K-Space at TRECVID 2008

In this paper we describe K-Space’s participation in TRECVid 2008 in the interactive search task. For 2008 the K-Space group performed one of the largest interactive video information retrieval experiments conducted in a laboratory setting. We had three institutions participating in a multi-site multi-system experiment. In total 36 users participated, 12 each from Dublin City University (DCU, Ireland), University of Glasgow (GU, Scotland) and Centrum Wiskunde and Informatica (CWI, the Netherlands). Three user interfaces were developed, two from DCU which were also used in 2007 as well as an interface from GU. All interfaces leveraged the same search service. Using a latin squares arrangement, each user conducted 12 topics, leading in total to 6 runs per site, 18 in total. We officially submitted for evaluation 3 of these runs to NIST with an additional expert run using a 4th system. Our submitted runs performed around the median. In this paper we will present an overview of the search system utilized, the experimental setup and a preliminary analysis of our results

Silicon resonant microcantilevers for absolute pressure measurement

This work is focused on the developing of silicon resonant microcantilevers for the measurement of the absolute pressure. The microcantilevers have been fabricated with a two-mask bulk micromachining process. The variation in resonance response of microcantilevers was investigated as a function of pressure 10−1-105 Pa, both in terms of resonance frequency and quality factor. A theoretical description of the resonating microstructure is given according to different molecular and viscous regimes. Also a brief discussion on the different quality factors contributions is presented. Theoretical and experimental data show a very satisfying agreement. The microstructure behavior demonstrates a certain sensitivity over a six decade range and the potential evolution of an absolute pressure sensor working in the same rang

Interpreting Link Prediction on Knowledge Graphs

Link Prediction (LP) on Knowledge Graphs (KGs) has re-cently become a sparkling research topic, benefiting from the explosion of machine learning techniques. Several relation-learning models are pub-lished every year, mostly relying on KG embeddings. So far, however, not much has been done to interpret the features they learn and predict, and the circumstances that allow them to achieve satisfactory performances. Our research aims at opening the black box of LP models, trying to explain their behaviors. In this work we first discuss the current lim-itations of LP benchmarks, showing how the use of global metrics on largely skewed datasets hinders our understanding of these models; we then report the main takeaways from our recent comparative analysis of state-of-the-art LP models [3], identifying the most inuential structural features of the graph for predictive effectiveness

Efficient Queries over Web Views

Large Web sites are becoming repositories of structured information that can benefit from being viewed and queried as relational databases. However, querying these views efficiently requires new techniques. Data usually resides at a remote site and is organized as a set of related HTML documents, with network access being a primary cost factor in query evaluation. This cost can be reduced by exploiting the redundancy often found in site design. We use a simple data model, a subset of the Araneus data model, to describe the structure of a Web site. We augment the model with link and inclusion constraints that capture the redundancies in the site. We map relational views of a site to a navigational algebra and show how to use the constraints to rewrite algebraic expressions, reducing the number of network accesses. We show that similar techniques can be used to maintain materialized views over sets of HTML pages

Knowledge graph embeddings or bias graph embeddings? A study of bias in link prediction models

Link Prediction aims at tackling Knowledge Graph incompleteness by inferring new facts based on the existing, already known ones. Nowadays most Link Prediction systems rely on Machine Learning and Deep Learning approaches; this results in inherent opaque models in which assessing the robustness to data biases is not trivial. We define 3 specific types of Sample Selection Bias and estimate their presence in the 5 best-established Link Prediction datasets. We then verify how these biases affect the behaviour of 9 systems representative for every major family of Link Prediction models. We find that these models do indeed learn and incorporate each of the presented biases, with a heavily negative effect on their behaviour. We thus advocate for the creation of novel more robust datasets and of more effective evaluation practices

Kelpie: an Explainability Framework for Embedding-based Link Prediction Models

The latest generations of Link Prediction (LP) models rely on embeddings to tackle incompleteness in Knowledge Graphs, achieving great performance at the cost of interpretability. Their opaqueness limits the trust that users can place in them, hindering their adoption in real-world applications. We have recently introduced Kelpie, an explainability framework tailored specifically for embedding-based LP models. Kelpie can be applied to any embedding-based LP model, and supports two explanation scenarios that we have called necessary and sufficient. In this demonstration we showcase Kelpie’s capability to explain the predictions of models based on vastly different architectures on the 5 major datasets in literature

Clustering Web pages based on their structure

Several techniques have been recently proposed to automatically generate Web wrappers, i.e., programs that extract data from HTML pages, and transform them into a more structured format, typically in XML. These techniques automatically induce a wrapper from a set of sample pages that share a common HTML template. An open issue, however, is how to collect suitable classes of sample pages to feed the wrapper inducer. Presently, the pages are chosen manually. In this paper, we tackle the problem of automatically discovering the main classes of pages offered by a site by exploring only a small yet representative portion of it. We propose a model to describe abstract structural features of HTML pages. Based on this model, we have developed an algorithm that accepts the URL of an entry point to a target Web site, visits a limited yet representative number of pages, and produces an accurate clustering of pages based on their structure. We have developed a prototype, which has been used to perform experiments on real-life Web sites. Ó 2004 Elsevier B.V. All rights reserved