Learning Tuple Probabilities

Learning the parameters of complex probabilistic-relational models from labeled training data is a standard technique in machine learning, which has been intensively studied in the subfield of Statistical Relational Learning (SRL), but---so far---this is still an under-investigated topic in the context of Probabilistic Databases (PDBs). In this paper, we focus on learning the probability values of base tuples in a PDB from labeled lineage formulas. The resulting learning problem can be viewed as the inverse problem to confidence computations in PDBs: given a set of labeled query answers, learn the probability values of the base tuples, such that the marginal probabilities of the query answers again yield in the assigned probability labels. We analyze the learning problem from a theoretical perspective, cast it into an optimization problem, and provide an algorithm based on stochastic gradient descent. Finally, we conclude by an experimental evaluation on three real-world and one synthetic dataset, thus comparing our approach to various techniques from SRL, reasoning in information extraction, and optimization

Efficient querying and learning in probabilistic and temporal databases

Probabilistic databases store, query, and manage large amounts of uncertain information. This thesis advances the state-of-the-art in probabilistic databases in three different ways: 1. We present a closed and complete data model for temporal probabilistic databases and analyze its complexity. Queries are posed via temporal deduction rules which induce lineage formulas capturing both time and uncertainty. 2. We devise a methodology for computing the top-k most probable query answers. It is based on first-order lineage formulas representing sets of answer candidates. Theoretically derived probability bounds on these formulas enable pruning low-probability answers. 3. We introduce the problem of learning tuple probabilities which allows updating and cleaning of probabilistic databases. We study its complexity, characterize its solutions, cast it into an optimization problem, and devise an approximation algorithm based on stochastic gradient descent. All of the above contributions support consistency constraints and are evaluated experimentally.Probabilistische Datenbanken können große Mengen an ungewissen Informationen speichern, anfragen und verwalten. Diese Doktorarbeit treibt den Stand der Technik in diesem Gebiet auf drei Arten vorran: 1. Ein abgeschlossenes und vollständiges Datenmodell für temporale, probabilistische Datenbanken wird präsentiert. Anfragen werden mittels Deduktionsregeln gestellt, welche logische Formeln induzieren, die sowohl Zeit als auch Ungewissheit erfassen. 2. Ein Methode zur Berechnung der k Anworten höchster Wahrscheinlichkeit wird entwickelt. Sie basiert auf logischen Formeln erster Stufe, die Mengen an Antwortkandidaten repräsentieren. Beschränkungen der Wahrscheinlichkeit dieser Formeln ermöglichen das Kürzen von Antworten mit niedriger Wahrscheinlichkeit. 3. Das Problem des Lernens von Tupelwahrscheinlichkeiten für das Aktualisieren und Bereiningen von probabilistischen Datenbanken wird eingeführt, auf Komplexität und Lösungen untersucht, als Optimierungsproblem dargestellt und von einem stochastischem Gradientenverfahren approximiert. All diese Beiträge unterstützen Konsistenzbedingungen und wurden experimentell analysiert

Effizientes Anfragen und Lernen in probabilistischen und temporalen Datenbanken

Probabilistic databases store, query, and manage large amounts of uncertain information. This thesis advances the state-of-the-art in probabilistic databases in three different ways: 1. We present a closed and complete data model for temporal probabilistic databases and analyze its complexity. Queries are posed via temporal deduction rules which induce lineage formulas capturing both time and uncertainty. 2. We devise a methodology for computing the top-k most probable query answers. It is based on first-order lineage formulas representing sets of answer candidates. Theoretically derived probability bounds on these formulas enable pruning low-probability answers. 3. We introduce the problem of learning tuple probabilities which allows updating and cleaning of probabilistic databases. We study its complexity, characterize its solutions, cast it into an optimization problem, and devise an approximation algorithm based on stochastic gradient descent. All of the above contributions support consistency constraints and are evaluated experimentally.Probabilistische Datenbanken können große Mengen an ungewissen Informationen speichern, anfragen und verwalten. Diese Doktorarbeit treibt den Stand der Technik in diesem Gebiet auf drei Arten vorran: 1. Ein abgeschlossenes und vollständiges Datenmodell für temporale, probabilistische Datenbanken wird präsentiert. Anfragen werden mittels Deduktionsregeln gestellt, welche logische Formeln induzieren, die sowohl Zeit als auch Ungewissheit erfassen. 2. Ein Methode zur Berechnung der k Anworten höchster Wahrscheinlichkeit wird entwickelt. Sie basiert auf logischen Formeln erster Stufe, die Mengen an Antwortkandidaten repräsentieren. Beschränkungen der Wahrscheinlichkeit dieser Formeln ermöglichen das Kürzen von Antworten mit niedriger Wahrscheinlichkeit. 3. Das Problem des Lernens von Tupelwahrscheinlichkeiten für das Aktualisieren und Bereiningen von probabilistischen Datenbanken wird eingeführt, auf Komplexität und Lösungen untersucht, als Optimierungsproblem dargestellt und von einem stochastischem Gradientenverfahren approximiert. All diese Beiträge unterstützen Konsistenzbedingungen und wurden experimentell analysiert

A System for Compositional Verification of Asynchronous Objects

We present a semantics, calculus, and system for compositional verification of Creol, an object-oriented modelling language for concurrent distributed applications. The system is an instance of KeY, a framework for object oriented software verification, which has so far been applied foremost to sequential Java. Building on KeY characteristic concepts, like dynamic logic, sequent calculus, symbolic execution via explicit substitutions, and the taclet rule language, the presented system addresses functional correctness of Creol models featuring local cooperative thread parallelism and global communication via asynchronous method calls. The calculus heavily operates on communication histories specified by the interfaces of Creol units. Two example scenarios demonstrate the usage of the system. This article extends the conference paper of Ahrendt and Dylla (2009) with a denotational semantics of Creol and an assumption-commitment style semantics of the logic

Resolving Temporal Conflicts in Inconsistent RDF Knowledge Bases

Abstract: Recent trends in information extraction have allowed us to not only extract large semantic knowledge bases from structured or loosely structured Web sources, but to also extract additional annotations along with the RDF facts these knowledge bases contain. Among the most important types of annotations are spatial and temporal annotations. In particular the latter temporal annotations help us to reflect that a majority of facts is not static but highly ephemeral in the real world, i.e., facts are valid for only a limited amount of time, or multiple facts stand in temporal dependencies with each other. In this paper, we present a declarative reasoning framework to express and process temporal consistency constraints and queries via first-order logical predicates. We define a subclass of first-order constraints with temporal predicates for which the knowledge base is guaranteed to be satisfiable. Moreover, we devise efficient grounding and approximation algorithms for this class of first order constraints, which can be solved within our framework. Specifically, we reduce the problem of finding a consistent subset of time-annotated facts to a scheduling problem and give an approximation algorithm for it. Experiments over a large temporal knowledge base (T-YAGO) demonstrate the scalability and excellent approximation performance of our framework.

A Temporal-Probabilistic Database Model for Information Extraction

Temporal annotations of facts are a key component both for building a high-accuracy knowledge base and for answering queries over the resulting temporal knowledge base with high precision and recall. In this paper, we present a temporalprobabilistic database model for cleaning uncertain temporal facts obtained from information extraction methods. Specifically, we consider a combination of temporal deduction rules, temporal consistency constraints and probabilistic inference based on the common possible-worlds semantics with data lineage, and we study the theoretical properties of this data model. We further develop a query engine that is capable of scaling to very large temporal knowledge bases, with millions of uncertain facts and hundreds of thousands of grounded rules. Our experiments over two real-world datasets demonstrate the increased robustness of our approach compared to related techniques based on constraint solving via Integer Linear Programming (ILP) and probabilistic inference via Markov Logic Networks (MLNs). We are also able to show that our runtime performance is more than competitive to ILP solvers and the fastest available, probabilistic but non-temporal, database engines. 1

Top-k Query Processing in Probabilistic Databases with Non-Materialized Views

Abstract—We investigate a novel approach of computing confidence bounds for top-k ranking queries in probabilistic databases with non-materialized views. Unlike related approaches, we present an exact pruning algorithm for finding the topranked query answers according to their marginal probabilities without the need to first materialize all answer candidates via the views. Specifically, we consider conjunctive queries over multiple levels of select-project-join views, the latter of which are cast into Datalog rules which we ground in a top-down fashion directly at query processing time. To our knowledge, this work is the first to address integrated data and confidence computations for intensional query evaluations in the context of probabilistic databases by considering confidence bounds over first-order lineage formulas. We extend our query processing techniques by a tool-suite of scheduling strategies based on selectivity estimation and the expected impact on confidence bounds. Further extensions to our query processing strategies include improved top-k bounds in the case when sorted relations are available as input, as well as the consideration of recursive rules. Experiments with large datasets demonstrate significant runtime improvements of our approach compared to both exact and sampling-based top-k methods over probabilistic data. I

Coupling Label Propagation and Constraints for Temporal Fact Extraction

International audienc

Coupling Label Propagation and Constraints for Temporal Fact Extraction

The Web and digitized text sources contain a wealth of information about named entities such as politicians, actors, companies, or cultural landmarks. Extracting this information has enabled the automated construction of large knowledge bases, containing hundred millions of binary relationships or attribute values about these named entities. However, in reality most knowledge is transient, i.e. changes over time, requiring a temporal dimension in fact extraction. In this paper we develop a methodology that combines label propagation with constraint reasoning for temporal fact extraction. Label propagation aggressively gathers fact candidates, and an Integer Linear Program is used to clean out false hypotheses that violate temporal constraints. Our method is able to improve on recall while keeping up with precision, which we demonstrate by experiments with biography-style Wikipedia pages and a large corpus of news articles.