WEB SCALE INFORMATION EXTRACTION USING WRAPPER INDUCTION APPROACH

Information extraction from unstructured, ungrammatical data such as classified listings is difficult because traditional structural and grammatical extraction methods do not apply. The proposed architecture extracts unstructured and un-grammatical data using wrapper induction and show the result in structured format. The source of data will be collected from various post website. The obtained post data pages are processed by page parsing, cleansing and data extraction to obtain new reference sets. Reference sets are used for mapping the user search query, which improvised the scale of search on unstructured and ungrammatical post data. We validate our approach with experimental results

Extração de conhecimento a partir de fontes semi-estruturadas

The increasing number of small, cheap devices, full of sensing capabilities lead to an untapped source of data that can be explored to improve and optimize multiple systems, from small-scale home automation to large-scale applications such as agriculture monitoring, traffic flow and industrial maintenance prediction. Yet, hand in hand with this growth, goes the increasing difficulty to collect, store and organize all these new data. The lack of standard context representation schemes is one of the main struggles in this area. Furthermore, conventional methods for extracting knowledge from data rely on standard representations or a priori relations. These a priori relations add latent information to the underlying model, in the form of context representation schemes, table relations, or even ontologies. Nonetheless, these relations are created and maintained by human users. While feasible for small-scale scenarios or specific areas, this becomes increasingly difficult to maintain when considering the potential dimension of IoT and M2M scenarios. This thesis addresses the problem of storing and organizing context information from IoT/M2M scenarios in a meaningful way, without imposing a representation scheme or requiring a priori relations. This work proposes a d-dimension organization model, which was optimized for IoT/M2M data. The model relies on machine learning features to identify similar context sources. These features are then used to learn relations between data sources automatically, providing the foundations for automatic knowledge extraction, where machine learning, or even conventional methods, can rely upon to extract knowledge on a potentially relevant dataset. During this work, two different machine learning techniques were tackled: semantic and stream similarity. Semantic similarity estimates the similarity between concepts (in textual form). This thesis proposes an unsupervised learning method for semantic features based on distributional profiles, without requiring any specific corpus. This allows the organizational model to organize data based on concept similarity instead of string matching. Another advantage is that the learning method does not require input from users, making it ideal for massive IoT/M2M scenarios. Stream similarity metrics estimate the similarity between two streams of data. Although these methods have been extensively researched for DNA sequencing, they commonly rely on variants of the longest common sub-sequence. This PhD proposes a generative model for stream characterization, specially optimized for IoT/M2M data. The model can be used to generate statistically significant data’s streams and estimate the similarity between streams. This is then used by the context organization model to identify context sources with similar stream patterns. The work proposed in this thesis was extensively discussed, developed and published in several international publications. The multiple contributions in projects and collaborations with fellow colleagues, where parts of the work developed were used successfully, support the claim that although the context organization model (and subsequent similarity features) were optimized for IoT/M2M data, they can potentially be extended to deal with any kind of context information in a wide array of applications.O número crescente de dispositivos pequenos e baratos, repletos de capacidades sensoriais, criou uma nova fonte de dados que pode ser explorada para melhorar e otimizar vários sistemas, desde domótica em ambientes residenciais até aplicações de larga escala como monitorização agrícola, gestão de tráfego e manutenção preditiva a nível industrial. No entanto, este crescimento encontra-se emparelhado com a crescente dificuldade em recolher, armazenar e organizar todos estes dados. A inexistência de um esquema de representação padrão é uma das principais dificuldades nesta área. Além disso, métodos de extração de conhecimento convencionais dependem de representações padrão ou relações definidas a priori. No entanto estas relações são definidas e mantidas por utilizadores humanos. Embora seja viável para cenários de pequena escala ou áreas especificas, este tipo de relações torna-se cada vez mais difícil de manter quando se consideram cenários com a dimensão associado a IoT e M2M. Esta tese de doutoramento endereça o problema de armazenar e organizar informação de contexto de cenários de IoT/M2M, sem impor um esquema de representação ou relações a priori. Este trabalho propõe um modelo de organização com d dimensões, especialmente otimizado para dados de IoT/M2M. O modelo depende de características de machine learning para identificar fontes de contexto similares. Estas caracteristicas são utilizadas para aprender relações entre as fontes de dados automaticamente, criando as fundações para a extração de conhecimento automática. Quer machine learning quer métodos convencionais podem depois utilizar estas relações automáticas para extrair conhecimento em datasets potencialmente relevantes. Durante este trabalho, duas técnicas foram desenvolvidas: similaridade semântica e similaridade entre séries temporais. Similaridade semântica estima a similaridade entre conceitos (em forma textual). Este trabalho propõe um método de aprendizagem não supervisionado para features semânticas baseadas em perfis distributivos, sem exigir nenhum corpus específico. Isto permite ao modelo de organização organizar dados baseado em conceitos e não em similaridade de caracteres. Numa outra vantagem importante para os cenários de IoT/M2M, o método de aprendizagem não necessita de dados de entrada adicionados por utilizadores. A similaridade entre séries temporais são métricas que permitem estimar a similaridade entre várias series temporais. Embora estes métodos tenham sido extensivamente desenvolvidos para sequenciação de ADN, normalmente dependem de variantes de métodos baseados na maior sub-sequencia comum. Esta tese de doutoramento propõe um modelo generativo para caracterizar séries temporais, especialmente desenhado para dados IoT/M2M. Este modelo pode ser usado para gerar séries temporais estatisticamente corretas e estimar a similaridade entre múltiplas séries temporais. Posteriormente o modelo de organização identifica fontes de contexto com padrões temporais semelhantes. O trabalho proposto foi extensivamente discutido, desenvolvido e publicado em diversas publicações internacionais. As múltiplas contribuições em projetos e colaborações com colegas, onde partes trabalho desenvolvido foram utilizadas com sucesso, permitem reivindicar que embora o modelo (e subsequentes técnicas) tenha sido otimizado para dados IoT/M2M, podendo ser estendido para lidar com outros tipos de informação de contexto noutras áreas.The present study was developed in the scope of the Smart Green Homes Project [POCI-01-0247-FEDER-007678], a co-promotion between Bosch Termotecnologia S.A. and the University of Aveiro. It is financed by Portugal 2020 under the Competitiveness and Internationalization Operational Program, and by the European Regional Development Fund.Programa Doutoral em Informátic

Question Paraphrase Generation for Question Answering System

The queries to a practical Question Answering (QA) system range from keywords, phrases, badly written questions, and occasionally grammatically perfect questions. Among different kinds of question analysis approaches, the pattern matching works well in analyzing such queries. It is costly to build this pattern matching module because tremendous manual labor is needed to expand its coverage to so many variations in natural language questions. This thesis proposes that the costly manual labor should be saved by the technique of paraphrase generation which can automatically generate semantically similar paraphrases of a natural language question. Previous approaches of paraphrase generation either require large scale of corpus and the dependency parser, or only deal with the relation-entity type of simple question queries. By introducing a method of inferring transformation operations between paraphrases, and a description of sentence structure, this thesis develops a paraphrase generation method and its implementation in Chinese with very limited amount of corpus. The evaluation results of this implementation show its ability to aid humans to efficiently create a pattern matching module for QA systems as it greatly outperforms the human editors in the coverage of natural language questions, with an acceptable precision in generated paraphrases

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity

A review of automatic phenotyping approaches using electronic health records

Electronic Health Records (EHR) are a rich repository of valuable clinical information that exist in primary and secondary care databases. In order to utilize EHRs for medical observational research a range of algorithms for automatically identifying individuals with a specific phenotype have been developed. This review summarizes and offers a critical evaluation of the literature relating to studies conducted into the development of EHR phenotyping systems. This review describes phenotyping systems and techniques based on structured and unstructured EHR data. Articles published on PubMed and Google scholar between 2013 and 2017 have been reviewed, using search terms derived from Medical Subject Headings (MeSH). The popularity of using Natural Language Processing (NLP) techniques in extracting features from narrative text has increased. This increased attention is due to the availability of open source NLP algorithms, combined with accuracy improvement. In this review, Concept extraction is the most popular NLP technique since it has been used by more than 50% of the reviewed papers to extract features from EHR. High-throughput phenotyping systems using unsupervised machine learning techniques have gained more popularity due to their ability to efficiently and automatically extract a phenotype with minimal human effort