Amit Sheth - Academia.edu (original) (raw)

Papers by Amit Sheth

Research paper thumbnail of RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem

Proceedings of the AAAI Conference on Artificial Intelligence

For non-expert users, a textual query is the most popular and simple means for communicating with... more For non-expert users, a textual query is the most popular and simple means for communicating with a retrieval or question answering system.However, there is a risk of receiving queries which do not match with the background knowledge.Query expansion and query rewriting are solutions for this problem but they are in danger of potentially yielding a large number of irrelevant words, which in turn negatively influences runtime as well as accuracy.In this paper, we propose a new method for automatic rewriting input queries on graph-structured RDF knowledge bases.We employ a Hidden Markov Model to determine the most suitable derived words from linguistic resources.We introduce the concept of triple-based co-occurrence for recognizing co-occurred words in RDF data.This model was bootstrapped with three statistical distributions.Our experimental study demonstrates the superiority of the proposed approach to the traditional n-gram model.

Research paper thumbnail of Defining and detecting toxicity on social media: context and knowledge are key

Research paper thumbnail of Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

Proceedings of the 28th International Conference on Computational Linguistics, 2020

Research paper thumbnail of Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 2017

Research paper thumbnail of Listed for sale: Analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket

Drug and Alcohol Dependence, 2020

Research paper thumbnail of “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma

JMIR Pediatrics and Parenting, 2018

Research paper thumbnail of Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio

Computational and Mathematical Organization Theory, 2018

Research paper thumbnail of On Using the Intelligent Edge for IoT Analytics

IEEE Intelligent Systems, 2017

Research paper thumbnail of Information Brokering Across Heterogeneous Digital Data

Advances in Database Systems, 2002

Research paper thumbnail of Gleaning Types for Literals in RDF Triples with Application to Entity Summarization

Lecture Notes in Computer Science, 2016

Research paper thumbnail of Analyzing the social media footprint of street gangs

2015 IEEE International Conference on Intelligence and Security Informatics (ISI), 2015

Research paper thumbnail of On applying classification to schema integration

[1991] Proceedings. First International Workshop on Interoperability in Multidatabase Systems

Research paper thumbnail of Data processing and semantics for advanced internet of things (IoT) applications

Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, 2013

ABSTRACT This tutorial presents tools and techniques for effectively utilizing the Internet of Th... more ABSTRACT This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analysis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.

Research paper thumbnail of Executing multidatabase transactions

Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences, 1992

Research paper thumbnail of Growing Fields of Interest - Using an Expand and Reduce Strategy for Domain Model Extraction

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008

Research paper thumbnail of Semantic Analytics in Intelligence

Advanced Topics in Database Research, Volume 5

Research paper thumbnail of On Using WS-Policy, Ontology, and Rule Reasoning to Discover Web Services

Lecture Notes in Computer Science, 2004

Research paper thumbnail of Semantic Information Brokering: How Can a Multi-agent Approach Help?

Lecture Notes in Computer Science, 1999

Research paper thumbnail of An Ontological Approach to the Document Access Problem of Insider Threat

Intelligence and Security Informatics, 2005

Research paper thumbnail of Discovering Fine-grained Sentiment in Suicide Notes

Biomedical Informatics Insights, 2012

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid syst... more This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams.

Research paper thumbnail of RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem

Proceedings of the AAAI Conference on Artificial Intelligence

For non-expert users, a textual query is the most popular and simple means for communicating with... more For non-expert users, a textual query is the most popular and simple means for communicating with a retrieval or question answering system.However, there is a risk of receiving queries which do not match with the background knowledge.Query expansion and query rewriting are solutions for this problem but they are in danger of potentially yielding a large number of irrelevant words, which in turn negatively influences runtime as well as accuracy.In this paper, we propose a new method for automatic rewriting input queries on graph-structured RDF knowledge bases.We employ a Hidden Markov Model to determine the most suitable derived words from linguistic resources.We introduce the concept of triple-based co-occurrence for recognizing co-occurred words in RDF data.This model was bootstrapped with three statistical distributions.Our experimental study demonstrates the superiority of the proposed approach to the traditional n-gram model.

Research paper thumbnail of Defining and detecting toxicity on social media: context and knowledge are key

Research paper thumbnail of Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

Proceedings of the 28th International Conference on Computational Linguistics, 2020

Research paper thumbnail of Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 2017

Research paper thumbnail of Listed for sale: Analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket

Drug and Alcohol Dependence, 2020

Research paper thumbnail of “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma

JMIR Pediatrics and Parenting, 2018

Research paper thumbnail of Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio

Computational and Mathematical Organization Theory, 2018

Research paper thumbnail of On Using the Intelligent Edge for IoT Analytics

IEEE Intelligent Systems, 2017

Research paper thumbnail of Information Brokering Across Heterogeneous Digital Data

Advances in Database Systems, 2002

Research paper thumbnail of Gleaning Types for Literals in RDF Triples with Application to Entity Summarization

Lecture Notes in Computer Science, 2016

Research paper thumbnail of Analyzing the social media footprint of street gangs

2015 IEEE International Conference on Intelligence and Security Informatics (ISI), 2015

Research paper thumbnail of On applying classification to schema integration

[1991] Proceedings. First International Workshop on Interoperability in Multidatabase Systems

Research paper thumbnail of Data processing and semantics for advanced internet of things (IoT) applications

Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, 2013

ABSTRACT This tutorial presents tools and techniques for effectively utilizing the Internet of Th... more ABSTRACT This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analysis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.

Research paper thumbnail of Executing multidatabase transactions

Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences, 1992

Research paper thumbnail of Growing Fields of Interest - Using an Expand and Reduce Strategy for Domain Model Extraction

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008

Research paper thumbnail of Semantic Analytics in Intelligence

Advanced Topics in Database Research, Volume 5

Research paper thumbnail of On Using WS-Policy, Ontology, and Rule Reasoning to Discover Web Services

Lecture Notes in Computer Science, 2004

Research paper thumbnail of Semantic Information Brokering: How Can a Multi-agent Approach Help?

Lecture Notes in Computer Science, 1999

Research paper thumbnail of An Ontological Approach to the Document Access Problem of Insider Threat

Intelligence and Security Informatics, 2005

Research paper thumbnail of Discovering Fine-grained Sentiment in Suicide Notes

Biomedical Informatics Insights, 2012

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid syst... more This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams.