Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1

We propose an efficient and scalable architecture for processing generalized graph-pattern queries as they are specified by the current W3C recommendation of the SPARQL 1.1 "Query Language" component. Specifically, the class of queries we consider consists of sets of SPARQL triple patterns with labeled property paths. From a relational perspective, this class resolves to conjunctive queries of relational joins with additional graph-reachability predicates. For the scalable, i.e., distributed, processing of this kind of queries over very large RDF collections, we develop a suitable partitioning and indexing scheme, which allows us to shard the RDF triples over an entire cluster of compute nodes and to process an incoming SPARQL query over all of the relevant graph partitions (and thus compute nodes) in parallel. Unlike most prior works in this field, we specifically aim at the unified optimization and distributed processing of queries consisting of both relational joins and graph-reachability predicates. All communication among the compute nodes is established via a proprietary, asynchronous communication protocol based on the Message Passing Interface

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

Generalized Lineage-Aware Temporal Windows: Supporting Outer and Anti Joins in Temporal-Probabilistic Databases

The result of a temporal-probabilistic (TP) join with negation includes, at each time point, the probability with which a tuple of a positive relation ${\bf p}$ matches none of the tuples in a negative relation ${\bf n}$, for a given join condition $\theta$. TP outer and anti joins thus resemble the characteristics of relational outer and anti joins also in the case when there exist time points at which input tuples from ${\bf p}$ have non-zero probabilities to be $true$ and input tuples from ${\bf n}$ have non-zero probabilities to be $false$, respectively. For the computation of TP joins with negation, we introduce generalized lineage-aware temporal windows, a mechanism that binds an output interval to the lineages of all the matching valid tuples of each input relation. We group the windows of two TP relations into three disjoint sets based on the way attributes, lineage expressions and intervals are produced. We compute all windows in an incremental manner, and we show that pipelined computations allow for the direct integration of our approach into PostgreSQL. We thereby alleviate the prevalent redundancies in the interval computations of existing approaches, which is proven by an extensive experimental evaluation with real-world datasets

A General Framework for Anytime Approximation in Probabilistic Databases

Anytime approximation algorithms that compute the probabilities of queries over probabilistic databases can be of great use to statistical learning tasks. Those approaches have been based so far on either (i) sampling or (ii) branch-and-bound with model-based bounds. We present here a more general branch-and-bound framework that extends the possible bounds by using 'dissociation', which yields tighter bounds.Comment: 3 pages, 2 figures, submitted to StarAI 2018 Worksho

Learning word-to-concept mappings for automatic text classification

For both classification and retrieval of natural language text documents, the standard document representation is a term vector where a term is simply a morphological normal form of the corresponding word. A potentially better approach would be to map every word onto a concept, the proper word sense and use this additional information in the learning process. In this paper we address the problem of automatically classifying natural language text documents. We investigate the effect of word to concept mappings and word sense disambiguation techniques on improving classification accuracy. We use the WordNet thesaurus as a background knowledge base and propose a generative language model approach to document classification. We show experimental results comparing the performance of our model with Naive Bayes and SVM classifiers

Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB

Guided by curated associations between genes, treatments (i.e., drugs), and diseases in pharmGKB, we constructed n-way Bayesian networks based on conditional probability tables (cpt’s) extracted from co-occurrence statistics over the entire Pubmed corpus, producing a broad-coverage analysis of the relationships between these biological entities. The networks suggest hypotheses regarding drug mechanisms, treatment biomarkers, and/or potential markers of genetic disease. The cpt’s enable Trio, an inferential database, to query indirect (inferred) relationships via an SQL-like query language

TopX : efficient and versatile top-k query processing for text, structured, and semistructured data

TopX is a top-k retrieval engine for text and XML data. Unlike Boolean engines, it stops query processing as soon as it can safely determine the k top-ranked result objects according to a monotonous score aggregation function with respect to a multidimensional query. The main contributions of the thesis unfold into four main points, confirmed by previous publications at international conferences or workshops: • Top-k query processing with probabilistic guarantees. • Index-access optimized top-k query processing. • Dynamic and self-tuning, incremental query expansion for top-k query processing. • Efficient support for ranked XML retrieval and full-text search. Our experiments demonstrate the viability and improved efficiency of our approach compared to existing related work for a broad variety of retrieval scenarios.TopX ist eine Top-k Suchmaschine für Text und XML Daten. Im Gegensatz zu Boole\u27; schen Suchmaschinen terminiert TopX die Anfragebearbeitung, sobald die k besten Ergebnisobjekte im Hinblick auf eine mehrdimensionale Anfrage gefunden wurden. Die Hauptbeiträge dieser Arbeit teilen sich in vier Schwerpunkte basierend auf vorherigen Veröffentlichungen bei internationalen Konferenzen oder Workshops: • Top-k Anfragebearbeitung mit probabilistischen Garantien. • Zugriffsoptimierte Top-k Anfragebearbeitung. • Dynamische und selbstoptimierende, inkrementelle Anfrageexpansion für Top-k Anfragebearbeitung. • Effiziente Unterstützung für XML-Anfragen und Volltextsuche. Unsere Experimente bestätigen die Vielseitigkeit und gesteigerte Effizienz unserer Verfahren gegenüber existierenden, führenden Ansätzen für eine weite Bandbreite von Anwendungen in der Informationssuche