9,357 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
Toward Entity-Aware Search
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability
Towards Scalable Real-Time Entity Resolution using a Similarity-Aware Inverted Index Approach
Most research into entity resolution (also known as record linkage or data matching) has concentrated on the quality of the matching results. In this paper, we focus on matching time and scalability, with the aim to achieve large-scale real-time entity resolution. Traditional entity resolution techniques have as-sumed the matching of two static databases. In our networked and online world, however, it is becoming increasingly important for many organisations to be able to conduct entity resolution between a collection of often very large databases and a stream of query or update records. The matching should be done in (near) real-time, and be as automatic and accurate as possible, returning a ranked list of matched records for each given query record. This task therefore be-comes similar to querying large document collections, as done for example by Web search engines, however based on a different type of documents: structured database records that, for example, contain personal information, such as names and addresses. In this paper, we investigate inverted indexing techniques, as commonly used in Web search engines, and employ them for real-time entity resolution. We present two variations of the traditional inverted in-dex approach, aimed at facilitating fast approximate matching. We show encouraging initial results on large real-world data sets, with the inverted index ap-proaches being up-to one hundred times faster than the traditionally used standard blocking approach. However, this improved matching speed currently comes at a cost, in that matching quality for larger data sets can be lower compared to when tandard blocking is used, and thus more work is required
Incremental Entity Resolution from Linked Documents
In many government applications we often find that information about
entities, such as persons, are available in disparate data sources such as
passports, driving licences, bank accounts, and income tax records. Similar
scenarios are commonplace in large enterprises having multiple customer,
supplier, or partner databases. Each data source maintains different aspects of
an entity, and resolving entities based on these attributes is a well-studied
problem. However, in many cases documents in one source reference those in
others; e.g., a person may provide his driving-licence number while applying
for a passport, or vice-versa. These links define relationships between
documents of the same entity (as opposed to inter-entity relationships, which
are also often used for resolution). In this paper we describe an algorithm to
cluster documents that are highly likely to belong to the same entity by
exploiting inter-document references in addition to attribute similarity. Our
technique uses a combination of iterative graph-traversal, locality-sensitive
hashing, iterative match-merge, and graph-clustering to discover unique
entities based on a document corpus. A unique feature of our technique is that
new sets of documents can be added incrementally while having to re-resolve
only a small subset of a previously resolved entity-document collection. We
present performance and quality results on two data-sets: a real-world database
of companies and a large synthetically generated `population' database. We also
demonstrate benefit of using inter-document references for clustering in the
form of enhanced recall of documents for resolution.Comment: 15 pages, 8 figures, patented wor
End-to-End Entity Resolution for Big Data: A Survey
One of the most important tasks for improving data quality and the
reliability of data analytics results is Entity Resolution (ER). ER aims to
identify different descriptions that refer to the same real-world entity, and
remains a challenging problem. While previous works have studied specific
aspects of ER (and mostly in traditional settings), in this survey, we provide
for the first time an end-to-end view of modern ER workflows, and of the novel
aspects of entity indexing and matching methods in order to cope with more than
one of the Big Data characteristics simultaneously. We present the basic
concepts, processing steps and execution strategies that have been proposed by
different communities, i.e., database, semantic Web and machine learning, in
order to cope with the loose structuredness, extreme diversity, high speed and
large scale of entity descriptions used by real-world applications. Finally, we
provide a synthetic discussion of the existing approaches, and conclude with a
detailed presentation of open research directions
Distantly Labeling Data for Large Scale Cross-Document Coreference
Cross-document coreference, the problem of resolving entity mentions across
multi-document collections, is crucial to automated knowledge base construction
and data mining tasks. However, the scarcity of large labeled data sets has
hindered supervised machine learning research for this task. In this paper we
develop and demonstrate an approach based on ``distantly-labeling'' a data set
from which we can train a discriminative cross-document coreference model. In
particular we build a dataset of more than a million people mentions extracted
from 3.5 years of New York Times articles, leverage Wikipedia for distant
labeling with a generative model (and measure the reliability of such
labeling); then we train and evaluate a conditional random field coreference
model that has factors on cross-document entities as well as mention-pairs.
This coreference model obtains high accuracy in resolving mentions and entities
that are not present in the training data, indicating applicability to
non-Wikipedia data. Given the large amount of data, our work is also an
exercise demonstrating the scalability of our approach.Comment: 16 pages, submitted to ECML 201
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