT, USA, November 10-16, 2012, pages
1460-1461. IEEE Computer Society, 2012.
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Liana Diesendruck, Luigi Marini, Rob Kooper, Mayank Kejriwal, and Kenton
McHenry.
Poster: Digitization and search: A non-traditional use of HPC.
In 2012 SC Companion: High Performance Computing, Networking
Storage and Analysis, Salt Lake City, UT, USA, November 10-16, 2012, page
1462. IEEE Computer Society, 2012.
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