12,876 research outputs found
Thermal entanglement in a two-spin-qutrit system under a nonuniform external magnetic field
The thermal entanglement in a two-spin-qutrit system with two spins coupled
by exchange interaction under a magnetic field in an arbitrary direction is
investigated. Negativity, the measurement of entanglement, is calculated. We
find that for any temperature the evolvement of negativity is symmetric with
respect to magnetic field. The behavior of negativity is presented for four
different cases. The results show that for different temperature, different
magnetic field give maximum entanglement. Both the parallel and antiparallel
magnetic field cases are investigated qualitatively (not quantitatively) in
detail, we find that the entanglement may be enhanced under an antiparallel
magnetic field.Comment: 2 eps figure
Knowledge Graph Embedding with Iterative Guidance from Soft Rules
Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of
current research. Combining such an embedding model with logic rules has
recently attracted increasing attention. Most previous attempts made a one-time
injection of logic rules, ignoring the interactive nature between embedding
learning and logical inference. And they focused only on hard rules, which
always hold with no exception and usually require extensive manual effort to
create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a
novel paradigm of KG embedding with iterative guidance from soft rules. RUGE
enables an embedding model to learn simultaneously from 1) labeled triples that
have been directly observed in a given KG, 2) unlabeled triples whose labels
are going to be predicted iteratively, and 3) soft rules with various
confidence levels extracted automatically from the KG. In the learning process,
RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and
integrates such newly labeled triples to update the embedding model. Through
this iterative procedure, knowledge embodied in logic rules may be better
transferred into the learned embeddings. We evaluate RUGE in link prediction on
Freebase and YAGO. Experimental results show that: 1) with rule knowledge
injected iteratively, RUGE achieves significant and consistent improvements
over state-of-the-art baselines; and 2) despite their uncertainties,
automatically extracted soft rules are highly beneficial to KG embedding, even
those with moderate confidence levels. The code and data used for this paper
can be obtained from https://github.com/iieir-km/RUGE.Comment: To appear in AAAI 201
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