6,471 research outputs found
The Vadalog System: Datalog-based Reasoning for Knowledge Graphs
Over the past years, there has been a resurgence of Datalog-based systems in
the database community as well as in industry. In this context, it has been
recognized that to handle the complex knowl\-edge-based scenarios encountered
today, such as reasoning over large knowledge graphs, Datalog has to be
extended with features such as existential quantification. Yet, Datalog-based
reasoning in the presence of existential quantification is in general
undecidable. Many efforts have been made to define decidable fragments. Warded
Datalog+/- is a very promising one, as it captures PTIME complexity while
allowing ontological reasoning. Yet so far, no implementation of Warded
Datalog+/- was available. In this paper we present the Vadalog system, a
Datalog-based system for performing complex logic reasoning tasks, such as
those required in advanced knowledge graphs. The Vadalog system is Oxford's
contribution to the VADA research programme, a joint effort of the universities
of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the
main contribution of this paper, we illustrate the first implementation of
Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive
termination control strategy. We also provide a comprehensive experimental
evaluation.Comment: Extended version of VLDB paper
<https://doi.org/10.14778/3213880.3213888
Functional Dependencies Unleashed for Scalable Data Exchange
We address the problem of efficiently evaluating target functional
dependencies (fds) in the Data Exchange (DE) process. Target fds naturally
occur in many DE scenarios, including the ones in Life Sciences in which
multiple source relations need to be structured under a constrained target
schema. However, despite their wide use, target fds' evaluation is still a
bottleneck in the state-of-the-art DE engines. Systems relying on an all-SQL
approach typically do not support target fds unless additional information is
provided. Alternatively, DE engines that do include these dependencies
typically pay the price of a significant drop in performance and scalability.
In this paper, we present a novel chase-based algorithm that can efficiently
handle arbitrary fds on the target. Our approach essentially relies on
exploiting the interactions between source-to-target (s-t) tuple-generating
dependencies (tgds) and target fds. This allows us to tame the size of the
intermediate chase results, by playing on a careful ordering of chase steps
interleaving fds and (chosen) tgds. As a direct consequence, we importantly
diminish the fd application scope, often a central cause of the dramatic
overhead induced by target fds. Moreover, reasoning on dependency interaction
further leads us to interesting parallelization opportunities, yielding
additional scalability gains. We provide a proof-of-concept implementation of
our chase-based algorithm and an experimental study aiming at gauging its
scalability with respect to a number of parameters, among which the size of
source instances and the number of dependencies of each tested scenario.
Finally, we empirically compare with the latest DE engines, and show that our
algorithm outperforms them
Using Ontologies for Semantic Data Integration
While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed
Dealing with Inconsistency and Incompleteness in Data Integration
Marco Schaerf, Giuseppe Di Battista, Domenico SaccÃ
WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
We present WeaverBird, an intelligent dialogue system designed specifically
for the finance domain. Our system harnesses a large language model of GPT
architecture that has been tuned using extensive corpora of finance-related
text. As a result, our system possesses the capability to understand complex
financial queries, such as "How should I manage my investments during
inflation?", and provide informed responses. Furthermore, our system
incorporates a local knowledge base and a search engine to retrieve relevant
information. The final responses are conditioned on the search results and
include proper citations to the sources, thus enjoying an enhanced credibility.
Through a range of finance-related questions, we have demonstrated the superior
performance of our system compared to other models. To experience our system
firsthand, users can interact with our live demo at
https://weaverbird.ttic.edu, as well as watch our 2-min video illustration at
https://www.youtube.com/watch?v=fyV2qQkX6Tc
Provenance in Collaborative Data Sharing
This dissertation focuses on recording, maintaining and exploiting provenance information in Collaborative Data Sharing Systems (CDSS). These are systems that support data sharing across loosely-coupled, heterogeneous collections of relational databases related by declarative schema mappings. A fundamental challenge in a CDSS is to support the capability of update exchange --- which publishes a participant\u27s updates and then translates others\u27 updates to the participant\u27s local schema and imports them --- while tolerating disagreement between them and recording the provenance of exchanged data, i.e., information about the sources and mappings involved in their propagation. This provenance information can be useful during update exchange, e.g., to evaluate provenance-based trust policies. It can also be exploited after update exchange, to answer a variety of user queries, about the quality, uncertainty or authority of the data, for applications such as trust assessment, ranking for keyword search over databases, or query answering in probabilistic databases.
To address these challenges, in this dissertation we develop a novel model of provenance graphs that is informative enough to satisfy the needs of CDSS users and captures the semantics of query answering on various forms of annotated relations. We extend techniques from data integration, data exchange, incremental view maintenance and view update to define the formal semantics of unidirectional and bidirectional update exchange. We develop algorithms to perform update exchange incrementally while maintaining provenance information. We present strategies for implementing our techniques over an RDBMS and experimentally demonstrate their viability in the Orchestra prototype system. We define ProQL, a query language for provenance graphs that can be used by CDSS users to combine data querying with provenance testing as well as to compute annotations for their data, based on their provenance, that are useful for a variety of applications. Finally, we develop a prototype implementation ProQL over an RDBMS and indexing techniques to speed up provenance querying, evaluate experimentally the performance of provenance querying and the benefits of our indexing techniques
- …