Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. As networks derived from increasingly large datasets become more common, identifying efficient methods for representing and analyzing them becomes crucial. This work extends the Prime Adjacency Matrices (PAMs) framework, which employs prime numbers to represent distinct relations within a network uniquely. This enables a compact representation of a complete multi-relational graph using a single adjacency matrix, which, in turn, facilitates quick computation of multi-hop adjacency matrices. In this work, we enhance the framework by introducing a lossless algorithm for calculating the multi-hop matrices and propose the Bag of Paths (BoP) representation, a versatile feature extraction methodology for various graph analytics tasks, at the node, edge, and graph level. We demonstrate the efficiency of the framework across various tasks and datasets, showing that simple BoP-based models perform comparably to or better than commonly used neural models while improving speed by orders of magnitude.

Abstract In this paper, we propose a novel framework, called STAL, which makes use of unlabeled graph data, through a combination of Active Learning and Self-Training, in order to improve node labeling by Graph Neural Networks (GNNs). GNNs have been shown to perform well on many tasks, when sufficient labeled data are available. Such data, however, is often scarce, leading to the need for methods that leverage unlabeled data that are abundant. Active Learning and Self-training are two common approaches towards this goal and we investigate here their combination, in the context of GNN training. Specifically, we propose a new framework that first uses active learning to select highly uncertain unlabeled nodes to be labeled and be included in the training set. In each iteration of active labeling, the proposed method expands also the label set through self-training. In particular, highly certain pseudo-labels are obtained and added automatically to the training set. This process is repeated, leading to good classifiers, with a limited amount of labeled data. Our experimental results on various datasets confirm the efficiency of the proposed approach.

We present a system for Complex Event Recognition (CER) based on automata. While multiple such systems have been described in the literature, they typically suffer from a lack of clear and denotational semantics, a limitation which often leads to confusion with respect to their expressive power. In order to address this issue, our system is based on an automaton model which is a combination of symbolic and register automata. We extend previous work on these types of automata, in order to construct a formalism with clear semantics and a corresponding automaton model whose properties can be formally investigated. We call such automata Symbolic Register Transducers (SRT). The distinctive feature of SRT , compared to previous automaton models used in CER, is that they can encode patterns relating multiple input events from an event stream, without sacrificing rigor and clarity. We show how SRT can be used in CER in order to detect patterns upon streams of events, using our framework that provides declarative and compositional semantics. We also compare our SRT -based CER engine against other state-of-the-art CER systems and show that it is both more expressive and more efficient.

In this work, we examine the auditor opinions that are provided in financial reports of public companies, when they express issues related to going concerns. Auditor opinions provide explicit insights regarding potential threats to the financial status of companies. We, therefore, provide methods for the automated classification of the auditor narratives to going concern issues, and we investigate which of those issues are related to bankruptcy events. We focus on annual reports of public US companies and publicly available bankruptcy labels to learn models that label these reports with probable going concern issues in an automated way. Our experimental results validate our approach and provide evidence that the analysis of these narratives can lead to the identification of specific issues related to bankruptcy concerns and thus alarm the interested parties.

In the maritime domain, vessels typically maintain straight, predictable routes at open sea, except in the rare cases of adverse weather conditions, accidents and traffic restrictions. Consequently, large amounts of streaming positional updates from vessels can hardly contribute additional knowledge about their actual motion patterns. We have been developing a system for vessel trajectory compression discarding a significant part of the original positional updates, with minimal trajectory reconstruction error. In this work, we present an extension of this system, that allows the user to fine-tune trajectory compression according to the requirements of a given application. The extended system avoids the issues of hyper-parameter tuning, supports incremental optimization and facilitates composite maritime event recognition. Finally, we report empirical results from a comprehensive empirical evaluation against two real-world datasets of vessel positions.

No abstract available.

Systems for symbolic event recognition accept as input a stream of time-stamped events from sensors and other computational devices, and seek to identify high-level composite events, collections of events that satisfy some pattern. RTEC is an Event Calculus dialect with novel implementation and `windowing' techniques that allow for efficient event recognition, scalable to large data streams. RTEC supports the expression of rather complex events, such as `two people are fighting', using simple primitives. It can operate in the absence of filtering modules, as it is only slightly affected by data that are irrelevant to the events we want to recognise. Furthermore, RTEC can deal with applications where event data arrive with a (variable) delay from, and are revised by, the underlying sources. RTEC can update already recognised events and recognise new events when data arrive with a delay or following data revision. We evaluate RTEC both theoretically, presenting a complexity analysis, and experimentally, using two real-world applications. The evaluation shows that RTEC can support real-time event recognition and is capable of meeting the performance requirements identified in a survey of event processing use cases.

Organisations require techniques for automated transformation of the Big Data they collect into operational knowledge. This requirement may be addressed by employing event processing systems that detect activities/events of special significance within an organisation, given streams of low-level information that are difficult to be utilised by humans [4]. Systems for event processing and in particular event recognition (‘event pattern matching’) accept as input a stream of time-stamped, simple or low-level events. A low-level event is the result of applying a computational derivation process to some other event, such as an event coming from a sensor. Using low-level events as input, event processing systems identify composite or high-level events of interest — collections of events that satisfy some pattern. Consider, for example, the recognition of attacks on nodes of a computer network given the TCP/IP messages, the recognition of suspicious trader behaviour given the transactions in a financial market, and the recognition of whale songs given a symbolic representation of whale sounds. Numerous event processing systems have been proposed in the literature [3]. Systems with a logic-based representation of event structures, for example, have been attracting considerable attention. They exhibit a formal, declarative semantics, allowing for verification and a code maintenance, they have proven to be efficient and scalable, and they are supported by machine learning tools, minimising human effort in the system development. In this tutorial, we review formal event processing systems. High-level event ‘definitions’ impose temporal and, possibly, atemporal constraints on subevents, that is, low-level events or other high-level events. We will review a Chronicle Recognition System, the Event Calculus, ProbLog and Markov Logic Networks. The Chronicle Recognition System is a purely temporal reasoning system that allows for efficient event processing. It has been used in various domains, ranging from medical applications to computer network management. The Event Calculus allows for the representation of temporal, as well as atemporal constraints. Consequently, the Event Calculus may be used in applications requiring

This paper provides an overview of the workshop Web-Scale Classification: Web Classification in the Big Data Era which was held in New York City, on February 28th as a workshop of the seventh International Conference on Web Search and Data Mining. The goal of the workshop was to discuss and assess recent research focusing on classification and mining in Web-scale category systems. The workshop brought together members of several communities such web mining, machine learning, text classification and social media mining.

A new transfer learning method is presented in this paper, addressing a particularly hard transfer learning problem: the case where the target domain shares only a subset of its classes with the source domain and only unlabeled data are provided for the target domain. This is a situation that occurs frequently in real-world applications, such as the multiclass document classification problems that motivated our work. The proposed approach is a transfer learning variant of the Probabilistic Latent Semantic Analysis (PLSA) model that we name TL-PLSA. Unlike most approaches in the literature, TL-PLSA captures both the difference of the domains and the commonalities of the class sets, given no labelled data from the target domain. We perform experiments over three different datasets and show the difficulty of the task, as well as the promising results that we obtained with the new method.

This paper presents a method along with a set of measures for evaluating learned ontologies against gold ontologies. The proposed method transforms the ontology concepts and their properties into a vector space representation to avoid the common string matching of concepts and properties at the lexical layer. The proposed evaluation measures exploit the vector space representation and calculate the similarity of the two ontologies (learned and gold) at the lexical and relational levels. Extensive evaluation experiments are provided, which show that these measures capture accurately the deviations from the gold ontology. The proposed method is tested using the Genia and the Lonely Planet gold ontologies, as well as the ontologies in the benchmark series of the Ontology Alignment Evaluation Initiative.

This paper presents a knowledge discovery framework for the construction of Community Web Directories, a concept that we introduced in our recent work, applying personalization to Web directories. In this context, the Web directory is viewed as a thematic hierarchy and personalization is realized by constructing user community models on the basis of usage data. In contrast to most of the work on Web usage mining, the usage data that are analyzed here correspond to user navigation throughout the Web, rather than a particular Web site, exhibiting as a result a high degree of thematic diversity. For modeling the user communities, we introduce a novel methodology that combines the users' browsing behavior with thematic information from the Web directories. Following this methodology, we enhance the clustering and probabilistic approaches presented in previous work and also present a new algorithm that combines these two approaches. The resulting community models take the form of Community Web Directories. The proposed personalization methodology is evaluated both on a specialized artificial and a general-purpose Web directory, indicating its potential value to the Web user. The experiments also assess the effectiveness of the different machine learning techniques on the task.

This paper proposes a method for learning ontologies given a corpus of text documents. The method identifies concepts in documents and organizes them into a subsumption hierarchy, without presupposing the existence of a seed ontology. The method uncovers latent topics in terms of which document text is being generated. These topics form the concepts of the new ontology. This is done in a language neutral way, using probabilistic space reduction techniques over the original term space of the corpus. Given multiple sets of concepts (latent topics) being discovered, the proposed method constructs a subsumption hierarchy by performing conditional independence tests among pairs of latent topics, given a third one. The paper provides experimental results over the GENIA corpus from the domain of biomedicine.

The recognition of Proper Nouns (PNs) is considered an important task in the area of Information Retrieval and Extraction. However the high performance of most existing PN classifiers heavily depends upon the availability of large dictionaries of domain-specific Proper Nouns, and a certain amount of manual work for rule writing or manual tagging. Though it is not a heavy requirement to rely on some existing PN dictionary (often these resources are available on the web), its coverage of a domain corpus may be rather low, in absence of manual updating. In this paper we propose a technique for the automatic updating of an PN Dictionary through the cooperation of an inductive and a probabilistic classifier. In our experiments we show that, whenever an existing PN Dictionary allows the identification of 50% of the proper nouns within a corpus, our technique allows, without additional manual effort, the successful recognition of about 90% of the remaining 50%.