3,234 research outputs found
Zero-Knowledge Functional Elementary Databases
Zero-knowledge elementary databases (ZK-EDBs) enable a prover to commit a database of key-value pairs and later provide a convincing answer to the query ``send me the value associated with \u27\u27 without revealing any extra knowledge (including the size of ). After its introduction, several works extended it to allow more expressive queries, but the expressiveness achieved so far is still limited: only a relatively simple queries--range queries over the keys and values-- can be handled by known constructions.
In this paper we introduce a new notion called zero knowledge functional elementary databases (ZK-FEDBs), which allows the most general functional queries. Roughly speaking, for any Boolean circuit , ZK-FEDBs allows the ZK-EDB prover to provide convincing answers to the queries of the form ``send me all records in satisfying ,\u27\u27 without revealing any extra knowledge (including the size of ). We present a construction of ZK-FEDBs in the random oracle model and generic group model, whose proof size is only linear in the length of record and the size of query circuit, and is independent of the size of input database .
Our technical constribution is two-fold. Firstly, we introduce a new variant of zero-knowledge sets (ZKS) which supports combined operations on sets, and present a concrete construction that is based on groups with unknown order. Secondly, we develop a tranformation that tranforms the query of Boolean circuit into a query of combined operations on related sets, which may be of independent interest
Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
Traditional semantic parsers map language onto compositional, executable
queries in a fixed schema. This mapping allows them to effectively leverage the
information contained in large, formal knowledge bases (KBs, e.g., Freebase) to
answer questions, but it is also fundamentally limiting---these semantic
parsers can only assign meaning to language that falls within the KB's
manually-produced schema. Recently proposed methods for open vocabulary
semantic parsing overcome this limitation by learning execution models for
arbitrary language, essentially using a text corpus as a kind of knowledge
base. However, all prior approaches to open vocabulary semantic parsing replace
a formal KB with textual information, making no use of the KB in their models.
We show how to combine the disparate representations used by these two
approaches, presenting for the first time a semantic parser that (1) produces
compositional, executable representations of language, (2) can successfully
leverage the information contained in both a formal KB and a large corpus, and
(3) is not limited to the schema of the underlying KB. We demonstrate
significantly improved performance over state-of-the-art baselines on an
open-domain natural language question answering task.Comment: Re-written abstract and intro, other minor changes throughout. This
version published at AAAI 201
Query Answering in Probabilistic Data and Knowledge Bases
Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory
Provenance Circuits for Trees and Treelike Instances (Extended Version)
Query evaluation in monadic second-order logic (MSO) is tractable on trees
and treelike instances, even though it is hard for arbitrary instances. This
tractability result has been extended to several tasks related to query
evaluation, such as counting query results [3] or performing query evaluation
on probabilistic trees [10]. These are two examples of the more general problem
of computing augmented query output, that is referred to as provenance. This
article presents a provenance framework for trees and treelike instances, by
describing a linear-time construction of a circuit provenance representation
for MSO queries. We show how this provenance can be connected to the usual
definitions of semiring provenance on relational instances [20], even though we
compute it in an unusual way, using tree automata; we do so via intrinsic
definitions of provenance for general semirings, independent of the operational
details of query evaluation. We show applications of this provenance to capture
existing counting and probabilistic results on trees and treelike instances,
and give novel consequences for probability evaluation.Comment: 48 pages. Presented at ICALP'1
Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses
A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses
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