CoChat
Sign Up
Cited by 15Advanced Graph Neural NetworksTopic ModelingSemantic Web and Ontologies

ProjFE: Prediction of fuzzy entity and relation for knowledge graph completion

Liu, Huajing, Bai, Luyi, Ma, Xiangnan, Yu, Wenting, Xu, Changming·2019·Applied Soft Computing
DOI 10.1016/j.asoc.2019.105525
Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.asoc.2019.105525

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ProjFE improves the combination operator by adding fuzzy-membership degree to improve performance with different degrees of positive and negative samples.

    “On the basis of ProjE, ProjFE [177] improves the combination operator by adding the fuzzy-membership degree, which is used to measure the degree of confidence that an entity belongs to a certain concept, to improve the performance of the model with different degrees of positive and negative samples.”

    From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [177]

Cited by

Sorted by citations

Authors
Liu, H., Bai, L., Ma, X., Yu, W., & Xu, C.
Affiliations
Northeastern University (CN)
Corresponding author
Bai, Luyi
ORCID
Venue
Applied Soft Computing
Publisher
Elsevier BV
Published
2019-08
Volume
81
Pages
105525
ISSN
1568-4946 (ISSN-L)
DOI
10.1016/j.asoc.2019.105525
OpenAlex
W2944838001
Cited by
15
References
0
Citations by year
2026: 3 · 2025: 1 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 3
FWCI
0.80
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
Applied Soft Computing (publishedVersion)
Funders
  • National Natural Science Foundation of China (61402087)
  • Natural Science Foundation of Hebei Province, China (F2019501030)
  • Fundamental Research Funds for the Central Universities, China (N172304026)
  • Hebei Education Department, China (QN2014339)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
Keywords
knowledge graph completion, membership degree, score function

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review