6 research outputs found
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints
The storage, management, and application of massive spatio-temporal data are
widely applied in various practical scenarios, including public safety.
However, due to the unique spatio-temporal distribution characteristics of
re-al-world data, most existing methods have limitations in terms of the
spatio-temporal proximity of data and load balancing in distributed storage.
There-fore, this paper proposes an efficient partitioning method of large-scale
public safety spatio-temporal data based on information loss constraints
(IFL-LSTP). The IFL-LSTP model specifically targets large-scale spatio-temporal
point da-ta by combining the spatio-temporal partitioning module (STPM) with
the graph partitioning module (GPM). This approach can significantly reduce the
scale of data while maintaining the model's accuracy, in order to improve the
partitioning efficiency. It can also ensure the load balancing of distributed
storage while maintaining spatio-temporal proximity of the data partitioning
results. This method provides a new solution for distributed storage of
mas-sive spatio-temporal data. The experimental results on multiple real-world
da-tasets demonstrate the effectiveness and superiority of IFL-LSTP
A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents
The relation triples extraction method based on table filling can address the
issues of relation overlap and bias propagation. However, most of them only
establish separate table features for each relationship, which ignores the
implicit relationship between different entity pairs and different relationship
features. Therefore, a feature reasoning relational triple extraction method
based on table filling for technological patents is proposed to explore the
integration of entity recognition and entity relationship, and to extract
entity relationship triples from multi-source scientific and technological
patents data. Compared with the previous methods, the method we proposed for
relational triple extraction has the following advantages: 1) The table filling
method that saves more running space enhances the speed and efficiency of the
model. 2) Based on the features of existing token pairs and table relations,
reasoning the implicit relationship features, and improve the accuracy of
triple extraction. On five benchmark datasets, we evaluated the model we
suggested. The result suggest that our model is advanced and effective, and it
performed well on most of these datasets
Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception
Existing research usually utilizes side information such as social network or
item attributes to improve the performance of collaborative filtering-based
recommender systems. In this paper, the knowledge graph with user perception is
used to acquire the source of side information. We proposed KGUPN to address
the limitations of existing embedding-based and path-based knowledge
graph-aware recommendation methods, an end-to-end framework that integrates
knowledge graph and user awareness into scientific and technological news
recommendation systems. KGUPN contains three main layers, which are the
propagation representation layer, the contextual information layer and
collaborative relation layer. The propagation representation layer improves the
representation of an entity by recursively propagating embeddings from its
neighbors (which can be users, news, or relationships) in the knowledge graph.
The contextual information layer improves the representation of entities by
encoding the behavioral information of entities appearing in the news. The
collaborative relation layer complements the relationship between entities in
the news knowledge graph. Experimental results on real-world datasets show that
KGUPN significantly outperforms state-of-the-art baselines in scientific and
technological news recommendation
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information
Since most scientific literature data are unlabeled, this makes unsupervised
graph-based semantic representation learning crucial. Therefore, an
unsupervised semantic representation learning method of scientific literature
based on graph attention mechanism and maximum mutual information (GAMMI) is
proposed. By introducing a graph attention mechanism, the weighted summation of
nearby node features make the weights of adjacent node features entirely depend
on the node features. Depending on the features of the nearby nodes, different
weights can be applied to each node in the graph. Therefore, the correlations
between vertex features can be better integrated into the model. In addition,
an unsupervised graph contrastive learning strategy is proposed to solve the
problem of being unlabeled and scalable on large-scale graphs. By comparing the
mutual information between the positive and negative local node representations
on the latent space and the global graph representation, the graph neural
network can capture both local and global information. Experimental results
demonstrate competitive performance on various node classification benchmarks,
achieving good results and sometimes even surpassing the performance of
supervised learning
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning
Academic networks in the real world can usually be portrayed as heterogeneous
information networks (HINs) with multi-type, universally connected nodes and
multi-relationships. Some existing studies for the representation learning of
homogeneous information networks cannot be applicable to heterogeneous
information networks because of the lack of ability to issue heterogeneity. At
the same time, data has become a factor of production, playing an increasingly
important role. Due to the closeness and blocking of businesses among different
enterprises, there is a serious phenomenon of data islands. To solve the above
challenges, aiming at the data information of scientific research teams closely
related to science and technology, we proposed an academic heterogeneous
information network embedding representation learning method based on federated
learning (FedAHE), which utilizes node attention and meta path attention
mechanism to learn low-dimensional, dense and real-valued vector
representations while preserving the rich topological information and
meta-path-based semantic information of nodes in network. Moreover, we combined
federated learning with the representation learning of HINs composed of
scientific research teams and put forward a federal training mechanism based on
dynamic weighted aggregation of parameters (FedDWA) to optimize the node
embeddings of HINs. Through sufficient experiments, the efficiency, accuracy
and feasibility of our proposed framework are demonstrated