Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org

Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org

Variation and Semantic Relation Interpretation: Linguistic and Processing Issues

International audienceStudies in linguistics define lexico-syntactic patterns to characterize the linguistic utterances that can be interpreted with semantic relations. Because patterns are assumed to reflect linguistic regularities that have a stable interpretation, several software implement such patterns to extract semantic relations from text. Nevertheless, a thorough analysis of pattern occurrences in various corpora proved that variation may affect their interpretation. In this paper, we report the linguistic variations that impact relation interpretation in language, and may lead to errors in relation extraction systems. We analyze several features of state-of-the-art pattern-based relation extraction tools, mostly how patterns are represented and matched with text, and discuss their role in the tool ability to manage variation

Methods of Definition Extraction and Linking for Food Recipes

This dissertation deals with definitional contexts extraction and automatic definitions linking in the Italian and English language. Definitional contexts extraction is a task that is not limited to glossaries and encyclopaediae, but has been addressed also in the field of Natural Language Processing. In this research, the objective is to identify definitional contexts in food-related Wikipedia articles. To set the basis of the work, we built two ad-hoc corpora out of the Italian and English dumps of Wikipedia. We trained two BERT models in a supervised fashion with a manually annotated dataset. The F1-measures of 96.08 and 97.66 testify the high performance. We then fed each model with 30 Wikipedia articles randomly extracted from the two corpora, one with Italian and one with English articles. We obtained the best results by restricting the selection to the first sentence of the article whose BERT positive score is above 0.6. The task of automatic definitions linking is loosely based on the wikification process. Rather than linking a term to its corresponding Wikipedia article, we aim at linking a term to its corresponding definition in a Wikipedia article. To lay the foundation of the task, we built two ad-hoc corpora from a cooking website in its Italian and English version. We created a pipeline for automatic definitions linking and carried out a successful experiment using the title of a recipe as input text, the output of which is a minimalistic HTML version of the input, whose terms are linked to their corresponding Wikipedia articles. The definitions linking is one of the two missing steps in the pipeline and discussed in the conclusions

Automatic extraction of definitions

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2014This doctoral research work provides a set of methods and heuristics for building a definition extractor or for fine-tuning an existing one. In order to develop and test the architecture, a generic definitions extractor for the Portuguese language is built. Furthermore, the methods were tested in the construction of an extractor for two languages different from Portuguese, which are English and, less extensively, Dutch. The approach presented in this work makes the proposed extractor completely different in nature in comparison to the other works in the field. It is a matter of fact that most systems that automatically extract definitions have been constructed taking into account a specific corpus on a specific topic, and are based on the manual construction of a set of rules or patterns capable of identifyinf a definition in a text. This research focused on three types of definitions, characterized by the connector between the defined term and its description. The strategy adopted can be seen as a "divide and conquer"approach. Differently from the other works representing the state of the art, specific heuristics were developed in order to deal with different types of definitions, namely copula, verbal and punctuation definitions. We used different methodology for each type of definition, namely we propose to use rule-based methods to extract punctuation definitions, machine learning with sampling algorithms for copula definitions, and machine learning with a method to increase the number of positive examples for verbal definitions. This architecture is justified by the increasing linguistic complexity that characterizes the different types of definitions. Numerous experiments have led to the conclusion that the punctuation definitions are easily described using a set of rules. These rules can be easily adapted to the relevant context and translated into other languages. However, in order to deal with the other two definitions types, the exclusive use of rules is not enough to get good performance and it asks for more advanced methods, in particular a machine learning based approach. Unlike other similar systems, which were built having in mind a specific corpus or a specific domain, the one reported here is meant to obtain good results regardless the domain or context. All the decisions made in the construction of the definition extractor take into consideration this central objective.Este trabalho de doutoramento visa proporcionar um conjunto de métodos e heurísticas para a construção de um extractor de definição ou para melhorar o desempenho de um sistema já existente, quando usado com um corpus específico. A fim de desenvolver e testar a arquitectura, um extractor de definic˛ões genérico para a língua Portuguesa foi construído. Além disso, os métodos foram testados na construção de um extractor para um idioma diferente do Português, nomeadamente Inglês, algumas heurísticas também foram testadas com uma terceira língua, ou seja o Holandês. A abordagem apresentada neste trabalho torna o extractor proposto neste trabalho completamente diferente em comparação com os outros trabalhos na área. É um fato que a maioria dos sistemas de extracção automática de definicões foram construídos tendo em conta um corpus específico com um tema bem determinado e são baseados na construc˛ão manual de um conjunto de regras ou padrões capazes de identificar uma definição num texto dum domínio específico. Esta pesquisa centrou-se em três tipos de definições, caracterizadas pela ligacão entre o termo definido e a sua descrição. A estratégia adoptada pode ser vista como uma abordagem "dividir para conquistar". Diferentemente de outras pesquisa nesta área, foram desenvolvidas heurísticas específicas a fim de lidar com as diferentes tipologias de definições, ou seja, cópula, verbais e definicões de pontuação. No presente trabalho propõe-se utilizar uma metodologia diferente para cada tipo de definição, ou seja, propomos a utilização de métodos baseados em regras para extrair as definições de pontuação, aprendizagem automática, com algoritmos de amostragem para definições cópula e aprendizagem automática com um método para aumentar automáticamente o número de exemplos positivos para a definição verbal. Esta arquitetura é justificada pela complexidade linguística crescente que caracteriza os diferentes tipos de definições. Numerosas experiências levaram à conclusão de que as definições de pontuação são facilmente descritas utilizando um conjunto de regras. Essas regras podem ser facilmente adaptadas ao contexto relevante e traduzido para outras línguas. No entanto, a fim de lidar com os outros dois tipos de definições, o uso exclusivo de regras não é suficiente para obter um bom desempenho e é preciso usar métodos mais avançados, em particular aqueles baseados em aprendizado de máquina. Ao contrário de outros sistemas semelhantes, que foram construídos tendo em mente um corpus ou um domínio específico, o sistema aqui apresentado foi desenvolvido de maneira a obter bons resultados, independentemente do domínio ou da língua. Todas as decisões tomadas na construção do extractor de definição tiveram em consideração este objectivo central.Fundação para a Ciência e a Tecnologia (FCT, SFRH/ BD/36732/2007

Automatically Detecting the Resonance of Terrorist Movement Frames on the Web

The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups

Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007

This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p

Harnessing sense-level information for semantically augmented knowledge extraction

Nowadays, building accurate computational models for the semantics of language lies at the very core of Natural Language Processing and Artificial Intelligence. A first and foremost step in this respect consists in moving from word-based to sense-based approaches, in which operating explicitly at the level of word senses enables a model to produce more accurate and unambiguous results. At the same time, word senses create a bridge towards structured lexico-semantic resources, where the vast amount of available machine-readable information can help overcome the shortage of annotated data in many languages and domains of knowledge. This latter phenomenon, known as the knowledge acquisition bottlneck, is a crucial problem that hampers the development of large-scale, data-driven approaches for many Natural Language Processing tasks, especially when lexical semantics is directly involved. One of these tasks is Information Extraction, where an effective model has to cope with data sparsity, as well as with lexical ambiguity that can arise at the level of both arguments and relational phrases. Even in more recent Information Extraction approaches where semantics is implicitly modeled, these issues have not yet been addressed in their entirety. On the other hand, however, having access to explicit sense-level information is a very demanding task on its own, which can rarely be performed with high accuracy on a large scale. With this in mind, in ths thesis we will tackle a two-fold objective: our first focus will be on studying fully automatic approaches to obtain high-quality sense-level information from textual corpora; then, we will investigate in depth where and how such sense-level information has the potential to enhance the extraction of knowledge from open text. In the first part of this work, we will explore three different disambiguation scenar- ios (semi-structured text, parallel text, and definitional text) and devise automatic disambiguation strategies that are not only capable of scaling to different corpus sizes and different languages, but that actually take advantage of a multilingual and/or heterogeneous setting to improve and refine their performance. As a result, we will obtain three sense-annotated resources that, when tested experimentally with a baseline system in a series of downstream semantic tasks (i.e. Word Sense Disam- biguation, Entity Linking, Semantic Similarity), show very competitive performances on standard benchmarks against both manual and semi-automatic competitors. In the second part we will instead focus on Information Extraction, with an emphasis on Open Information Extraction (OIE), where issues like sparsity and lexical ambiguity are especially critical, and study how to exploit at best sense-level information within the extraction process. We will start by showing that enforcing a deeper semantic analysis in a definitional setting enables a full-fledged extraction pipeline to compete with state-of-the-art approaches based on much larger (but noisier) data. We will then demonstrate how working at the sense level at the end of an extraction pipeline is also beneficial: indeed, by leveraging sense-based techniques, very heterogeneous OIE-derived data can be aligned semantically, and unified with respect to a common sense inventory. Finally, we will briefly shift the focus to the more constrained setting of hypernym discovery, and study a sense-aware supervised framework for the task that is robust and effective, even when trained on heterogeneous OIE-derived hypernymic knowledge