A knowledge-based framework for information extraction and exploration

Harnessing insights from the colossal amount of online information requires the computerised processing of unstructured text in order to satisfy the information need of particular applications such as recommender systems and sentiment analysis. The increasing availability of online documents that describe domain-specific information provides an opportunity in employing a knowledge-based approach in extracting information from Web data. In this thesis, a novel comprehensive knowledge-based framework is proposed to construct and exploit a domain-specific semantic knowledgebase. The proposed framework introduces a methodology for linking several components of different techniques and tools. It focuses on providing reusable and configurable data and application templates, which allow developers to apply it in diversity of domains. The objectives of this framework are: extracting information from unstructured data, constructing a semantic knowledgebase from the extracted information, enriching the resultant semantic knowledgebase by sourcing appropriate semi-structured and structured datasets, and consuming the resultant semantic knowledgebase to facilitate the intelligent exploration and search of information. For the purpose of investigating the challenges of extracting and modelling information in a specific domain, the financial domain was employed as a use-case in the context of a stock investment motivating scenario. The developed knowledge-based approach exploits the semantic and syntactic characteristics of the problem domain knowledge in implementing a hybrid approach of Rule-based and Machine Learning based relation classification. The rule-based approach is adopted in the Natural Language Processing tasks associated with linguistic and structural features, Named Entity Recognition, instances labelling and feature generation processes. The results of these tasks are used to classify the relations between the named entities by employing the Machine Learning based relation classification. In addition, the domain knowledge is analysed to benefit knowledge modelling by translating the domain key concepts into a formal ontology. This ontology is employed in constructing semantic knowledgebase from unstructured online data of a specific domain, enriching the resulting semantic knowledgebase by sourcing semi-structured and structured online data sources and applying advanced classifications and inference technologies to infer new and interesting facts to improve the decision-making and intelligent exploration activities. However, most relations are non-binary in the problem domain knowledge because of its specific characteristic hence an appropriate N-ary relation patterns technique were adopted and investigated. A serious of a novel experiments were conducted to implement and configure a Machine Learning based relation classification. The experimental evaluation evidenced that the developed knowledge-assisted ML relation classification model, which was further boosted by our implementation of GAs to reduce the feature space, has resulted in significant improvement in the process of relation extraction. The experimental results also indicate that amongst the implemented ML algorithms, SVM exhibited the best relation classification accuracy in the majority of the training datasets, while retaining acceptable levels of accuracy in the rest in the remaining training datasets. Web Ontology Language (OWL) reasoning and rule-based reasoning on the resultant semantic knowledgebase were applied to derive stock investment specific recommendations. In addition, SPARQL query language was employed to explore the semantic knowledgebase. Moreover, taking into consideration the problem domain's requirements for modelling non-binary relations, a relation-as-class N-ary relations pattern was implemented, and the reasoning axioms and query language were adjusted to fit the intermediate resources in the N-ary relations requirements. In this thesis also the experience on addressing the challenges of implementing the proposed knowledge-based framework for constructing and exploiting a semantic knowledgebase were summarised. These challenges can be considered by domain experts and knowledge engineers as a novel methodology for employing the Semantic Web Technologies for the knowledge user to intelligently exploit knowledge in similar problem domains. The evaluation of knowledge accessibility by utilising Semantic Web Technologies in the developed application includes the ability of data retrieval to obtain either the entire or some portion of the data from the semantic knowledgebase for a particular use-case scenario. Investigating the tasks of reasoning, accessing and querying the semantic knowledgebase evidences that Semantic Web Technologies can perform an accurate and complex knowledge representation to share Knowledge from a diversity of data sources and, improve the decision‑making process and the intelligent exploration of the semantic knowledgebase

Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review

Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typically used for large-scale integration and analysis of data using graph-based data models. One of the central concepts in this area is the Semantic Web, with the vision of providing a well-defined meaning to information and services on the Web through a set of standards. Particularly, linked data and ontologies have been quite essential for data sharing, discovery, integration, and reuse. In this paper, we provide a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies. The review takes into account four prominent publication venues, namely, Extended Semantic Web Conference, International Semantic Web Conference, Journal of Web Semantics, and Semantic Web Journal. The review highlights the tools, methods, types of data sources, ontologies, and publication methods, together with the challenges, limitations, and lessons learned in the knowledge graph creation processes.publishedVersio

Linked data wrapper curation: A platform perspective

131 p.Linked Data Wrappers (LDWs) turn Web APIs into RDF end-points, leveraging the LOD cloud with current data. This potential is frequently undervalued, regarding LDWs as mere by-products of larger endeavors, e.g. developing mashup applications. However, LDWs are mainly data-driven, not contaminated by application semantics, hence with an important potential for reuse. If LDWs could be decoupled from their breakout projects, this would increase the chances of LDWs becoming truly RDF end-points. But this vision is still under threat by LDW fragility upon API upgrades, and the risk of unmaintained LDWs. LDW curation might help. Similar to dataset curation, LDW curation aims to clean up datasets but, in this case, the dataset is implicitly described by the LDW definition, and ¿stains¿ are not limited to those related with the dataset quality but also include those related to the underlying API. This requires the existence of LDW Platforms that leverage existing code repositories with additional functionalities that cater for LDW definition, deployment and curation. This dissertation contributes to this vision through: (1) identifying a set of requirements for LDW Platforms; (2) instantiating these requirements in SYQL, a platform built upon Yahoo's YQL; (3) evaluating SYQL through a fully-developed proof of concept; and (4), validating the extent to which this approach facilitates LDW curation

Matching Startup Founders to Investors: a Tool and a Study

The process of matching startup founders with venture capital investors is a necessary first step for many modern technology companies, yet there have been few attempts to study the characteristics of the two parties and their interactions. Surprisingly little has been shown quantitatively about the process, and many of the common assumptions are based on anecdotal evidence. In this thesis, we aim to learn more about the matching component of the startup fundraising process. We begin with a tool (VCWiz), created from the current set of best-practices to help inexperienced founders navigate the founder-investor matching process. The goal of this tool is to increase efficiency and equitability, while collecting data to inform further studies. We use this data, combined with public data on venture investments in the USA, to draw conclusions about the characteristics of venture financing rounds. Finally, we explore the communication data contributed to the tool by founders who are actively fundraising, and use it to learn which social attributes are most beneficial for individuals to possess when soliciting investments.Comment: MIT Master's of Engineering in Computer Science thesis. June 2018. 152 page

TSPOONS: Tracking Salience Profiles Of Online News Stories

News space is a relatively nebulous term that describes the general discourse concerning events that affect the populace. Past research has focused on qualitatively analyzing news space in an attempt to answer big questions about how the populace relates to the news and how they respond to it. We want to ask when do stories begin? What stories stand out among the noise? In order to answer the big questions about news space, we need to track the course of individual stories in the news. By analyzing the specific articles that comprise stories, we can synthesize the information gained from several stories to see a more complete picture of the discourse. The individual articles, the groups of articles that become stories, and the overall themes that connect stories together all complete the narrative about what is happening in society. TSPOONS provides a framework for analyzing news stories and answering two main questions: what were the important stories during some time frame and what were the important stories involving some topic. Drawing technical news stories from Techmeme.com, TSPOONS generates profiles of each news story, quantitatively measuring the importance, or salience, of news stories as well as quantifying the impact of these stories over time

Web interaction environments : characterising Web accessibility at the large

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2012Accessibility quality on the Web is essential for providing a good Web experience to people with disabilities. The existence of virtual ramps aid these users grasping and interacting withWeb content, just like the experience of those who are unimpaired. However, more often than not, Web pages impose accessibility barriers, usually centred on the unavailability of tailored content to specific perceptual abilities (e.g., textual description of images, enabling grasping information with assistive technologies), as well as on proper HTML structural elements that adequate the semantics of a Web page. When evaluating the accessibility quality of Web pages, the resulting analysis is often focused on a small sample set (e.g., a single Web page or a selection of pages from a Web site). While this kind of analysis gets the gist of accessibility quality, it misses the big picture on the overall accessibility quality of the Web. This thesis addresses the challenge of observing accessibility phenomena on the Web, through the experimental evaluation of large collections of Web pages. This resulted on new findings about the accessibility quality of the Web, such as its correlation with HTML element count, and the erroneous perception of accessibility quality by developers. Small-scale experiments have been verified also at large scale, such as the correlation between the usage of HTML templates and accessibility quality. Based on the challenges raised by the experimental evaluation, this thesis proposes a novel approach for large scale Web accessibility evaluation based on Linked Data, as well as the establishment of metrics to assess the truthfulness and coverage of automated evaluation methods.A qualidade da acessibilidade é um factor crucial para as pessoas com deficiências terem uma boa experiência de interacção com a Web.A qualidade da acessibilidade é um factor crucial para as pessoas com deficiências terem uma boa experiência de interacção com a Web. A existência de rampas virtuais ajuda estas pessoas a compreender e interagir com conteúdos Web, a par do que o utilizador comum já experiencia. Porém, a maioria das páginas Web ainda contêm barreiras à acessibilidade. Estas barreiras centram-se normalmente na indisponibilidade de conteúdos perceptíveis por diferentes tipos de capacidades (e.g., descrições textuais de imagens), bem como no uso incorrecto de elementos HTML de acordo com a semântica de uma página Web. Nos dias de hoje, a avaliação da qualidade de acessibilidade de páginas Web é ainda efectuada em pequena escala (e.g., uma página Web ou, no melhor caso, um conjunto de páginas representativas de um sítio Web). Apesar deste tipo de avaliações resultarem na compreensão de alguns fenómenos do estado da acessibilidade na Web, ainda não se sabe qual o seu impacto em larga escala. Esta tese discute os principais desafios na observação da acessibilidade da Web, tendo por base um conjunto de avaliações experimentais de colecções de grande dimensão de páginas Web. Destes estudos destacam-se as seguintes contribuições e resultados:a diferença drástica na interpretação dos avisos resultantes de avaliações de acessibilidade Web: um dos resultados principais da avaliação experimental em larga escala destaca a diferença na interpretação dos avisos (warnings) da aplicação de técnicas da norma WCAG, onde a interpretação optimista (i.e., a visão da maioria dos criadores de páginas Web) se distancia amplamente da interpretação conservadora (onde os avisos são interpretados como erros); a correlação entre a qualidade da acessibilidade de uma página Web e a sua complexidade: este mesmo estudo de larga escala revelou uma correlação entre a complexidade de uma página Web (no que diz respeito ao número de elementos HTML que contém) e a qualidade da acessibilidade. Quanto menor a complexidade de uma página Web, mais certa se torna a alta qualidade da acessibilidade dessa página; o benefício do uso de templates e sistemas de gestão de conteúdos na melhoria da acessibilidade de páginas Web: em ambos os estudos experimentais de acessibilidade foi detectada uma correlação entre a qualidade de acessibilidade das páginas Web e o uso de templates e sistemas de gestão de conteúdo. Esta propriedade foi verificada quer em pequena escala (sobre uma colecção de páginas Web da Wikipedia), quer em larga escala; o incumprimento das regras mais elementares e mais conhecidas da acessibilidade: estes estudos experimentais permitiram também verificar que, apesar de toda a envagelização e educação sobre as questões de acessibilidade na Web, a maioria das regras de acessibilidade são incessantemente quebradas pela maioria das páginas Web.Esta problemática verifica-se, em particular, nas regras de cumprimento de acessibilidade mais conhecidas, tal como por exemplo a disponibilidade de textos alternativos a conteúdos multimédia. Com base nestas experiências e resultados, esta tese apresenta um novo modelo de estudo da acessibilidade na Web, tendo por base o ciclo de estudos da Web em larga escala. Deste modelo resultaram as seguintes contribuições: um modelo para a avaliação distribuída de acessibilidade Web, baseado em propriedades tecnológicas e topológicas: foi concebido um modelo de avaliação de acessibilidade Web que permite a concepção de sistemas de avaliação com base em propriedades tecnológicas e topológicas. Este modelo possibilita, entre outras características, o estudo da cobertura de plataformas e avaliadores de acessibilidade, bem como da sua aplicação em larga escala; uma extensão às linguagens e modelos EARL e Linked Data, bem como um conjunto de definições para extrair informação destes: este modelo de avaliação de acessibilidade Web foi sustentado também pela sua concretização em linguagens e modelos já existentes para o estudo de acessibilidade (EARL) e da Web em larga escala (Linked Data), permitindo assim a sua validação; definição dos limites da avaliação de acessibilidade Web: por fim, este modelo de avaliação de acessibilidade permitiu também delinear uma metodologia de meta-avaliação da acessibilidade, na qual se poderão enquadrar as propriedades dos avaliadores de acessibilidade existentes. Todas estas contribuições resultaram também num conjunto de publicações científicas, das quais se destacam: Rui Lopes and Luís Carriço, A Web Science Perspective of Web Accessibility, in submission for the ACM Transactions on Accessible Computing (TACCESS), ACM, 2011; Rui Lopes and Luís Carriço, Macroscopic Characterisations of Web Accessibility, New Review of Hypermedia and Multimedia – Special Issue on Web Accessibility. Taylor & Francis, 2010; Rui Lopes, Karel Van Isacker and Luís Carriço, Redefining Assumptions: Accessibility and Its Stakeholders, The 12th International Conference on Computers Helping People with Special Needs (ICCHP), Vienna, Austria, 14-16 July 2010; Rui Lopes, Daniel Gomes and Luís Carriço, Web Not For All: A Large Scale Study of Web Accessibility, W4A: 7th ACM International Cross-Disciplinary Conference on Web Accessibility, Raleigh, North Carolina, USA, 26-27 April 2010; Rui Lopes, Konstantinos Votis, Luís Carriço, Dimitrios Tzovaras, and Spiridon Likothanassis, The Semantics of Personalised Web Accessibility Assessment, 25th Annual ACM Symposium on Applied Computing (SAC), Sierre, Switzerland, 22-26 March, 2010 Konstantinos Votis, Rui Lopes, Dimitrios Tzovaras, Luís Carriço and Spiridon Likothanassis, A Semantic Accessibility Assessment Environment for Design and Development for the Web, HCI International 2009 (HCII 2009), San Diego, California, USA, 19-24 July 2009 Rui Lopes and Luís Carriço, On the Gap Between Automated and In-Vivo Evaluations of Web Accessibility, HCI International 2009 (HCII 2009), San Diego, California, USA, 19-24 July 2009; Rui Lopes, Konstantinos Votis, Luís Carriço, Spiridon Likothanassis and Dimitrios Tzovaras, Towards the Universal Semantic Assessment of Accessibility, 24th Annual ACM Symposium on Applied Computing (SAC),Waikiki Beach, Honolulu, Hawaii, USA, 8-12 March 2009; Rui Lopes and Luís Carriço, Querying Web Accessibility Knowledge from Web Graphs, Handbook of Research on Social Dimensions of Semantic Technologies, IGI Global, 2009; Rui Lopes, Konstantinos Votis, Luís Carriço, Spiridon Likothanassis and Dimitrios Tzovaras, A Service Oriented Ontological Framework for the Semantic Validation of Web Accessibility, Handbook of Research on Social Dimensions of Semantic Technologies, IGI Global, 2009; Rui Lopes and Luís Carriço, On the Credibility of Wikipedia: an Accessibility Perspective, Second Workshop on Information Credibility on the Web (WICOW 2008), Napa Valley, California, USA, 2008; Rui Lopes, Luís Carriço, A Model for Universal Usability on the Web, WSW 2008: Web Science Workshop, Beijing, China, 22 April 2008; Rui Lopes, Luís Carriço, The Impact of Accessibility Assessment in Macro Scale Universal Usability Studies of the Web, W4A: 5th ACM International Cross-Disciplinary Conference on Web Accessibility, Beijing, China, 21-22 April 2008. Best paper award; Rui Lopes, Luís Carriço, Modelling Web Accessibility for Rich Document Production, Journal on Access Services 6 (1-2), Routledge, Taylor & Francis Group, 2009; Rui Lopes, Luís Carriço, Leveraging Rich Accessible Documents on the Web, W4A: 4th ACM International Cross-Disciplinary Conference on Web Accessibility, Banff, Canada, 7-8 May 2007.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/29150/2006

Enabling Human-Robot Collaboration via Holistic Human Perception and Partner-Aware Control

As robotic technology advances, the barriers to the coexistence of humans and robots are slowly coming down. Application domains like elderly care, collaborative manufacturing, collaborative manipulation, etc., are considered the need of the hour, and progress in robotics holds the potential to address many societal challenges. The future socio-technical systems constitute of blended workforce with a symbiotic relationship between human and robot partners working collaboratively. This thesis attempts to address some of the research challenges in enabling human-robot collaboration. In particular, the challenge of a holistic perception of a human partner to continuously communicate his intentions and needs in real-time to a robot partner is crucial for the successful realization of a collaborative task. Towards that end, we present a holistic human perception framework for real-time monitoring of whole-body human motion and dynamics. On the other hand, the challenge of leveraging assistance from a human partner will lead to improved human-robot collaboration. In this direction, we attempt at methodically defining what constitutes assistance from a human partner and propose partner-aware robot control strategies to endow robots with the capacity to meaningfully engage in a collaborative task

LODNav – An Interactive Visualization of the Linking Open Data Cloud

The emergence of the Linking Open Data Cloud (LODC) is an example of the adoption of Linked Data principles and the creation of a Web of Data. There is an increasing amount of information linked across member datasets of the LODC by means of RDF links, yet there is little support for a human to understand which datasets are connected to one another. This research presents a novel approach for understanding these interconnections with the publicly accessible tool LODNav – Linking Open Data Navigator. LODNav provides a visualization metaphor of the LODC by positioning member datasets of the LODC on a world map based on the geographical location of the dataset. This interactive tool aims to provide a dynamic up-to-date visualization of the LODC and allows the extraction of information about the datasets as well as their interconnections as RDF data

Filter (Dials | ...)

An increasing number of data providing organizations publish their unique information as Linked data which is available in the Semantic Web in RDF (Resource Distribution Framework) format. RDF is a standard model describing web information that is understandable to computer applications. For users without much technical knowledge, information visualization provides support to interpret the structure of underlying data in the web, understand their relationships, form queries and extract more interesting information. This thesis proposes a visual query technique focusing on the visualization of overall availability of data. The prototype visualization tool developed shows the available groups of data items and their size according to different filtering options. The thesis report starts with a clear description of the research problem and an analysis of the efforts made so far in the field for finding a solution. A survey of the the related work is presented to find the potential for the visualization. The Filter Dials, a novel visualization concept serves as the base work and a starting point and that the evolved new technique attempts to overcome some of the notable drawbacks of the Dials. The thesis also proposes a new visual query technique that aims to achieve the goal of visually presenting an overview of large and complex data available in the web. A prototypical implementation of the visualization tool is done and evaluated by users and conclusions are drawn with the applications, advantages, disadvantages and the possible future directions in the proposed method