To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined.
Multi-relational data mining algorithms emerged to enable the extraction of multiple relation patterns, with efficiency and efficacy, without the necessity of joining the data into a single table [4, 5].
Community structures and relation patterns, and ranking them for social networks provide us with great knowledge about network.
Besides, semi-supervised methods for Relation Extraction are usually applied over a great volume of data, such as Web pages, and the outcome is usually a large number of new relation patterns (e.g., Open IE).
We address the challenge of learning complex relation patterns from data objects exhibiting heterogeneous properties, and develop an effective multi-view multimodal similarity learning model with much improved learning performance and model interpretability.
Second, we obtained passenger social relation patterns by using the method of questionnaire investigation.
According to the survey data, we summarized the major social relation patterns of passengers and calculated the average number of passengers in each social relation pattern, which respectively are 3 (relatives), 4 (friends), 6 (colleagues), 1 (individuals).
In this part, we define the object structure as "hasProperty" relation patterns between property nodes P and object nodes O.
Based on the result above, the research data of passenger social relation patterns in the airport terminal are obtained.
It can simultaneously learn the relation pattern's information and the semantic properties of given entities.
