Relational Boosted Bandits

Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representation, which makes them unfeasible for real-world domains like social networks are inherently relational. We propose Relational Boosted Bandits(RB2), acontextual bandits algorithm for relational domains based on (relational) boosted trees. RB2 enables us to learn interpretable and explainable models due to the more descriptive nature of the relational representation. We empirically demonstrate the effectiveness and interpretability of RB2 on tasks such as link prediction, relational classification, and recommendations.Comment: 8 pages, 3 figure

Extending Event-Driven Architecture for Proactive Systems

ABSTRACT Proactive Event-Driven Computing is a new paradigm, in which a decision is not made due to explicit users' requests nor is it made as a response to past events. Rather, the decision is autonomously triggered by forecasting future states. Proactive event-driven computing requires a departure from current event-driven architectures to ones capable of handling uncertainty and future events, and real-time decision making. We present a proactive event-driven architecture for Scalable Proactive Event-Driven Decision-making (SPEEDD), which combines these capabilities. The proposed architecture is composed of three main components: complex event processing, real-time decision making, and visualization. This architecture is instantiated by a real use case from the traffic management domain. In the future, the results of actual implementations of the use case will help us revise and refine the proposed architecture

Exploiting prior knowledge and latent variable representations for the statistical modeling and probabilistic querying of large knowledge graphs

Large knowledge graphs increasingly add great value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. These applications include Google search, Bing search, IBM’s Watson, but also smart mobile assistants as Apple’s Siri, Google Now or Microsoft’s Cortana. Popular knowledge graphs like DBpedia, YAGO or Freebase store a broad range of facts about the world, to a large extent derived from Wikipedia, currently the biggest web encyclopedia. In addition to these freely accessible open knowledge graphs, commercial ones have also evolved including the well-known Google Knowledge Graph or Microsoft’s Satori. Since incompleteness and veracity of knowledge graphs are known problems, the statistical modeling of knowledge graphs has increasingly gained attention in recent years. Some of the leading approaches are based on latent variable models which show both excellent predictive performance and scalability. Latent variable models learn embedding representations of domain entities and relations (representation learning). From these embeddings, priors for every possible fact in the knowledge graph are generated which can be exploited for data cleansing, completion or as prior knowledge to support triple extraction from unstructured textual data as successfully demonstrated by Google’s Knowledge-Vault project. However, large knowledge graphs impose constraints on the complexity of the latent embeddings learned by these models. For graphs with millions of entities and thousands of relation-types, latent variable models are required to exploit low dimensional embeddings for entities and relation-types to be tractable when applied to these graphs. The work described in this thesis extends the application of latent variable models for large knowledge graphs in three important dimensions. First, it is shown how the integration of ontological constraints on the domain and range of relation-types enables latent variable models to exploit latent embeddings of reduced complexity for modeling large knowledge graphs. The integration of this prior knowledge into the models leads to a substantial increase both in predictive performance and scalability with improvements of up to 77% in link-prediction tasks. Since manually designed domain and range constraints can be absent or fuzzy, we also propose and study an alternative approach based on a local closed-world assumption, which derives domain and range constraints from observed data without the need of prior knowledge extracted from the curated schema of the knowledge graph. We show that such an approach also leads to similar significant improvements in modeling quality. Further, we demonstrate that these two types of domain and range constraints are of general value to latent variable models by integrating and evaluating them on the current state of the art of latent variable models represented by RESCAL, Translational Embedding, and the neural network approach used by the recently proposed Google Knowledge Vault system. In the second part of the thesis it is shown that the just mentioned three approaches all perform well, but do not share many commonalities in the way they model knowledge graphs. These differences can be exploited in ensemble solutions which improve the predictive performance even further. The third part of the thesis concerns the efficient querying of the statistically modeled knowledge graphs. This thesis interprets statistically modeled knowledge graphs as probabilistic databases, where the latent variable models define a probability distribution for triples. From this perspective, link-prediction is equivalent to querying ground triples which is a standard functionality of the latent variable models. For more complex querying that involves e.g. joins and projections, the theory on probabilistic databases provides evaluation rules. In this thesis it is shown how the intrinsic features of latent variable models can be combined with the theory of probabilistic databases to realize efficient probabilistic querying of the modeled graphs

A study on the Probabilistic Interval-based Event Calculus

Η Αναγνώριση Σύνθετων Γεγονότων είναι το πεδίο εκείνο της Τεχνητής Νοημοσύνης το οποίο αποσκοπεί στο σχεδιασμό και την κατασκευή συστημάτων τα οποία επεξεργάζονται γρήγορα μεγάλες και πιθανώς ετερογενείς ροές δεδομένων και τα οποία είναι σε θέση να αναγνωρίζουν εγκαίρως μη τετριμμένα και ενδιαφέροντα συμβάντα, βάσει κατάλληλων ορισμών που προέρχονται από ειδικούς. Σκοπός ενός τέτοιου συστήματος είναι η αυτοματοποιημένη εποπτεία πολύπλοκων και απαιτητικών καταστάσεων και η υποβοήθηση της λήψης αποφάσεων από τον άνθρωπο. Η αβεβαιότητα και ο θόρυβος είναι έννοιες που υπεισέρχονται φυσικά σε τέτοιες ροές δεδομένων και συνεπώς, καθίσταται απαραίτητη η χρήση της Θεωρίας Πιθανοτήτων για την αντιμετώπισή τους. Η πιθανοτική Αναγνώριση Σύνθετων Γεγονότων μπορεί να πραγματοποιηθεί σε επίπεδο χρονικής στιγμής ή σε επίπεδο χρονικού διαστήματος. Η παρούσα εργασία εστιάζει στον PIEC, έναν σύγχρονο αλγόριθμο για την Αναγνώριση Σύνθετων Γεγονότων με τη χρήση πιθανοτικών, μέγιστων διαστημάτων. Αρχικά παρουσιάζουμε τον αλγόριθμο και τον ερευνούμε ενδελεχώς. Μελετούμε την ορθότητά του μέσα από μια σειρά μαθηματικών αποδείξεων περί της ευρωστίας (soundness) και της πληρότητάς του (completeness). Κατόπιν, παραθέτουμε εκτενή πειραματική αποτίμηση του υπό μελέτη αλγορίθμου και σύγκρισή του με συστήματα πιθανοτικής Αναγνώρισης Γεγονότων σε επίπεδο χρονικών σημείων. Τα αποτελέσματά μας δείχνουν ότι ο PIEC επιδεικνύει σταθερά καλύτερη Ανάκληση (Recall), παρουσιάζοντας, ωστόσο κάποιες απώλειες σε Ακρίβεια (Precision) σε ορισμένες περιπτώσεις. Για τον λόγο αυτόν, εμβαθύνουμε και εξετάζουμε συγκεκριμένες περιπτώσεις στις οποίες ο PIEC αποδίδει καλύτερα, καθώς και άλλες στις οποίες παράγει αποτελέσματα υποδεέστερα των παραδοσιακών μεθόδων σημειακής αναγνώρισης, σε μια προσπάθεια να εντοπίσουμε και να διατυπώσουμε τις δυνατότητες αλλά και τις αδυναμίες του αλγορίθμου. Τέλος, θέτουμε τις γενικές κατευθυντήριες γραμμές για περαιτέρω έρευνα στο εν λόγω ζήτημα, τμήματα της οποίας βρίσκονται ήδη σε εξέλιξη.Complex Event Recognition is the subdivision of Artificial Intelligence that aims to design and construct systems that quickly process large and often heterogeneous streams of data and timely deduce – based on definitions set by domain experts – the occurrence of non-trivial and interesting incidents. The purpose of such systems is to provide useful insights into involved and demanding situations that would otherwise be difficult to monitor, and to assist decision making. Uncertainty and noise are inherent in such data streams and therefore, Probability Theory becomes necessary in order to deal with them. The probabilistic recognition of Complex Events can be done in a timepoint-based or an interval-based manner. This thesis focuses on PIEC, a state-of-the-art probabilistic, interval-based Complex Event Recognition algorithm. We present the algorithm and examine it in detail. We study its correctness through a series of mathematical proofs of its soundness and completeness. Afterwards, we provide thorough experimental evaluation and comparison to point-based probabilistic Event Recognition methods. Our evaluation shows that PIEC consistently displays better Recall measures, often at the expense of a generally worse Precision. We then focus on cases where PIEC performs significantly better and cases where it falls short, in an effort to detect and state its main strengths and weaknesses. We also set the general directions for further research on the topic, parts of which are already in progress

In Online Structure Learning for Markov Logic Networks

Abstract. Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which becomes computationally expensive and eventually infeasible for large datasets with thousands of training examples which may not even all fit in main memory. To address this issue, previous work has used online learning to train MLNs. However, they all assume that the model’s structure (set of logical clauses) is given, and only learn the model’s parameters. However, the input structure is usually incomplete, so it should also be updated. In this work, we present OSL—the first algorithm that performs both online structure and parameter learning for MLNs. Experimental results on two realworld datasets for natural-language field segmentation show that OSL outperforms systems that cannot revise structure.