Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys