Explaining clusters with inductive logic programming and linked data

Knowledge Discovery consists in discovering hidden regularities in large amounts of data using data mining techniques. The obtained patterns require an interpretation that is usually achieved using some background knowledge given by experts from several domains. On the other hand, the rise of Linked Data has increased the number of connected cross-disciplinary knowledge, in the form of RDF datasets, classes and relationships. Here we show how Linked Data can be used in an Inductive Logic Programming process, where they provide background knowledge for finding hypotheses regarding the unrevealed connections between items of a cluster. By using an example with clusters of books, we show how different Linked Data sources can be used to automatically generate rules giving an underlying explanation to such clusters

Gender congruency effect in European Artwork: the perceived femininity of abstract nouns

openGli artefatti culturali (statue, dipinti) fanno parte dell’immaginario collettivo culturale. I concetti astratti vengono spesso rappresentati nelle opere d’arte attraverso l’uso di personificazioni femminili. Le persone sono spesso esposte a molteplici rappresentazioni artistiche di concetti astratti come Libertà, Giustizia, e Innocenza che appaiono nei quadri sotto sembianze di donna. La corrispondenza tra astratto e femminile non è causale, infatti questi concetti condividono il fatto che sono grammaticalmente femminili sia in latino che in greco. Benché la letteratura sugli effetti del genere grammaticale non sia ampia, alcuni studi suggeriscono che il genere assegnato dall’artista alla personificazione nelle opere d’arte possa in parte essere stato influenzato dal genere grammaticale del concetto astratto rappresentato. In altre parole, il genere grammaticale potrebbe essere in grado di influenzare la nostra percezione di mascolinità-femminilità dei concetti astratti non solo in maniera diretta ma anche attraverso gli artefatti culturali che spesso mantengono il genere grammaticale del concetto nella lingua nativa dell’artista. In questa tesi verrà approfondita la relazione tra astratto e femminile e le possibili conseguenze sociali e culturali che possono derivare da questa corrispondenza

Walking Linked Data: a graph traversal approach to explain clusters

Link traversal is one of the biggest advantages of Linked Data, as it allows the serendipitous discovery of new knowledge thanks to the natural connections between data of different sources. Our general problem is to understand how such a property can benefit the Knowledge Discovery process: in particular, we aim at using Linked Data to explain the patterns of data that have been extracted from a typical data min- ing process such as clustering. The strategy we propose here is Linked Data traversal, in which we explore and build on-the-fly an unknown Linked Data graph by simply deferencing entities’ URIs until we find, by following the links between entities, a valid explanation to our clusters. The experiments section gives an insight into the performance of such an approach, in terms of time and scalability, and show how the links easily gather knowledge from different data sources

Using Linked Data traversal to label academic communities

In this paper we exploit knowledge from Linked Data to ease the process of analysing scholarly data. In the last years, many techniques have been presented with the aim of analysing such data and revealing new, unrevealed knowledge, generally presented in the form of “patterns”. How-ever, the discovered patterns often still require human interpretation to be further exploited, which might be a time and energy consuming process. Our idea is that the knowledge shared within Linked Data can actuality help and ease the process of interpreting these patterns. In practice, we show how research communities obtained through standard network analytics techniques can be made more understand- able through exploiting the knowledge contained in Linked Data. To this end, we apply our system Dedalo that, by performing a simple Linked Data traversal, is able to automatically label clusters of words, corresponding to topics of the different communities

Learning to Assess Linked Data Relationships Using Genetic Programming

The goal of this work is to learn a measure supporting the detection of strong relationships between Linked Data entities. Such relationships can be represented as paths of entities and properties, and can be obtained through a blind graph search process traversing Linked Data. The challenge here is therefore the design of a cost-function that is able to detect the strongest relationship between two given entities, by objectively assessing the value of a given path. To achieve this, we use a Genetic Programming approach in a supervised learning method to generate path evaluation functions that compare well with human evaluations. We show how such a cost-function can be generated only using basic topological features of the nodes of the paths as they are being traversed (i.e. without knowledge of the whole graph), and how it can be improved through introducing a very small amount of knowledge about the vocabularies of the properties that connect nodes in the graph

An Ontology Design Pattern to Define Explanations

In this paper, we propose an ontology design pattern for the concept of “explanation”. The motivation behind this work comes from our research, which focuses on automatically identifying explanations for data patterns. If we want to produce explanations from data agnostically from the application domain, we first need a formal definition of what an explanation is, i.e. which are its components, their roles or their interactions. We analysed and surveyed works from the disciplines grouped under the name of Cognitive Sciences, with the aim of identifying differences and commonalities in the way their researchers intend the concept of explanation. We then produced not only an ontology design pattern to model it, but also the instantiations of this in each of the analysed disciplines. Besides those contributions, the paper presents how the proposed ontology design pattern can be used to analyse the validity of the explanations produced by our, and other, frameworks

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

The goal of this work is to describe how robots interact with complex city environments, and to identify the main characteristics of an emerging field that we call Robot--City Interaction (RCI). Given the central role recently gained by modern cities as use cases for the deployment of advanced technologies, and the advancements achieved in the robotics field in recent years, we assume that there is an increasing interest both in integrating robots in urban ecosystems, and in studying how they can interact and benefit from each others. Therefore, our challenge becomes to verify the emergence of such area, to assess its current state and to identify the main characteristics, core themes and research challenges associated with it. This is achieved by reviewing a preliminary body of work contributing to this area, which we classify and analyze according to an analytical framework including a set of key dimensions for the area of RCI. Such review not only serves as a preliminary state-of-the-art in the area, but also allows us to identify the main characteristics of RCI and its research landscape

Task-Agnostic Object Recognition for Mobile Robots through Few-Shot Image Matching

To assist humans with their daily tasks, mobile robots are expected to navigate complex and dynamic environments, presenting unpredictable combinations of known and unknown objects. Most state-of-the-art object recognition methods are unsuitable for this scenario because they require that: (i) all target object classes are known beforehand, and (ii) a vast number of training examples is provided for each class. This evidence calls for novel methods to handle unknown object classes, for which fewer images are initially available (few-shot recognition). One way of tackling the problem is learning how to match novel objects to their most similar supporting example. Here, we compare different (shallow and deep) approaches to few-shot image matching on a novel data set, consisting of 2D views of common object types drawn from a combination of ShapeNet and Google. First, we assess if the similarity of objects learned from a combination of ShapeNet and Google can scale up to new object classes, i.e., categories unseen at training time. Furthermore, we show how normalising the learned embeddings can impact the generalisation abilities of the tested methods, in the context of two novel configurations: (i) where the weights of a Convolutional two-branch Network are imprinted and (ii) where the embeddings of a Convolutional Siamese Network are L2-normalised