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Context sensing and complex semantic association based data space modeling method

A technology of semantic correlation and modeling method, which is applied in the fields of electronic digital data processing, special data processing applications, semi-structured data retrieval, etc., can solve the problems of low query accuracy and long average response time of keyword queries, etc., and achieve expression powerful effect

Inactive Publication Date: 2016-10-12
HARBIN ENG UNIV
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of low query accuracy of keywords and semantic relations and long average response time of keyword queries in existing methods; and propose a data space modeling method based on context awareness and complex semantic association

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  • Context sensing and complex semantic association based data space modeling method
  • Context sensing and complex semantic association based data space modeling method
  • Context sensing and complex semantic association based data space modeling method

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specific Embodiment approach 1

[0022] Specific implementation mode one: combine Figure 11 To illustrate this embodiment, the data space modeling method based on context awareness and complex semantic association in this embodiment is specifically prepared according to the following steps:

[0023] Step 1. Construct a semi-structured graph model, called the context-aware complex semantic association network model COSAN; Step 2, represent the context-aware interpretation object according to the context-aware complex semantic association network model COSAN; Step 3, according to the context Perceived interpretation objects derive context-aware basic semantic associations and complex multiple semantic associations; step 4, derive semantic association inference rules based on context-aware basic semantic associations and complex multiple semantic associations.

[0024] We first propose a semi-structured graph model called the context-aware complex semantic association network model COSAN, then describe in detai...

specific Embodiment approach 2

[0025] Specific embodiment two: the difference between this embodiment and specific embodiment one is: a kind of semi-structured graph model is built in the described step one, is referred to as context-aware complex semantic association network model COSAN; Concrete process is:

[0026] To address the problems or challenges mentioned above, this subsection proposes a semi-structured graph model, called the COSAN model. In our model, the main considerations are as follows:

[0027] (4). Not only consider the unified representation of structured, semi-structured and unstructured data, but also consider the impact of context. Furthermore, context should also be considered when expressing simple binary semantic associations.

[0028] (5). The traditional data space model puts too much emphasis on the representation of entities, ignoring the representation of complex semantic relations between entities. Specifically, this chapter formalizes it through a set of constraint sets th...

specific Embodiment approach 3

[0038] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that in the second step, the context-aware complex semantic association network model COSAN represents context-aware heterogeneous data and complex semantic relationships; the specific process is:

[0039] 1. Context-aware heterogeneous data representation

[0040] This subsection first gives some definitions, aiming to formalize the description of context-aware interpretation objects; then, an example is used to demonstrate how to represent context-aware heterogeneous data.

[0041] Definition 2.1 Context Conditions.

[0042] A context condition c is dΔν where,

[0043] 1) d is the dimension name of the context condition c, for example, time;

[0044] 2) Δ is an operator, such as, =, ≠, ≤, , ≥, in, not in, between, between + , between - , and between*;

[0045] 3) ν is a value expression corresponding to dimension d, such as {b,...,e};

[0046] Assumption: if the operator is = or ≠, the...

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Abstract

A context sensing and complex semantic association based data space modeling method is provided. The invention relates to a data space modeling method. The method can overcome the following detects of the conventional methods: 1) context sensing performance is poor; 2) semantic relation expression ability is poor; and 3) semantic association inference ability is poor. The context sensing and complex semantic association based data space modeling method includes: a step 1, establishing a semi-structured graph model called as a complex semantic association network model COSAN of context sensing; a step 2, expressing an interpretative object of the context sensing according to the complex semantic association network model COSAN of context sensing; a step 3, acquiring a basic semantic association and a complex multi- semantic association of context sensing according to the interpretative object of context sensing; and a step 4, acquiring a semantic association inference rule according to the basic semantic association and the complex multi-semantic association of context sensing. The data space modeling method can be applied to the field of data space modeling.

Description

technical field [0001] The present invention relates to data space modeling methods. Background technique [0002] Data management is an important long-term goal of the database community. However, the need to manage diverse data continues to change over time and applications. At present, in more and more data management scenarios (such as enterprise and government data management, digital library, personal information management, and scientific data management, etc.), data sources are highly heterogeneous and loosely related. Since structured, semi-structured and unstructured data from different data sources influence and interact with each other, managing these data in a convenient, integrated and guideable manner has become a major challenging task. To this end, Dataspace is proposed as a vision and as a new abstraction for data management. It advocates data integration in an incremental, pay-as-you-go manner and can model arbitrary relationships between two (multiple)...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/80
Inventor 王念滨周连科王红滨祝官文宋奎勇何鸣王瑛琦
Owner HARBIN ENG UNIV
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