Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Crowdsourced Truth Discovery in the Presence of Hierarchies for
  Knowledge Fusion   Existing works for truth discovery in categorical data usually assume that
claimed values are mutually exclusive and only one among them is correct.
However, many claimed values are not mutually exclusive even for functional
predicates due to their hierarchical structures. Thus, we need to consider the
hierarchical structure to effectively estimate the trustworthiness of the
sources and infer the truths. We propose a probabilistic model to utilize the
hierarchical structures and an inference algorithm to find the truths. In
addition, in the knowledge fusion, the step of automatically extracting
information from unstructured data (e.g., text) generates a lot of false
claims. To take advantages of the human cognitive abilities in understanding
unstructured data, we utilize crowdsourcing to refine the result of the truth
discovery. We propose a task assignment algorithm to maximize the accuracy of
the inferred truths. The performance study with real-life datasets confirms the
effectiveness of our truth inference and task assignment algorithms.

23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat