A Taxonomy of Information Retrieval Models and Tools

Information retrieval is attracting significant attention due to the exponential growth of the amount of information available in digital format. The proliferation of information retrieval objects, including algorithms, methods, technologies, and tools, makes it difficult to assess their capabilities and features and to understand the relationships that exist among them. In addition, the terminology is often confusing and misleading, as different terms are used to denote the same, or similar, tasks. This paper proposes a taxonomy of information retrieval models and tools and provides precise definitions for the key terms. The taxonomy consists of superimposing two views: a vertical taxonomy, that classifies IR models with respect to a set of basic features, and a horizontal taxonomy, which classifies IR systems and services with respect to the tasks they support. The aim is to provide a framework for classifying existing information retrieval models and tools and a solid point to assess future developments in the field

Modeling Complex Networks For (Electronic) Commerce

NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Affinity-Based Reinforcement Learning : A New Paradigm for Agent Interpretability

The steady increase in complexity of reinforcement learning (RL) algorithms is accompanied by a corresponding increase in opacity that obfuscates insights into their devised strategies. Methods in explainable artificial intelligence seek to mitigate this opacity by either creating transparent algorithms or extracting explanations post hoc. A third category exists that allows the developer to affect what agents learn: constrained RL has been used in safety-critical applications and prohibits agents from visiting certain states; preference-based RL agents have been used in robotics applications and learn state-action preferences instead of traditional reward functions. We propose a new affinity-based RL paradigm in which agents learn strategies that are partially decoupled from reward functions. Unlike entropy regularisation, we regularise the objective function with a distinct action distribution that represents a desired behaviour; we encourage the agent to act according to a prior while learning to maximise rewards. The result is an inherently interpretable agent that solves problems with an intrinsic affinity for certain actions. We demonstrate the utility of our method in a financial application: we learn continuous time-variant compositions of prototypical policies, each interpretable by its action affinities, that are globally interpretable according to customers’ financial personalities. Our method combines advantages from both constrained RL and preferencebased RL: it retains the reward function but generalises the policy to match a defined behaviour, thus avoiding problems such as reward shaping and hacking. Unlike Boolean task composition, our method is a fuzzy superposition of different prototypical strategies to arrive at a more complex, yet interpretable, strategy.publishedVersio

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Semantic multimedia analysis using knowledge and context

PhDThe difficulty of semantic multimedia analysis can be attributed to the extended diversity in form and appearance exhibited by the majority of semantic concepts and the difficulty to express them using a finite number of patterns. In meeting this challenge there has been a scientific debate on whether the problem should be addressed from the perspective of using overwhelming amounts of training data to capture all possible instantiations of a concept, or from the perspective of using explicit knowledge about the concepts’ relations to infer their presence. In this thesis we address three problems of pattern recognition and propose solutions that combine the knowledge extracted implicitly from training data with the knowledge provided explicitly in structured form. First, we propose a BNs modeling approach that defines a conceptual space where both domain related evi- dence and evidence derived from content analysis can be jointly considered to support or disprove a hypothesis. The use of this space leads to sig- nificant gains in performance compared to analysis methods that can not handle combined knowledge. Then, we present an unsupervised method that exploits the collective nature of social media to automatically obtain large amounts of annotated image regions. By proving that the quality of the obtained samples can be almost as good as manually annotated images when working with large datasets, we significantly contribute towards scal- able object detection. Finally, we introduce a method that treats images, visual features and tags as the three observable variables of an aspect model and extracts a set of latent topics that incorporates the semantics of both visual and tag information space. By showing that the cross-modal depen- dencies of tagged images can be exploited to increase the semantic capacity of the resulting space, we advocate the use of all existing information facets in the semantic analysis of social media

Real time detection of malicious webpages using machine learning techniques

In today's Internet, online content and especially webpages have increased exponentially. Alongside this huge rise, the number of users has also amplified considerably in the past two decades. Most responsible institutions such as banks and governments follow specific rules and regulations regarding conducts and security. But, most websites are designed and developed using little restrictions on these issues. That is why it is important to protect users from harmful webpages. Previous research has looked at to detect harmful webpages, by running the machine learning models on a remote website. The problem with this approach is that the detection rate is slow, because of the need to handle large number of webpages. There is a gap in knowledge to research into which machine learning algorithms are capable of detecting harmful web applications in real time on a local machine. The conventional method of detecting malicious webpages is going through the black list and checking whether the webpages are listed. Black list is a list of webpages which are classified as malicious from a user's point of view. These black lists are created by trusted organisations and volunteers. They are then used by modern web browsers such as Chrome, Firefox, Internet Explorer, etc. However, black list is ineffective because of the frequent-changing nature of webpages, growing numbers of webpages that pose scalability issues and the crawlers' inability to visit intranet webpages that require computer operators to login as authenticated users. The thesis proposes to use various machine learning algorithms, both supervised and unsupervised to categorise webpages based on parsing their features such as content (which played the most important role in this thesis), URL information, URL links and screenshots of webpages. The features were then converted to a format understandable by machine learning algorithms which analysed these features to make one important decision: whether a given webpage is malicious or not, using commonly available software and hardware. Prototype tools were developed to compare and analyse the efficiency of these machine learning techniques. These techniques include supervised algorithms such as Support Vector Machine, Naïve Bayes, Random Forest, Linear Discriminant Analysis, Quantitative Discriminant Analysis and Decision Tree. The unsupervised techniques are Self-Organising Map, Affinity Propagation and K-Means. Self-Organising Map was used instead of Neural Networks and the research suggests that the new version of Neural Network i.e. Deep Learning would be great for this research. The supervised algorithms performed better than the unsupervised algorithms and the best out of all these techniques is SVM that achieves 98% accuracy. The result was validated by the Chrome extension which used the classifier in real time. Unsupervised algorithms came close to supervised algorithms. This is surprising given the fact that they do not have access to the class information beforehand

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Attribute Selection for Unsupervised and Language Independent Classification of Documents

Raw text documents are the most common way documents are written, that is, unstruc- tured text. So, they contain most of the information available. Thus, it is desirable that there are tools capable of extracting the core content of each document and, through it, identify the group to which it belongs, since in unstructured texts there is usually no fore- seen place for indicating the document class. Nowadays, English is not the only language documents appear in the available repositories. This suggests the construction of tools that, if possible, do not depend on the language in which the texts are written, which is a challenge. This dissertation focuses mainly on clustering documents according to their content, using no class labels, that is, unsupervised clustering. It aims to mine and to create features from text in order to achieve that purpose. It is also intended to classify new doc- uments, in a supervised approach, according to the classes identified in the unsupervised training phase. In order to solve this, the proposed solution finds the best features inside the docu- ments, and uses their discriminative power to provide clustering. In order to summarise the core content of each cluster found by this approach, key expressions are automatically extracted from their documents.Documentos de texto bruto são a forma mais comum de escrita de documentos, ou seja, texto não estruturado. Assim, eles contêm a maioria das informações disponíveis. Deste modo, é desejável que existam ferramentas capazes de extrair o conteúdo mais importante de um documento e, por este meio, identificar o grupo ao qual o documento pertence, pois em textos não estruturados geralmente não há uma previsão de indicação da classe do mesmo. Atualmente, o Inglês não é a única linguagem em que os documentos aparecem nos repositórios disponíveis. Isto sugere a construção de ferramentas que, se possível, não dependam da linguagem em que os textos são escritos, sendo isto um desafio. Esta dissertação foca-se principalmente em agrupar os documentos de acordo com o seu conteúdo, sem usar rótulos de classes, ou seja, agrupamento não supervisionado. O objetivo será alcançado através da extração e criação de atributos a partir do texto. Pretende-se também classificar novos documentos, numa abordagem supervisionada, de acordo com as classes identificadas na fase de treino não supervisionado. De modo a tentar resolver este problema, é proposta uma solução que encontra os melhores atributos nos documentos, e usa o poder discriminativo das mesmas para fa- zer o agrupamento. De modo a sumarizar o conteúdo principal destes agrupamentos, expressões chave são automaticamente extraídas dos documentos