Toggle navigation
Home
About
About Journal
Historical Evolution
Indexed In
Awards
Reference Index
Editorial Board
Journal Online
Archive
Project Articles
Most Download Articles
Most Read Articles
Instruction
Contribution Column
Author Guidelines
Template
FAQ
Copyright Agreement
Expenses
Academic Integrity
Contact
Contact Us
Location Map
Subscription
Advertisement
中文
Journals
Publication Years
Keywords
Search within results
(((LIU Guojun[Author]) AND 1[Journal]) AND year[Order])
AND
OR
NOT
Title
Author
Institution
Keyword
Abstract
PACS
DOI
Please wait a minute...
For Selected:
Download Citations
EndNote
Ris
BibTeX
Toggle Thumbnails
Select
Knowledge graph completion algorithm based on similarity between entities
WANG Zihan, SHAO Mingguang, LIU Guojun, GUO Maozu, BI Jiandong, LIU Yang
Journal of Computer Applications 2018, 38 (
11
): 3089-3093. DOI:
10.11772/j.issn.1001-9081.2018041238
Abstract
(
1258
)
PDF
(784KB)(
672
)
Knowledge map
Save
In order to solve the link prediction problem of knowledge graph, a shared variable network model named LCPE (Local Combination Projection Embedding) was proposed, which realized the prediction of links by embedding entities and relationships into vector space. By analyzing the Unstructured Model, it was derived that the distance between related entities' embedding was shorter in the vector space, in other words, similar entities were more likely to be related. In LCPE model, ProjE model was used based on similarity between two entities to judge whether the two entities were related and the relation type between them. The experiment shows that with the same number of parameters, the LCPE improves Mean Rank by 11 and lifts Hit@10 0.2 percentage points in dataset WN18 while improves Mean Rank 7.5 and lifts Hit@10 3.05 percentage points in dataset FB15k, which proves that the similarity between entities, as auxiliary information, can improve predictive capability of the ProjE model.
Reference
|
Related Articles
|
Metrics
Select
MTRF: a topic model with spatial information
PAN Zhiyong, LIU Yang, LIU Guojun, GUO Maozu, LI Pan
Journal of Computer Applications 2015, 35 (
10
): 2715-2720. DOI:
10.11772/j.issn.1001-9081.2015.10.2715
Abstract
(
563
)
PDF
(1118KB)(
586
)
Knowledge map
Save
To overcome the limitation of the assumptions of topic model-word independence and topic independence, a topic model which inosculated the spatial relationship of visual words was proposed, namely Markov Topic Random Field (MTRF). In addition, it was discussed that the "topic" of topic model represented the part of object in image processing. There is a high probability of the neighbor visual words generated from the same topic, and whether the visual words were generated from the same topic determined the topic was generated from Markov Random Field (MRF) or multinomial distribution of topic model. Meanwhile, both theoretical analysis and experimental results prove that "topic" of topic model appeared as mid-level feature to represent the parts of objects rather than the instances of objects. In experiments of image classification, the average accuracy of MTRF was 3.91% higher than that of Latent Dirichlet Allocation (LDA) on Caltech101 dataset, and the mean Average Precision (mAP) of MTRF was 2.03% higher than that of LDA on VOC2007 dataset. Furthermore, MTRF assigned topics to visual words more accurately and got the mid-level features which represented the parts of objects more effectively than LDA. The experimental results show that MTRF makes use of the spatial information effectively and improves the accuracy of the model.
Reference
|
Related Articles
|
Metrics