Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Large Margin Nearest Neighbor Embedding for Knowledge Representation   Traditional way of storing facts in triplets ({\it head\_entity, relation,
tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively
displayed and easily acquired by mankind, but hardly computed or even reasoned
by AI machines. Inspired by the success in applying {\it Distributed
Representations} to AI-related fields, recent studies expect to represent each
entity and relation with a unique low-dimensional embedding, which is different
from the symbolic and atomic framework of displaying knowledge in triplets. In
this way, the knowledge computing and reasoning can be essentially facilitated
by means of a simple {\it vector calculation}, i.e. ${\bf h} + {\bf r} \approx
{\bf t}$. We thus contribute an effective model to learn better embeddings
satisfying the formula by pulling the positive tail entities ${\bf t^{+}}$ to
get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and
simultaneously pushing the negatives ${\bf t^{-}}$ away from the positives
${\bf t^{+}}$ via keeping a {\it Large Margin}. We also design a corresponding
learning algorithm to efficiently find the optimal solution based on {\it
Stochastic Gradient Descent} in iterative fashion. Quantitative experiments
illustrate that our approach can achieve the state-of-the-art performance,
compared with several latest methods on some benchmark datasets for two
classical applications, i.e. {\it Link prediction} and {\it Triplet
classification}. Moreover, we analyze the parameter complexities among all the
evaluated models, and analytical results indicate that our model needs fewer
computational resources on outperforming the other methods.

36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat