77,328 research outputs found
Query-Driven Sampling for Collective Entity Resolution
Probabilistic databases play a preeminent role in the processing and
management of uncertain data. Recently, many database research efforts have
integrated probabilistic models into databases to support tasks such as
information extraction and labeling. Many of these efforts are based on batch
oriented inference which inhibits a realtime workflow. One important task is
entity resolution (ER). ER is the process of determining records (mentions) in
a database that correspond to the same real-world entity. Traditional pairwise
ER methods can lead to inconsistencies and low accuracy due to localized
decisions. Leading ER systems solve this problem by collectively resolving all
records using a probabilistic graphical model and Markov chain Monte Carlo
(MCMC) inference. However, for large datasets this is an extremely expensive
process. One key observation is that, such exhaustive ER process incurs a huge
up-front cost, which is wasteful in practice because most users are interested
in only a small subset of entities. In this paper, we advocate pay-as-you-go
entity resolution by developing a number of query-driven collective ER
techniques. We introduce two classes of SQL queries that involve ER operators
--- selection-driven ER and join-driven ER. We implement novel variations of
the MCMC Metropolis Hastings algorithm to generate biased samples and
selectivity-based scheduling algorithms to support the two classes of ER
queries. Finally, we show that query-driven ER algorithms can converge and
return results within minutes over a database populated with the extraction
from a newswire dataset containing 71 million mentions
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
The Internet has enabled the creation of a growing number of large-scale
knowledge bases in a variety of domains containing complementary information.
Tools for automatically aligning these knowledge bases would make it possible
to unify many sources of structured knowledge and answer complex queries.
However, the efficient alignment of large-scale knowledge bases still poses a
considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a
simple algorithm for aligning knowledge bases with millions of entities and
facts. SiGMa is an iterative propagation algorithm which leverages both the
structural information from the relationship graph as well as flexible
similarity measures between entity properties in a greedy local search, thus
making it scalable. Despite its greedy nature, our experiments indicate that
SiGMa can efficiently match some of the world's largest knowledge bases with
high precision. We provide additional experiments on benchmark datasets which
demonstrate that SiGMa can outperform state-of-the-art approaches both in
accuracy and efficiency.Comment: 10 pages + 2 pages appendix; 5 figures -- initial preprin
MinoanER: Schema-Agnostic, Non-Iterative, Massively Parallel Resolution of Web Entities
Entity Resolution (ER) aims to identify different descriptions in various
Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the
Variety, Volume and Veracity of entity descriptions published in the Web of
Data. To address them, we propose the MinoanER framework that simultaneously
fulfills full automation, support of highly heterogeneous entities, and massive
parallelization of the ER process. MinoanER leverages a token-based similarity
of entities to define a new metric that derives the similarity of neighboring
entities from the most important relations, as they are indicated only by
statistics. A composite blocking method is employed to capture different
sources of matching evidence from the content, neighbors, or names of entities.
The search space of candidate pairs for comparison is compactly abstracted by a
novel disjunctive blocking graph and processed by a non-iterative, massively
parallel matching algorithm that consists of four generic, schema-agnostic
matching rules that are quite robust with respect to their internal
configuration. We demonstrate that the effectiveness of MinoanER is comparable
to existing ER tools over real KBs exhibiting low Variety, but it outperforms
them significantly when matching KBs with high Variety.Comment: Presented at EDBT 2001
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Entity resolution (ER), an important and common data cleaning problem, is
about detecting data duplicate representations for the same external entities,
and merging them into single representations. Relatively recently, declarative
rules called matching dependencies (MDs) have been proposed for specifying
similarity conditions under which attribute values in database records are
merged. In this work we show the process and the benefits of integrating three
components of ER: (a) Classifiers for duplicate/non-duplicate record pairs
built using machine learning (ML) techniques, (b) MDs for supporting both the
blocking phase of ML and the merge itself; and (c) The use of the declarative
language LogiQL -an extended form of Datalog supported by the LogicBlox
platform- for data processing, and the specification and enforcement of MDs.Comment: To appear in Proc. SUM, 201
Common vocabularies for collective intelligence - work in progress
Web based applications and tools offer a great potential to increase the efficiency of information flow and communication among different agents during emergencies. Among the different factors, technical and non technical, that hinder the integration of an information model in emergency management sector, is a lack of a common, shared vocabulary. This paper furthers previous work in the area of ontology development, and presents a summary and overview of the goal, process and methodology to construct a shared set of metadata that can be used to map existing vocabulary. This paper is a work in progress report
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Entity resolution (ER), an important and common data cleaning problem, is
about detecting data duplicate representations for the same external entities,
and merging them into single representations. Relatively recently, declarative
rules called "matching dependencies" (MDs) have been proposed for specifying
similarity conditions under which attribute values in database records are
merged. In this work we show the process and the benefits of integrating four
components of ER: (a) Building a classifier for duplicate/non-duplicate record
pairs built using machine learning (ML) techniques; (b) Use of MDs for
supporting the blocking phase of ML; (c) Record merging on the basis of the
classifier results; and (d) The use of the declarative language "LogiQL" -an
extended form of Datalog supported by the "LogicBlox" platform- for all
activities related to data processing, and the specification and enforcement of
MDs.Comment: Final journal version, with some minor technical corrections.
Extended version of arXiv:1508.0601
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Measuring entity relatedness is a fundamental task for many natural language
processing and information retrieval applications. Prior work often studies
entity relatedness in static settings and an unsupervised manner. However,
entities in real-world are often involved in many different relationships,
consequently entity-relations are very dynamic over time. In this work, we
propose a neural networkbased approach for dynamic entity relatedness,
leveraging the collective attention as supervision. Our model is capable of
learning rich and different entity representations in a joint framework.
Through extensive experiments on large-scale datasets, we demonstrate that our
method achieves better results than competitive baselines.Comment: In Proceedings of CoNLL 201
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