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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Ontology Population Using Human Computation

Evirgen, Gencay Kemal 01 January 2010 (has links) (PDF)
In recent years, many researchers have developed new techniques on ontology population. However, these methods cannot overcome the semantic gap between humans and the extracted ontologies. Words-Around is a web application that forms a user-friendly environment which channels the vast Internet population to provide data towards solving ontology population problem that no known efficient computer algorithms can yet solve. This application&rsquo / s fundamental data structure is a list of words that people naturally link to each other. It displays these lists as a word cloud that is fun to drag around and play with. Users are prompted to enter whatever word comes to their mind upon seeing a word that is suggested from the application&rsquo / s database / or they can search for one word in particular to see what associations other users have made to it. Once logged in, users can view their activity history, which words they were the first to associate, and mark particular words as misspellings or as junk, to help keep the list&rsquo / s structure to be relevant and accurate. The results of this implementation indicate the fact that an interesting application that enables users just to play with its visual elements can also be useful to gather information.
2

Enrichissement et peuplement d’ontologie à partir de textes et de données du LOD : Application à l’annotation automatique de documents / Ontology enrichment and population from texts and data from LOD : Application to automatic annotation of documents

Alec, Céline 26 September 2016 (has links)
Cette thèse traite d'une approche, guidée par une ontologie, conçue pour annoter les documents d'un corpus où chaque document décrit une entité de même type. Dans notre contexte, l'ensemble des documents doit être annoté avec des concepts qui sont en général trop spécifiques pour être explicitement mentionnés dans les textes. De plus, les concepts d'annotation ne sont représentés au départ que par leur nom, sans qu'aucune information sémantique ne leur soit reliée. Enfin, les caractéristiques des entités décrites dans les documents sont incomplètes. Pour accomplir ce processus particulier d'annotation de documents, nous proposons une approche nommée SAUPODOC (Semantic Annotation Using Population of Ontology and Definitions of Concepts) qui combine plusieurs tâches pour (1) peupler et (2) enrichir une ontologie de domaine. La phase de peuplement (1) ajoute dans l'ontologie des informations provenant des documents du corpus mais aussi du Web des données (Linked Open Data ou LOD). Le LOD représente aujourd'hui une source prometteuse pour de très nombreuses applications du Web sémantique à condition toutefois de développer des techniques adaptées d'acquisition de données. Dans le cadre de SAUPODOC, le peuplement de l'ontologie doit tenir compte de la diversité des données présentes dans le LOD : propriétés multiples, équivalentes, multi-valuées ou absentes. Les correspondances à établir, entre le vocabulaire de l'ontologie à peupler et celui du LOD, étant complexes, nous proposons un modèle pour faciliter leur spécification. Puis, nous montrons comment ce modèle est utilisé pour générer automatiquement des requêtes SPARQL et ainsi faciliter l'interrogation du LOD et le peuplement de l'ontologie. Celle-ci, une fois peuplée, est ensuite enrichie(2) avec les concepts d'annotation et leurs définitions qui sont apprises grâce à des exemples de documents annotés. Un raisonnement sur ces définitions permet enfin d'obtenir les annotations souhaitées. Des expérimentations ont été menées dans deux domaines d'application, et les résultats, comparés aux annotations obtenues avec des classifieurs, montrent l'intérêt de l'approche. / This thesis deals with an approach, guided by an ontology, designed to annotate documents from a corpus where each document describes an entity of the same type. In our context, all documents have to be annotated with concepts that are usually too specific to be explicitly mentioned in the texts. In addition, the annotation concepts are represented initially only by their name, without any semantic information connected to them. Finally, the characteristics of the entities described in the documents are incomplete. To accomplish this particular process of annotation of documents, we propose an approach called SAUPODOC (Semantic Annotation of Population Using Ontology and Definitions of Concepts) which combines several tasks to (1) populate and (2) enrich a domain ontology. The population step (1) adds to the ontology information from the documents in the corpus but also from the Web of Data (Linked Open Data or LOD). The LOD represents today a promising source for many applications of the Semantic Web, provided that appropriate techniques of data acquisition are developed. In the settings of SAUPODOC, the ontology population has to take into account the diversity of the data in the LOD: multiple, equivalent, multi-valued or absent properties. The correspondences to be established, between the vocabulary of the ontology to be populated and that of the LOD, are complex, thus we propose a model to facilitate their specification. Then, we show how this model is used to automatically generate SPARQL queries and facilitate the interrogation of the LOD and the population of the ontology. The latter, once populated, is then enriched (2) with the annotation concepts and definitions that are learned through examples of annotated documents. Reasoning on these definitions finally provides the desired annotations. Experiments have been conducted in two areas of application, and the results, compared with the annotations obtained with classifiers, show the interest of the approach.
3

APPONTO-PRO: um processo incremental para o aprendizado e povoamento de ontologias de aplicação / APPONTO-PRO: an incremental process for learning and population of ontologies of application

Santos, Suzane Carvalho dos 18 August 2014 (has links)
Made available in DSpace on 2016-08-17T14:53:28Z (GMT). No. of bitstreams: 1 Suzane Carvalho dos Santos.pdf: 4549168 bytes, checksum: 85d08a343bc93d5bf241da9f6f02f5b4 (MD5) Previous issue date: 2014-08-18 / Ontologies are knowledge representation structures capable of expressing a set of entities of a domain, their relationships and axioms that are being used by modern knowledge based systems (KBS) in the decision making process. However, manual construction of ontology is expensive and subject to errors, thus a viable alternative is the automation of this process. Several techniques and tools have been developed to learn the different components of an ontology from textual sources, named concepts, hierarchies, instances, relationships, properties and axioms. However, these elements are generally acquired in a isolated manner. Due to the lack of approaches to acquire all the elements of an ontology jointly, there is a need to develop a process to make the reuse and the learning of each of the elements of an ontology in a synergistic manner. To attend this need, this work presents Apponto-Pro, an incremental learning process for populating application ontologies from textual information sources that is capable of generating a complete ontology through the integration of different techniques to generate isolated elements of an ontology. The process was evaluated through a case study that consisted in the automatic construction of Family_Law, an application ontology in the field of family law developed with Apponto-ProTool, a software tool to support Apponto-Pro that integrates the approaches that compound the whole process. This evaluation aimed to determine the effectiveness of the ontology constructed with Apponto-ProTool against an ontology manually built by a domain specialist and used as reference ontology. For this reason, the "precision"was calculated for the elements of the ontology automatically generated using the reference ontology. As a result it was found that in some cases the ontology developed with Apponto-ProTool tends to present more suitable results. / As ontologias são estruturas de representação de conhecimento capazes de expressar um conjunto de entidades de um dado domínio, seus relacionamentos e axiomas, sendo utilizadas pelos modernos Sistemas Baseados em Conhecimento (SBC) no processo de tomada de decisões. No entanto, a construção manual de ontologias é cara e sujeita a erros, sendo uma alternativa viável a sua construção de forma automática. Diversas técnicas e ferramentas têm sido desenvolvidas para aprender os diferentes componentes de uma ontologia a partir de fontes textuais, quais sejam conceitos, hierarquias, instâncias, relacionamentos, propriedades e axiomas. Entretanto estes elementos são, em regra, adquidiros de forma isolada. Devido à carência de abordagens que adquirem todos os elementos de uma ontologia de forma conjunta, surgiu a necessidade de desenvolver um processo que faça o reúso e a aprendizagem de cada um dos elementos de uma ontologia de forma completa. Atendendo a esta necessidade, este trabalho apresenta o Apponto-Pro, um processo incremental para o aprendizado e povoamento de ontologias de aplicação a partir de fontes de informação textuais capaz de gerar uma ontologia completa através da integração de diferentes técnicas que geram elementos da ontologia de forma isolada. O processo foi avalizado através de um estudo de caso que consistiu na construção automática da Family_Law, uma ontologia de aplicação no domínio do Direito da Família construída através da aplicação da ferramenta de software Apponto-ProTool, desenvolvida para dar suporte ao processo Apponto-Pro que integrou as ferramentas correspondentes as abordagens contidas no processo. Esta avaliação teve como objetivo verificar a efetividade da ontologia construída pela Apponto-ProTool em relação a uma ontologia construída manualmente por um especialista do domínio e utilizada como ontologia de referência. Para isso foi calculado o valor da medida "precision" para os elementos da ontologia construída utilizando a ontologia de referência. Como resultado verificou-se formalmente que em alguns casos a ontologia desenvolvida pela Apponto-ProTool tende a apresentar resultados mais adequados.
4

UM PROCESSO INDEPENDENTE DE DOMÍNIO PARA O POVOAMENTO AUTOMÁTICO DE ONTOLOGIAS A PARTIR DE FONTES TEXTUAIS / AN INDEPENDENT PROCESS OF DOMAIN FOR THE ONTOLOGY AUTOMATIC POPULATION STARTING FROM TEXTUAL SOURCES

Alves, Carla Gomes de Faria 05 June 2013 (has links)
Made available in DSpace on 2016-08-17T16:54:32Z (GMT). No. of bitstreams: 1 Tese Carla.pdf: 23507425 bytes, checksum: b08fca6c8eacdc0fd5d075a385f235e5 (MD5) Previous issue date: 2013-06-05 / Knowledge systems are a suitable computational approach to solve complex problems and to provide decision support. Ontologies are an approach for knowledge representation about an application domain, allowing the semantic processing of information and, through more precise interpretation of information, turning systems more effective and usable. Ontology Population looks for instantiating the constituent elements of an ontology, like properties and non-taxonomic relationships. Manual population by domain experts and knowledge engineers is an expensive and time consuming task. Fast ontology population is critical for the success of knowledge-based applications. Thus, automatic or semi-automatic approaches are needed. This work proposes a generic process for Automatic Ontology Population by specifying its phases and the techniques used to perform the activities on each phase. It also proposes a domain-independent process for automatic population of ontologies (DIAOPPro) from text that applies natural language processing and information extraction techniques to acquire and classify ontology instances. This is a new approach for automatic ontology population that uses an ontology to automatically generate rules to extract instances from text and classify them in ontology classes. These rules can be generated from ontologies of any domain, making the proposed process domain independent. To evaluate DIAOP-Pro four case studies were conducted to demonstrate its effectiveness and feasibility. In the first one we evaluated the effectiveness of phase "Identification of Candidate instances" comparing the results obtained by applying statistical techniques with those of purely linguistic techniques. In the second experiment we evaluated the feasibility of the phase "Construction of a Classifier", through the automatic generation of a classifier. The last two experiments evaluated the effectiveness of DIAOP-Pro into two distinct domains: the legal and the tourism domains. The results indicate that our approach can extract and classify instances with high effectiveness with the additional advantage of domain independence. / A demanda por sistemas baseado em conhecimento é crescente considerando suas aptidões para a solução de problemas complexos e para a tomada de decisão. As ontologias são formalismos para a representação de conhecimento de um dado domínio, que permitem o processamento semântico das informações e, através de interpretações mais precisas das informações, os sistemas apresentam maior efetividade e usabilidade. O povoamento de ontologias visa a instanciação de propriedades e relacionamentos não taxonômicos de classes de ontologias. Entretanto, o povoamento manual de ontologias por especialistas de domínio e engenheiros do conhecimemto é uma tarefa cara e que consome muito tempo. O povoamento de ontologias rápido e com baixo custo é crucial para o sucesso de aplicações baseadas em conhecimento. Portanto, torna-se fundamental uma semi-automatização ou automatização desse processo. Esta tese propõe um processo genérico para o problema do Povoamento Automático de Ontologias, especificando suas fases e técnicas que podem ser aplicadas em cada uma delas. É também proposto um Processo Independente de Domínio para o Povoamento Automático de Ontologias (DIAOP-Pro) a partir de fontes textuais, que aplica técnicas de processamento da linguagem natural e extração de informação para adquirir e classificar instâncias de ontologias. O DIAOP-Pro se constitui em uma abordagem original uma vez que propõe o povoamento automático de ontologias utilizando uma ontologia para a geração automática de regras para extrair instâncias a partir de textos e classifica-as como instâncias de classes da ontologia. Estas regras podem ser geradas a partir de ontologias específicas de qualquer domínio, tornando o processo independente de domínio. Para avaliar o processo DIAOP-Pro foram conduzidos quatro estudos de caso de modo a demonstrar a sua efetividade e viabilidade. O primeiro estudo de caso foi realizado para avaliar a efetividade da fase Identificação de Instâncias Candidatas , no qual foram comparados os resultados obtidos com a aplicação de técnicas estatísticas e de técnicas puramente lingüísticas. O segundo estudo de caso foi realizado para avaliar a viabiliadade da fase Construção de um Classificador , através da experimentação com a geração automática do classificador. O terceiro e o quarto estudo de caso foram realizados para avaliar a efetividade do processo proposto em dois domínios distintos, o jurídico e o turístico. Os resultados indicam que o processo DIAOP-Pro povoa ontologias específicas de qualquer domínio com boa efetividade e com a vantagem adicional da independência do domínio.
5

Représentation OWL de la ressource lexicale LVF et son utilisation dans le traitement automatique de la langue

Abdi, Radia 09 1900 (has links)
Le dictionnaire LVF (Les Verbes Français) de J. Dubois et F. Dubois-Charlier représente une des ressources lexicales les plus importantes dans la langue française qui est caractérisée par une description sémantique et syntaxique très pertinente. Le LVF a été mis disponible sous un format XML pour rendre l’accès aux informations plus commode pour les applications informatiques telles que les applications de traitement automatique de la langue française. Avec l’émergence du web sémantique et la diffusion rapide de ses technologies et standards tels que XML, RDF/RDFS et OWL, il serait intéressant de représenter LVF en un langage plus formalisé afin de mieux l’exploiter par les applications du traitement automatique de la langue ou du web sémantique. Nous en présentons dans ce mémoire une version ontologique OWL en détaillant le processus de transformation de la version XML à OWL et nous en démontrons son utilisation dans le domaine du traitement automatique de la langue avec une application d’annotation sémantique développée dans GATE. / The LVF dictionary (Les Verbes Français) by J. Dubois and F. Dubois-Charlier is one of the most important lexical resources in the French language, which is characterized by a highly relevant semantic and syntactic description. The LVF has been available in an XML format to make access to information more convenient for computer applications such as NLP applications for French language. With the emergence of the Semantic Web and the rapid diffusion of its technologies and standards such as XML, RDF/RDFS and OWL, it would be interesting to represent LVF in a more formalized format for a better and more sophisticated usage by natural language processing and semantic web applications. We present in this paper an OWL ontology version of LVF by demonstrating the mapping process between the data model elements of the XML version and OWL. We give account about its use in the field of natural language processing by presenting an application of semantic annotation developed in GATE.
6

Représentation OWL de la ressource lexicale LVF et son utilisation dans le traitement automatique de la langue

Abdi, Radia 09 1900 (has links)
Le dictionnaire LVF (Les Verbes Français) de J. Dubois et F. Dubois-Charlier représente une des ressources lexicales les plus importantes dans la langue française qui est caractérisée par une description sémantique et syntaxique très pertinente. Le LVF a été mis disponible sous un format XML pour rendre l’accès aux informations plus commode pour les applications informatiques telles que les applications de traitement automatique de la langue française. Avec l’émergence du web sémantique et la diffusion rapide de ses technologies et standards tels que XML, RDF/RDFS et OWL, il serait intéressant de représenter LVF en un langage plus formalisé afin de mieux l’exploiter par les applications du traitement automatique de la langue ou du web sémantique. Nous en présentons dans ce mémoire une version ontologique OWL en détaillant le processus de transformation de la version XML à OWL et nous en démontrons son utilisation dans le domaine du traitement automatique de la langue avec une application d’annotation sémantique développée dans GATE. / The LVF dictionary (Les Verbes Français) by J. Dubois and F. Dubois-Charlier is one of the most important lexical resources in the French language, which is characterized by a highly relevant semantic and syntactic description. The LVF has been available in an XML format to make access to information more convenient for computer applications such as NLP applications for French language. With the emergence of the Semantic Web and the rapid diffusion of its technologies and standards such as XML, RDF/RDFS and OWL, it would be interesting to represent LVF in a more formalized format for a better and more sophisticated usage by natural language processing and semantic web applications. We present in this paper an OWL ontology version of LVF by demonstrating the mapping process between the data model elements of the XML version and OWL. We give account about its use in the field of natural language processing by presenting an application of semantic annotation developed in GATE.
7

Ontoilper: an ontology- and inductive logic programming-based method to extract instances of entities and relations from texts

Lima, Rinaldo José de, Freitas, Frederico Luiz Gonçalves de 31 January 2014 (has links)
Submitted by Nayara Passos (nayara.passos@ufpe.br) on 2015-03-13T12:33:46Z No. of bitstreams: 2 TESE Rinaldo José de Lima.pdf: 8678943 bytes, checksum: e88c290e414329ee00d2d6a35a466de0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Approved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T13:16:54Z (GMT) No. of bitstreams: 2 TESE Rinaldo José de Lima.pdf: 8678943 bytes, checksum: e88c290e414329ee00d2d6a35a466de0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-13T13:16:54Z (GMT). No. of bitstreams: 2 TESE Rinaldo José de Lima.pdf: 8678943 bytes, checksum: e88c290e414329ee00d2d6a35a466de0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2014 / CNPq, CAPES. / Information Extraction (IE) consists in the task of discovering and structuring information found in a semi-structured or unstructured textual corpus. Named Entity Recognition (NER) and Relation Extraction (RE) are two important subtasks in IE. The former aims at finding named entities, including the name of people, locations, among others, whereas the latter consists in detecting and characterizing relations involving such named entities in text. Since the approach of manually creating extraction rules for performing NER and RE is an intensive and time-consuming task, researchers have turned their attention to how machine learning techniques can be applied to IE in order to make IE systems more adaptive to domain changes. As a result, a myriad of state-of-the-art methods for NER and RE relying on statistical machine learning techniques have been proposed in the literature. Such systems typically use a propositional hypothesis space for representing examples, i.e., an attribute-value representation. In machine learning, the propositional representation of examples presents some limitations, particularly in the extraction of binary relations, which mainly demands not only contextual and relational information about the involving instances, but also more expressive semantic resources as background knowledge. This thesis attempts to mitigate the aforementioned limitations based on the hypothesis that, to be efficient and more adaptable to domain changes, an IE system should exploit ontologies and semantic resources in a framework for IE that enables the automatic induction of extraction rules by employing machine learning techniques. In this context, this thesis proposes a supervised method to extract both entity and relation instances from textual corpora based on Inductive Logic Programming, a symbolic machine learning technique. The proposed method, called OntoILPER, benefits not only from ontologies and semantic resources, but also relies on a highly expressive relational hypothesis space, in the form of logical predicates, for representing examples whose structure is relevant to the information extraction task. OntoILPER automatically induces symbolic extraction rules that subsume examples of entity and relation instances from a tailored graph-based model of sentence representation, another contribution of this thesis. Moreover, this graph-based model for representing sentences also enables the exploitation of domain ontologies and additional background knowledge in the form of a condensed set of features including lexical, syntactic, semantic, and relational ones. Differently from most of the IE methods (a comprehensive survey is presented in this thesis, including the ones that also apply ILP), OntoILPER takes advantage of a rich text preprocessing stage which encompasses various shallow and deep natural language processing subtasks, including dependency parsing, coreference resolution, word sense disambiguation, and semantic role labeling. Further mappings of nouns and verbs to (formal) semantic resources are also considered. OntoILPER Framework, the OntoILPER implementation, was experimentally evaluated on both NER and RE tasks. This thesis reports the results of several assessments conducted using six standard evaluationcorpora from two distinct domains: news and biomedical. The obtained results demonstrated the effectiveness of OntoILPER on both NER and RE tasks. Actually, the proposed framework outperforms some of the state-of-the-art IE systems compared in this thesis. / A área de Extração de Informação (IE) visa descobrir e estruturar informações dispostas em documentos semi-estruturados ou desestruturados. O Reconhecimento de Entidades Nomeadas (REN) e a Extração de Relações (ER) são duas subtarefas importantes em EI. A primeira visa encontrar entidades nomeadas, incluindo nome de pessoas e lugares, entre outros; enquanto que a segunda, consiste na detecção e caracterização de relações que envolvem as entidades nomeadas presentes no texto. Como a tarefa de criar manualmente as regras de extração para realizar REN e ER é muito trabalhosa e onerosa, pesquisadores têm voltado suas atenções na investigação de como as técnicas de aprendizado de máquina podem ser aplicadas à EI a fim de tornar os sistemas de ER mais adaptáveis às mudanças de domínios. Como resultado, muitos métodos do estado-da-arte em REN e ER, baseados em técnicas estatísticas de aprendizado de máquina, têm sido propostos na literatura. Tais sistemas normalmente empregam um espaço de hipóteses com expressividade propositional para representar os exemplos, ou seja, eles são baseado na tradicional representação atributo-valor. Em aprendizado de máquina, a representação proposicional apresenta algums fatores limitantes, principalmente na extração de relações binárias que exigem não somente informações contextuais e estruturais (relacionais) sobre as instâncias, mas também outras formas de como adicionar conhecimento prévio do problema durante o processo de aprendizado. Esta tese visa atenuar as limitações acima mencionadas, tendo como hipótese de trabalho que, para ser eficiente e mais facilmente adaptável às mudanças de domínio, os sistemas de EI devem explorar ontologias e recursos semânticos no contexto de um arcabouço para EI que permita a indução automática de regras de extração de informação através do emprego de técnicas de aprendizado de máquina. Neste contexto, a presente tese propõe um método supervisionado capaz de extrair instâncias de entidades (ou classes de ontologias) e de relações a partir de textos apoiando-se na Programação em Lógica Indutiva (PLI), uma técnica de aprendizado de máquina supervisionada capaz de induzir regras simbólicas de classificação. O método proposto, chamado OntoILPER, não só se beneficia de ontologias e recursos semânticos, mas também se baseia em um expressivo espaço de hipóteses, sob a forma de predicados lógicos, capaz de representar exemplos cuja estrutura é relevante para a tarefa de EI consideradas nesta tese. OntoILPER automaticamente induz regras simbólicas para classificar exemplos de instâncias de entidades e relações a partir de um modelo de representação de frases baseado em grafos. Tal modelo de representação é uma das constribuições desta tese. Além disso, o modelo baseado em grafos para representação de frases e exemplos (instâncias de classes e relações) favorece a integração de conhecimento prévio do problema na forma de um conjunto reduzido de atributos léxicos, sintáticos, semânticos e estruturais. Diferentemente da maioria dos métodos de EI (uma pesquisa abrangente é apresentada nesta tese, incluindo aqueles que também se aplicam a PLI), OntoILPER faz uso de várias subtarefas do Processamento de Linguagem
8

Scalable Detection and Extraction of Data in Lists in OCRed Text for Ontology Population Using Semi-Supervised and Unsupervised Active Wrapper Induction

Packer, Thomas L 01 October 2014 (has links) (PDF)
Lists of records in machine-printed documents contain much useful information. As one example, the thousands of family history books scanned, OCRed, and placed on-line by FamilySearch.org probably contain hundreds of millions of fact assertions about people, places, family relationships, and life events. Data like this cannot be fully utilized until a person or process locates the data in the document text, extracts it, and structures it with respect to an ontology or database schema. Yet, in the family history industry and other industries, data in lists goes largely unused because no known approach adequately addresses all of the costs, challenges, and requirements of a complete end-to-end solution to this task. The diverse information is costly to extract because many kinds of lists appear even within a single document, differing from each other in both structure and content. The lists' records and component data fields are usually not set apart explicitly from the rest of the text, especially in a corpus of OCRed historical documents. OCR errors and the lack of document structure (e.g. HMTL tags) make list content hard to recognize by a software tool developed without a substantial amount of highly specialized, hand-coded knowledge or machine learning supervision. Making an approach that is not only accurate but also sufficiently scalable in terms of time and space complexity to process a large corpus efficiently is especially challenging. In this dissertation, we introduce a novel family of scalable approaches to list discovery and ontology population. Its contributions include the following. We introduce the first general-purpose methods of which we are aware for both list detection and wrapper induction for lists in OCRed or other plain text. We formally outline a mapping between in-line labeled text and populated ontologies, effectively reducing the ontology population problem to a sequence labeling problem, opening the door to applying sequence labelers and other common text tools to the goal of populating a richly structured ontology from text. We provide a novel admissible heuristic for inducing regular expression wrappers using an A* search. We introduce two ways of modeling list-structured text with a hidden Markov model. We present two query strategies for active learning in a list-wrapper induction setting. Our primary contributions are two complete and scalable wrapper-induction-based solutions to the end-to-end challenge of finding lists, extracting data, and populating an ontology. The first has linear time and space complexity and extracts highly accurate information at a low cost in terms of user involvement. The second has time and space complexity that are linear in the size of the input text and quadratic in the length of an output record and achieves higher F1-measures for extracted information as a function of supervision cost. We measure the performance of each of these approaches and show that they perform better than strong baselines, including variations of our own approaches and a conditional random field-based approach.

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