30,072 research outputs found
xDBTagger: Explainable Natural Language Interface to Databases Using Keyword Mappings and Schema Graph
Translating natural language queries (NLQ) into structured query language
(SQL) in interfaces to relational databases is a challenging task that has been
widely studied by researchers from both the database and natural language
processing communities. Numerous works have been proposed to attack the natural
language interfaces to databases (NLIDB) problem either as a conventional
pipeline-based or an end-to-end deep-learning-based solution. Nevertheless,
regardless of the approach preferred, such solutions exhibit black-box nature,
which makes it difficult for potential users targeted by these systems to
comprehend the decisions made to produce the translated SQL. To this end, we
propose xDBTagger, an explainable hybrid translation pipeline that explains the
decisions made along the way to the user both textually and visually. We also
evaluate xDBTagger quantitatively in three real-world relational databases. The
evaluation results indicate that in addition to being fully interpretable,
xDBTagger is effective in terms of accuracy and translates the queries more
efficiently compared to other state-of-the-art pipeline-based systems up to
10000 times.Comment: 20 pages, 6 figures. This work is the extended version of
arXiv:2101.04226 that appeared in PVLDB'2
The relational XQuery puzzle: a look-back on the pieces found so far
Given the tremendous versatility of relational database implementations toward awide range of database problems, it seems only natural to consider them as back-ends for XML data processing. Yet, the assumptions behind the language XQuery are considerably different to those in traditional RDBMSs. The underlying data model is a tree, data and results carry an intrinsic order, queries are described using explicit iteration and, after all, problems are everything else but regular. Solving the relational XQuery puzzle, therefore, has challenged anumber of research groups over the past years. The purpose of this article is to summarize and assess some of the results that have been obtained during this period to solve the puzzle. Our main focus is on the Pathfinder XQuery compiler, afull reference implementation of apurely relational XQuery processor. As we dissect its components, we relate them to other work in the field and also point to open problems and limitations in the context of relational XQuery processin
Knowledge Rich Natural Language Queries over Structured Biological Databases
Increasingly, keyword, natural language and NoSQL queries are being used for
information retrieval from traditional as well as non-traditional databases
such as web, document, image, GIS, legal, and health databases. While their
popularity are undeniable for obvious reasons, their engineering is far from
simple. In most part, semantics and intent preserving mapping of a well
understood natural language query expressed over a structured database schema
to a structured query language is still a difficult task, and research to tame
the complexity is intense. In this paper, we propose a multi-level
knowledge-based middleware to facilitate such mappings that separate the
conceptual level from the physical level. We augment these multi-level
abstractions with a concept reasoner and a query strategy engine to dynamically
link arbitrary natural language querying to well defined structured queries. We
demonstrate the feasibility of our approach by presenting a Datalog based
prototype system, called BioSmart, that can compute responses to arbitrary
natural language queries over arbitrary databases once a syntactic
classification of the natural language query is made
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
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