76,008 research outputs found
Joining Extractions of Regular Expressions
Regular expressions with capture variables, also known as "regex formulas,"
extract relations of spans (interval positions) from text. These relations can
be further manipulated via Relational Algebra as studied in the context of
document spanners, Fagin et al.'s formal framework for information extraction.
We investigate the complexity of querying text by Conjunctive Queries (CQs) and
Unions of CQs (UCQs) on top of regex formulas. We show that the lower bounds
(NP-completeness and W[1]-hardness) from the relational world also hold in our
setting; in particular, hardness hits already single-character text! Yet, the
upper bounds from the relational world do not carry over. Unlike the relational
world, acyclic CQs, and even gamma-acyclic CQs, are hard to compute. The source
of hardness is that it may be intractable to instantiate the relation defined
by a regex formula, simply because it has an exponential number of tuples. Yet,
we are able to establish general upper bounds. In particular, UCQs can be
evaluated with polynomial delay, provided that every CQ has a bounded number of
atoms (while unions and projection can be arbitrary). Furthermore, UCQ
evaluation is solvable with FPT (Fixed-Parameter Tractable) delay when the
parameter is the size of the UCQ
Learning Models over Relational Data using Sparse Tensors and Functional Dependencies
Integrated solutions for analytics over relational databases are of great
practical importance as they avoid the costly repeated loop data scientists
have to deal with on a daily basis: select features from data residing in
relational databases using feature extraction queries involving joins,
projections, and aggregations; export the training dataset defined by such
queries; convert this dataset into the format of an external learning tool; and
train the desired model using this tool. These integrated solutions are also a
fertile ground of theoretically fundamental and challenging problems at the
intersection of relational and statistical data models.
This article introduces a unified framework for training and evaluating a
class of statistical learning models over relational databases. This class
includes ridge linear regression, polynomial regression, factorization
machines, and principal component analysis. We show that, by synergizing key
tools from database theory such as schema information, query structure,
functional dependencies, recent advances in query evaluation algorithms, and
from linear algebra such as tensor and matrix operations, one can formulate
relational analytics problems and design efficient (query and data)
structure-aware algorithms to solve them.
This theoretical development informed the design and implementation of the
AC/DC system for structure-aware learning. We benchmark the performance of
AC/DC against R, MADlib, libFM, and TensorFlow. For typical retail forecasting
and advertisement planning applications, AC/DC can learn polynomial regression
models and factorization machines with at least the same accuracy as its
competitors and up to three orders of magnitude faster than its competitors
whenever they do not run out of memory, exceed 24-hour timeout, or encounter
internal design limitations.Comment: 61 pages, 9 figures, 2 table
Answering Complex Questions Using Open Information Extraction
While there has been substantial progress in factoid question-answering (QA),
answering complex questions remains challenging, typically requiring both a
large body of knowledge and inference techniques. Open Information Extraction
(Open IE) provides a way to generate semi-structured knowledge for QA, but to
date such knowledge has only been used to answer simple questions with
retrieval-based methods. We overcome this limitation by presenting a method for
reasoning with Open IE knowledge, allowing more complex questions to be
handled. Using a recently proposed support graph optimization framework for QA,
we develop a new inference model for Open IE, in particular one that can work
effectively with multiple short facts, noise, and the relational structure of
tuples. Our model significantly outperforms a state-of-the-art structured
solver on complex questions of varying difficulty, while also removing the
reliance on manually curated knowledge.Comment: Accepted as short paper at ACL 201
Recursive Programs for Document Spanners
A document spanner models a program for Information Extraction (IE) as a function that takes as input a text document (string over a finite alphabet) and produces a relation of spans (intervals in the document) over a predefined schema. A well-studied language for expressing spanners is that of the regular spanners: relational algebra over regex formulas, which are regular expressions with capture variables. Equivalently, the regular spanners are the ones expressible in non-recursive Datalog over regex formulas (which extract relations that constitute the extensional database). This paper explores the expressive power of recursive Datalog over regex formulas. We show that such programs can express precisely the document spanners computable in polynomial time. We compare this expressiveness to known formalisms such as the closure of regex formulas under the relational algebra and string equality. Finally, we extend our study to a recently proposed framework that generalizes both the relational model and the document spanners
Ontology-based knowledge representation of experiment metadata in biological data mining
According to the PubMed resource from the U.S. National Library of Medicine,
over 750,000 scientific articles have been published in the ~5000 biomedical journals
worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes
Graphene: A Context-Preserving Open Information Extraction System
We introduce Graphene, an Open IE system whose goal is to generate accurate,
meaningful and complete propositions that may facilitate a variety of
downstream semantic applications. For this purpose, we transform syntactically
complex input sentences into clean, compact structures in the form of core
facts and accompanying contexts, while identifying the rhetorical relations
that hold between them in order to maintain their semantic relationship. In
that way, we preserve the context of the relational tuples extracted from a
source sentence, generating a novel lightweight semantic representation for
Open IE that enhances the expressiveness of the extracted propositions.Comment: 27th International Conference on Computational Linguistics (COLING
2018
Graphene: Semantically-Linked Propositions in Open Information Extraction
We present an Open Information Extraction (IE) approach that uses a
two-layered transformation stage consisting of a clausal disembedding layer and
a phrasal disembedding layer, together with rhetorical relation identification.
In that way, we convert sentences that present a complex linguistic structure
into simplified, syntactically sound sentences, from which we can extract
propositions that are represented in a two-layered hierarchy in the form of
core relational tuples and accompanying contextual information which are
semantically linked via rhetorical relations. In a comparative evaluation, we
demonstrate that our reference implementation Graphene outperforms
state-of-the-art Open IE systems in the construction of correct n-ary
predicate-argument structures. Moreover, we show that existing Open IE
approaches can benefit from the transformation process of our framework.Comment: 27th International Conference on Computational Linguistics (COLING
2018
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