Error detection in Knowledge Graphs: Path Ranking, Embeddings or both?

This paper attempts to compare and combine different approaches for de-tecting errors in Knowledge Graphs. Knowledge Graphs constitute a mainstreamapproach for the representation of relational information on big heterogeneous data,however, they may contain a big amount of imputed noise when constructed auto-matically. To address this problem, different error detection methodologies have beenproposed, mainly focusing on path ranking and representation learning. This workpresents various mainstream approaches and proposes a novel hybrid and modularmethodology for the task. We compare these methods on two benchmarks and one real-world biomedical publications dataset, showcasing the potential of our approach anddrawing insights regarding the state-of-art in error detection in Knowledge Graphs

A Logic Programming Approach to Activity Recognition

We have been developing a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities detected on video frames. The output of our system is a set of recognised long-term activities, which are pre-defined temporal combinations of short-term activities. The constraints on the short-term activities that, if satisfied, lead to the recognition of a long-term activity, are expressed using a dialect of the Event Calculus. We illustrate the expressiveness of the dialect by showing the representation of several typical complex activities. Furthermore, we present a detailed evaluation of the system through experimentation on a benchmark dataset of surveillance videos.

Stacking classifiers for anti-spam filtering of e-mail

We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications.

Resolving Part-of-Speech Ambiguity in the Greek Language Using Learning Techniques

This article investigates the use of Transformation-Based Error-Driven learning for resolving part-of-speech ambiguity in the Greek language. The aim is not only to study the performance, but also to examine its dependence on different thematic domains. Results are presented here for two different test cases: a corpus on "management succession events" and a general-theme corpus. The two experiments show that the performance of this method does not depend on the thematic domain of the corpus, and its accuracy for the Greek language is around 95%.