Automatic extraction of facts, relations, and entities for web-scale knowledge base population

Equipping machines with knowledge, through the construction of machinereadable knowledge bases, presents a key asset for semantic search, machine translation, question answering, and other formidable challenges in artificial intelligence. However, human knowledge predominantly resides in books and other natural language text forms. This means that knowledge bases must be extracted and synthesized from natural language text. When the source of text is the Web, extraction methods must cope with ambiguity, noise, scale, and updates. The goal of this dissertation is to develop knowledge base population methods that address the afore mentioned characteristics of Web text. The dissertation makes three contributions. The first contribution is a method for mining high-quality facts at scale, through distributed constraint reasoning and a pattern representation model that is robust against noisy patterns. The second contribution is a method for mining a large comprehensive collection of relation types beyond those commonly found in existing knowledge bases. The third contribution is a method for extracting facts from dynamic Web sources such as news articles and social media where one of the key challenges is the constant emergence of new entities. All methods have been evaluated through experiments involving Web-scale text collections.Maschinenlesbare Wissensbasen sind ein zentraler Baustein für semantische Suche, maschinelles Übersetzen, automatisches Beantworten von Fragen und andere komplexe Fragestellungen der Künstlichen Intelligenz. Allerdings findet man menschliches Wissen bis dato überwiegend in Büchern und anderen natürlichsprachigen Texten. Das hat zur Folge, dass Wissensbasen durch automatische Extraktion aus Texten erstellt werden müssen. Bei Texten aus dem Web müssen Extraktionsmethoden mit einem hohen Maß an Mehrdeutigkeit und Rauschen sowie mit sehr großen Datenvolumina und häufiger Aktualisierung zurechtkommen. Das Ziel dieser Dissertation ist, Methoden zu entwickeln, die die automatische Erstellung von Wissensbasen unter den zuvor genannten Unwägbarkeiten von Texten aus dem Web ermöglichen. Die Dissertation leistet dazu drei Beiträge. Der erste Beitrag ist ein skalierbar verteiltes Verfahren, das die effiziente Extraktion hochwertiger Fakten unterstützt, indem logische Inferenzen mit robuster Textmustererkennung kombiniert werden. Der zweite Beitrag der Arbeit ist eine Methodik zur automatischen Konstruktion einer umfassenden Sammlung typisierter Relationen, die weit über die in existierenden Wissensbasen bekannten Relationen hinausgeht. Der dritte Beitrag ist ein neuartiges Verfahren zur Extraktion von Fakten aus dynamischen Webinhalten wie Nachrichtenartikeln und sozialen Medien. Insbesondere werden Lösungen vorgestellt zur Erkennung und Registrierung neuer Entitäten, die bislang in keiner Wissenbasis enthalten sind. Alle Verfahren wurden durch umfassende Experimente auf großen Text und Webkorpora evaluiert

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

Notes on Randomized Algorithms

Lecture notes for the Yale Computer Science course CPSC 469/569 Randomized Algorithms. Suitable for use as a supplementary text for an introductory graduate or advanced undergraduate course on randomized algorithms. Discusses tools from probability theory, including random variables and expectations, union bound arguments, concentration bounds, applications of martingales and Markov chains, and the Lov\'asz Local Lemma. Algorithmic topics include analysis of classic randomized algorithms such as Quicksort and Hoare's FIND, randomized tree data structures, hashing, Markov chain Monte Carlo sampling, randomized approximate counting, derandomization, quantum computing, and some examples of randomized distributed algorithms

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum

Efficient Data Structures for Partial Orders, Range Modes, and Graph Cuts

This thesis considers the study of data structures from the perspective of the theoretician, with a focus on simplicity and practicality. We consider both the time complexity as well as space usage of proposed solutions. Topics discussed fall in three main categories: partial order representation, range modes, and graph cuts. We consider two problems in partial order representation. The first is a data structure to represent a lattice. A lattice is a partial order where the set of elements larger than any two elements x and y are all larger than an element z, known as the join of x and y; a similar condition holds for elements smaller than any two elements. Our data structure is the first correct solution that can simultaneously compute joins and the inverse meet operation in sublinear time while also using subquadratic space. The second is a data structure to support queries on a dynamic set of one-dimensional ordered data; that is, essentially any operation computable on a binary search tree. We develop a data structure that is able to interpolate between binary search trees and efficient priority queues, offering more-efficient insertion times than the former when query distribution is non-uniform. We also consider static and dynamic exact and approximate range mode. Given one-dimensional data, the range mode problem is to compute the mode of a subinterval of the data. In the dynamic range mode problem, insertions and deletions are permitted. For the approximate problem, the element returned is to have frequency no less than a factor (1+epsilon) of the true mode, for some epsilon > 0. Our results include a linear-space dynamic exact range mode data structure that simultaneously improves on best previous operation complexity and an exact dynamic range mode data structure that breaks the Theta(n^(2/3)) time per operation barrier. For approximate range mode, we develop a static succinct data structure offering a logarithmic-factor space improvement and give the first dynamic approximate range mode data structure. We also consider approximate range selection. The final category discussed is graph and dynamic graph algorithms. We develop an optimal offline data structure for dynamic 2- and 3- edge and vertex connectivity. Here, the data structure is given the entire sequence of operations in advance, and the dynamic operations are edge insertion and removal. Finally, we give a simplification of Karger's near-linear time minimum cut algorithm, utilizing heavy-light decomposition and iteration in place of dynamic programming in the subroutine to find a minimum cut of a graph G that cuts at most two edges of a spanning tree T of G

Calibración de un algoritmo de detección de anomalías marítimas basado en la fusión de datos satelitales

La fusión de diferentes fuentes de datos aporta una ayuda significativa en el proceso de toma de decisiones. El presente artículo describe el desarrollo de una plataforma que permite detectar anomalías marítimas por medio de la fusión de datos del Sistema de Información Automática (AIS) para seguimiento de buques y de imágenes satelitales de Radares de Apertura Sintética (SAR). Estas anomalías son presentadas al operador como un conjunto de detecciones que requieren ser monitoreadas para descubrir su naturaleza. El proceso de detección se lleva adelante primero identificando objetos dentro de las imágenes SAR a través de la aplicación de algoritmos CFAR, y luego correlacionando los objetos detectados con los datos reportados mediante el sistema AIS. En este trabajo reportamos las pruebas realizadas con diferentes configuraciones de los parámetros para los algoritmos de detección y asociación, analizamos la respuesta de la plataforma y reportamos la combinación de parámetros que reporta mejores resultados para las imágenes utilizadas. Este es un primer paso en nuestro objetivo futuro de desarrollar un sistema que ajuste los parámetros en forma dinámica dependiendo de las imágenes disponibles.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Bowdoin Orient v.139, no.1-26 (2009-2010)

https://digitalcommons.bowdoin.edu/bowdoinorient-2010s/1000/thumbnail.jp