Arnab Dutta

University of Hong Kong

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Shamsuzzaman Farooq

National University of Singapore

Iftekhar A. Karimi

National University of Singapore

Debirupa Mitra

Birla Institute of Technology and Science - Hyderabad Campus

Richard K. Green

University of Southern California

Christian Meilicke

University of Mannheim

Pranav Nambiar

Gladstone Institutes

Venkatesh Panchapagesan

Indian Institute of Management Bangalore

Madalasa Venkataraman

Indian Institute of Management Bangalore

Dhanush Majji

Birla Institute of Technology and Science, Pilani

Alok Choudhary

University of Warwick

Cooperative Institutions

Birla Institute of Technology and Science - Hyderabad Campus

National University of Singapore

Birla Institute of Technology and Science, Pilani

Deenbandhu Chhotu Ram University of Science and Technology

University of Mannheim

The University of Texas at Austin

Indian Institute of Technology Delhi

Sinhgad Dental College and Hospital

University of Southern California

The Cambridge Centre for Advanced Research and Education in Singapore

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Enriching Structured Knowledge with Open Information

Arnab Dutta Christian Meilicke Heiner Stuckenschmidt

We propose an approach for semantifying web extracted facts. In particular, we map subject and object terms of these facts to instances; and relational phrases to object properties defined in a target knowledge base. By doing this we resolve the ambiguity inherent in the web extracted facts, while simultaneously enriching the target knowledge base with a significant number of new assertions. In this paper, we focus on the mapping of the relational phrases in the context of the overall work ow. Furthermore, in an open extraction setting identical semantic relationships can be represented by different surface forms, making it necessary to group these surface forms together. To solve this problem we propose the use of markov clustering. In this work we present a complete, ontology independent, generalized workflow which we evaluate on facts extracted by Nell and Reverb. Our target knowledge base is DBpedia. Our evaluation shows promising results in terms of producing highly precise facts. Moreover, the results indicate that the clustering of relational phrases pays of in terms of an improved instance and property mapping.

Entity linking

10.1145/2736277.2741139

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Citations (32)

Design and optimization of boil-off gas recondensation process by recovering waste cold energy in LNG regasification terminals

Process Safety and Environmental Protection (2024)

Malayanur Srilekha Arnab Dutta

10.1016/j.cherd.2024.03.041

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Citations (2)

Semantifying Triples from Open Information Extraction Systems

Frontiers in artificial intelligence and applications (2014)

Arnab Dutta Christian Meilicke Heiner Stuckenschmidt

The last few years have witnessed some remarkable success of the state-of-the art unsupervised knowledge extraction systems like NELL and REVERB. These systems are gifted with typically web-scale coverage but are often plagued with ambiguity due to lack of proper schema or unique identifiers for the instances. This classifies them apart from extraction systems like DBPEDIA, YAGO or FREE-BASE which have precise information content but have smaller coverage. In this work we bring together the former to enrich the later with high precision novel facts and present a statistical approach to discover new knowledge. In particular, we semantify NELL triples using DBPEDIA.

10.3233/978-1-61499-421-3-111

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Citations (11)

Value added hydrocarbons from lignin derived bio-oils: Insights from process simulations

Materials Today Proceedings (2022)

Anant Gaurav Kedia Pankaj Kumar Arnab Dutta

Guaiacol

Hydrodeoxygenation

Depolymerization

10.1016/j.matpr.2022.07.042

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Citations (1)

KisanConnect: Reshaping Agricultural Trade with Dynamic Pricing Model

Souvik Bhukta Agniva Shil Arnab Dutta Bikash Sadhukhan

10.1109/icnews60873.2024.10730822

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Citations (0)

A SAR ADC with Reduced kT/C Noise by Decoupling Noise PSD and BW

Zhelu Li Arnab Dutta Abhishek Mukherjee Xiyuan Tang Linxiao Shen

This paper presents a SAR ADC with reduced front-end sampling kT/C noise. This is achieved by using an active sampling circuit with a specially designed 2-stage amplifier that decouples the tight relationship between the sampling noise power spectral density (PSD) and BW. A 12-bit 12-MS/s prototype ADC in 40nm CMOS achieves the sampling noise power reduction by 3.5 times. It permits the use of a small sampling capacitor of only 132 fF. This relaxes the requirement on the ADC input driver and reference buffer, which can lead to significant savings in power, area, and complexity on the system level.

Decoupling (probability)

Successive approximation ADC

Noise spectral density

Correlated double sampling

10.1109/vlsicircuits18222.2020.9162846

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Citations (12)

A brief note on compactness of composition operators on H²

Complex Variables and Elliptic Equations (2017)

John R. Akeroyd Arnab Dutta

Let be a univalent (analytic) mapping from the unit disk into itself. Under a certain regularity condition on the bundary of , we show that the composition operator is compact on the Hardy space if and only if

Unit disk

Composition operator

Operator (biology)

10.1080/17476933.2017.1416358

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Citations (0)

A Note on Closed-Range Composition Operators

Complex Analysis and Operator Theory (2016)

John R. Akeroyd Arnab Dutta

Unit disk

Composition operator

Operator (biology)