Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

&nbsp;

IIT Palakkad

Quantity queries, with filter conditions on quantitative measures of entities, are beyond the functionality of search engines and QA assistants. To enable such queries over web contents, this paper develops a novel method for automatically extracting quantity facts from ad-hoc web tables. This involves recognizing quantities, with normalized values and units, aligning them with the proper entities and contextualizing these pairs with informative cues to match sophisticated queries with modifiers. Our method includes a new approach to aligning quantity columns to entity columns. Prior works assumed a single subject-column per table, whereas our approach is geared for complex tables and leverages external corpora as evidence. For contextualization, we identify informative cues from text and structural markup that surrounds a table. For query-time fact ranking, we devise a new scoring technique that exploits both context similarity and inter-fact consistency. Comparisons of our building blocks against state-of-the-art baselines and extrinsic experiments with two query benchmarks demonstrate the benefits of our method.

World University root node

Department of Data Science

The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021

Extracting contextualized quantity facts from web tables

Quantities are financial, technological, physical and other measures that denote relevant properties of entities, such as revenue of companies, energy efficiency of cars or distance and brightness of stars and galaxies. Queries with filter conditions on quantities are an important building block for downstream analytics and pose challenges when the content of interest is spread across a huge number of web tables and other ad-hoc datasets. Search engines support quantity lookups, but largely fail on quantity filters. The QuTE system presented in this paper aims to overcome these problems. It comprises methods for automatically extracting entity-quantity facts from web tables, as well as methods for online query processing, with new techniques for query matching and answer ranking.

Proceedings of the ACM SIGMOD International Conference on Management of Data

QuTE: Answering Quantity Queries from Web Tables

Similar facts are often expressed in different ways in natural language text, which introduces the redundancy and ambiguity of Subject-Predicate-Object (SPO) triples in Open Information Extraction (Open IE). This work focuses on canonicalizing such SPO triples. We propose a clustering framework using non-negative matrix tri-factorization that jointly clusters predicate phrases and subject-object pairs and aligns them in a meaningful manner. The evaluation shows that our co-clustering method outperforms significantly over rule mining and Knowledge-Base-embedding approaches for two existing datasets.

PervasiveHealth: Pervasive Computing Technologies for Healthcare

Co-clustering triples from open information extraction

Quantities are more than numeric values. They represent measures for entities, expressed in numbers with associated units. Search queries often include quantities, such as athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. Processing such queries requires understanding the quantities, where capturing the surrounding context is an essential part of it. Although modern search engines or QA systems handle entity-centric queries well, they consider numbers and units as simple keywords and therefore fail to understand the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.) and the context of the quantity (200m race, quarterly revenue, etc.) As a result, they cannot generate the correct candidate answers. In this work, we demonstrate a prototype QA system, called Qsearch, that can handle advanced queries with quantity constraints using the common cues present in both query and the text sources.

WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining

Entities with quantities: Extraction, search and ranking

Quantities appear in search queries in numerous forms: companies with annual revenue of at least 50 Mio USD, athletes who ran 200 m faster than 19.5 s, electric cars with range above 400 miles and so on. Processing such queries requires the understanding of numbers present in the query to capture the contextual information about the queried entities. Modern search engines and QA systems can handle queries that involve entities and types, but they often fail on properly interpreting quantities in queries and candidate answers when the specifics of the search condition (less than, above, etc.), the units of interest (seconds, miles, meters, etc.) and the context of the quantity matter (annual or quarterly revenue, etc.). In this paper, we present a search and QA system, called Qsearch, that can effectively answer advanced queries with quantity conditions. Our solution is based on a deep neural network for extracting quantity-centric tuples from text sources and a novel matching model to retrieve and rank answers from news articles and other web pages. Experiments demonstrate the effectiveness of Qsearch on benchmark queries collected by crowdsourcing.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Qsearch: Answering Quantity Queries from Text

Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality.

International Conference on Information and Knowledge Management, Proceedings

Commonsense properties from query logs and question answering forums

Tables or ranked lists summarize facts about a group of entities in a concise and structured fashion. They are found in all kind of domains and easily comprehensible by humans. Some globally prominent examples of such rankings are the tallest buildings in the World, the {\^{a}}€¦

Mining and Querying Ranked Entities

This work proposes and evaluates a novel approach to determining interesting attributes, in order to categorize entities accordingly. Once identified, such categories are of immense value to allow constraining (filtering) a user's current view to subsets of entities. We show how a classifier is trained that is able to tell whether or not a categorical attribute can act as a constraint, in the sense of human-perceived interestingness. The training data is harnessed from Wikipedia tables, treating the presence or absence of a table as an indication that the attribute used as a filter constraint is reasonable or not. For learning the classification model, we review four well-known statistical measures (features) for categorical attributes—entropy, unalikeability, peculiarity and coverage. We additionally propose three new statistical measures to capture the distribution of data, tailored to our main objective. The learned model is evaluated by relevance assessments obtained through a user study, reflecting the applicability of the approach as a whole and, further, demonstrates the superiority of the proposed diversity measures over existing measures like information entropy.

ACM International Conference Proceeding Series

Learning interesting attributes for automated data categorization∗

This work proposes and evaluates a novel approach to determine interesting categorical attributes for lists of entities. Once identified, such categories are of immense value to allow constraining (filtering) a current view of a user to subsets of entities. We show how a classifier is trained that is able to tell whether or not a categorical attribute can act as a constraint, in the sense of human-perceived interestingness. The training data is harnessed from Web tables, treating the presence or absence of a table as an indication that the attribute used as a filter constraint is reasonable or not. For learning the classification model, we review four well-known statistical measures (features) for categorical attributes---entropy, unalikeability, peculiarity and coverage. We additionally propose three new statistical measures to capture the distribution of data, tailored to our main objective. The learned model is evaluated by relevance assessments obtained through a user study, reflecting the applicability of the approach as a whole and, further, demonstrates the superiority of the proposed diversity measures over existing statistical measures like information entropy.

arXiv preprint arXiv:1711.

Learning Interesting Categorical Attributes for Refined Data Exploration

Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)

Exploring databases via reverse engineering ranking queries with Paleo

We address the problem of index pruning without compromising the quality of ad-hoc similarity search among sets and ranked lists. We discuss three different ways to prune the index structure and, by linking the index structure with the concept of Locality Sensitive Hashing (LSH), we introduce two solutions to query processing over the pruned index. Through a probabilistic analysis we ensure that a user-defined recall goal is still guaranteed. We are able to formulate an optimization problem that can determine the optimal pruning factor for all three pruning methods. The experimental evaluations over real-world data validate that the optimal pruning factor indeed ensures the recall goal without any significant effect on the quality of similarity search on a much smaller index.

20th International Workshop on the Web and Databases, WebDB 2017 - Web of Opinions: Truths, Beliefs and Conficts

LSH-based probabilistic pruning of inverted indices for sets and ranked lists

Datenbank-Spektrum

Mining Entity Rankings

We consider the problem of similarity search in a set of topk lists under the generalized Kendall's Tau distance. This distance describes how related two rankings are in terms of discordantly ordered items. We consider pair and tripletsbased indices to counter the shortcomings of naive inverted indices and derive efficient query schemes by relating the proposed index structures to the concept of locality sensitive hashing (LSH). Specifically, we devise four different LSH schemes for Kendall's Tau using two generic hash families over individual elements or pairs of them. We show that each of these functions has the desired property of being locality sensitive. Further, we discuss the selection of hash functions for the proposed LSH schemes for a given query ranking, called query-driven LSH and derive bounds for the required number of hash functions to use in order to achieve a predefined recall goal. Experimental results, using two realworld datasets, show that the devised methods outperform the SimJoin method the state of the art method to query for similar sets and are far superior to a plain inverted index-based approach.

Efficient similarity search across Top-k Lists under the Kendall's Tau distance

This work proposes and evaluates a novel approach to determine interesting category for ranked lists using $\nu$-SVM. We identify three characteristics (features), entropy, unlikability and peculiarity and show how to train a classifier on these features using a set of Wikipedia tables. The learned model is evaluated by relevance assessments obtained through a user study, reflecting the correctness of our approach.

Advances in Database Technology - EDBT

A data mining approach to choosing categorical attributes for ranked lists

We consider the problem of similarity search within a set of top-k lists under the Kendall's Tau distance function. This distance describes how related two rankings are in terms of concordantly and discordantly ordered items. As top-k lists are usually very short compared to the global domain of possible items to be ranked, creating an inverted index to look up overlapping lists is possible but does not capture tight enough the similarity measure. In this work, we investigate locality sensitive hashing schemes for the Kendall's Tau distance and evaluate the proposed methods using two real-world datasets.

arXiv preprint arXiv:1409.

An LSH Index for Computing Kendall's Tau over Top-k Lists

Maintaining the satisfactory QoS, synchronization is a big challenge to the researcher in the&nbsp;field of Information and Communication Technology (ICT). In this paper we address a multimedia synchronization issue called multipoint synchronization which is necessary in some distributed collaborative application like teleteaching, teleconferencing etc.,involving the play out process of same media stream at different receivers at the same time to achieve fairness among the receivers. We found the in capability of existing petri net to model the above synchronization issue. Some new features are added in existing petri nets to increase its modeling and analyzing power, called self modifying stochastic color petri net (SMSCPN).Then the synchronization issue is modeled with the help of SMSCPN and an generalized algorithm is proposed to achieving multipoint synchronization in IP network. Also some metrics are defined that can measurethe performance of the proposed synchronization algorithm.

INFOCOMP

Multipoint MultimediaSynchronization: A Petri Net Based Approach

National Institute of Technology Durgapur, India

Synchronization and QoS Issues in Real Time Multimedia Communication in IP Network

Distributed multimedia application over IP network is an evolving paradigm for the researcher in the field of Information and Communication Technology (ICT). There are several applications of this technology - video conferencing, tele-teaching, telemedicine and any collaborative application. Maintaining the satisfactory Quality Of Service [1,2 ] and synchronization is a big challenge to the researcher in this field. In this paper, we address multipoint synchronization or inter destination synchronization as an issue, involving the play out process of same stream at different receivers at the same time to achieve fairness among the receivers. In this paper, we propose a distributed algorithm for achieving multipoint synchronization and we also model the algorithm with an extension of Petri Nets and verify its correctness. © 2011 IEEE.

2011 3rd International Conference on Communication Systems and Networks, COMSNETS 2011

A Petri Nets based model for multipoint synchronization in audio conferencing

Distributed multimedia application over IP network is an evolving paradigm for the researcher in the field of Information and Communication Technology (ICT). There are several applications of this technology -video conferencing, teleteaching and any collaborative application. Maintaining the satisfactory Quality of Service [1] and synchronization is a big challenge to the researchers in this field. In this paper, we extend our previous work [2] by ensur-ing multistream multipoint synchronization as an issue, involving the play out process of two streams (audio and video that were generated at the same time and bear temporal relationship ) at different receivers at the same time and maintaining the temporal relationship between them to achieve fairness among the receivers. In this paper, we propose a distributed algorithm for achieving multi-stream multipoint synchronization and we also model the algorithm with Stochastic Petri Nets and verify its correctness. {\textcopyright} 2011 Springer-Verlag.