A semantic approximate query method for RDF knowledge map

A technology of knowledge graph and query method, which is applied in the field of semantic approximate query oriented to RDF knowledge graph, and can solve problems such as query accuracy and performance.

Active Publication Date: 2018-12-07
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art, propose a semantic approximation query method for RDF knowledge graph, and effectively solve the query accuracy a

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  • A semantic approximate query method for RDF knowledge map
  • A semantic approximate query method for RDF knowledge map
  • A semantic approximate query method for RDF knowledge map

Examples

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Example Embodiment

[0042] The following examples are used in conjunction with the drawings to demonstrate the specific implementation of the present invention. The overall system architecture of the present invention is as figure 1 As shown, each stage is processed in sequence as follows:

[0043] Step 1: Offline corpus generation and training stage.

[0044] This stage includes transforming the RDF knowledge graph into a trainable text corpus, and using the text embedding model to perform context-sensitive semantic learning on the text corpus to train the semantic vectors of entities and predicates. It mainly includes the following three steps:

[0045] Step 1.1: Entity division

[0046] For the entire English wiki library ( https: / / www.wikipedia.org / ) RDF knowledge graph, according to the type of entity, entities of the same type are gathered into one category, and all entities in the RDF knowledge graph are divided into n entity sets E={E k |1≤k≤n,k∈N}, where each entity set E k Contains m entit...

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Abstract

The invention discloses a semantic approximate query method for an RDF knowledge map. The offline stage of the invention comprises the following steps: firstly, the RDF knowledge map is divided into RDF knowledge maps according to the semantic locality characteristics of entities and predicates of the RDF knowledge map, and the divided knowledge maps are generated into trainable text corpus; secondly, context-sensitive semantic learning is performed on the text corpus using the text embedding model, and the semantic vectors of entities and predicates are obtained. In the on-line phase: firstly, the syntax of SPARQL query submitted by users is analyzed, and the semantics of the predicates is extended; secondly, an approximate query based on predicate semantic similarity is carried out froma given entity, and the semantic approximate query results are obtained. The method utilizes semantic locality features to carry out context-sensitive semantic learning on the RDF knowledge map, thereby supporting fuzzy query application of the RDF knowledge map, and returning approximate query results satisfying user query intent in real time.

Description

technical field [0001] The invention relates to the technical field of knowledge graph query, in particular to a semantic approximate query method for RDF knowledge graph. Background technique [0002] In recent years, with the rise of a new generation of large-scale Internet intelligent applications such as social networks and e-commerce, the scale, update rate, and complexity of various types of data in the network are increasing day by day. In the process of big data analysis, the Knowledge Graph based on RDF (Resource Description Framework), as a data representation that effectively describes big data and its complex relationships, plays an increasingly important role. [0003] At present, the query methods for RDF knowledge graphs can be divided into the following two categories according to the technologies used and the application scenarios: (1) based on the subgraph traversal and matching algorithm, the precise query for a given query graph is realized, that is, the ...

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F40/30
Inventor 徐小良葛张鹏王宇翔
Owner HANGZHOU DIANZI UNIV
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