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Sub-graph-based tense knowledge graph question and answer method

A knowledge graph and temporal technology, applied in the subgraph-based temporal knowledge graph question answering field, to achieve good evaluation and best performance

Pending Publication Date: 2022-07-05
NAT UNIV OF DEFENSE TECH
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The reason why TempoQR can achieve excellent results without considering complex constraints is mainly due to the pseudo-temporal problem in CronQuestions

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Embodiment Construction

[0076] The present invention is further described below in conjunction with the accompanying drawings, but the present invention is not limited in any way, and any transformation or replacement based on the teaching of the present invention belongs to the protection scope of the present invention.

[0077] Temporal knowledge graph K:=(E, R, T, F) is a multi-relational directed graph with edges with timestamps between entities. A fact in K can be formalized as (s, r, o, τ) ∈ F, where s, o ∈ E represent subject and object entities, r ∈ R represents the relationship between them, and τ ∈ T is the Relationship-related timestamps. Temporal knowledge graph embedding method learns each of K and a K-dimensional vector of τ∈T such that the score ratio of each fact (s, r, o, τ) ∈ F by the scoring function φ( ) has a high score, which is formally expressed as φ(e s ,v r ,e o ,t τ )>φ(e s′ ,v r′ ,e o′ ,t τ′ ).

[0078] Given a temporal knowledge graph K and a natural langua...

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Abstract

The invention discloses a tense knowledge graph question answering method based on a subgraph, which comprises the following steps of: extracting and analyzing time information hidden in a question through knowledge in a tense knowledge graph, and replacing the question in a natural language format with a simplified question by using a regular expression; converting the problem added with the time constraint into vector representation, and obtaining candidate entities and semantic scores thereof by using a tense knowledge graph scoring function and a time sensitive function; constructing a tense neighbor sub-graph for each problem; cutting the tense neighbor sub-graph through time constraint; performing quantitative scoring on each entity in the tense neighbor subgraph by using a time activation function to obtain a subgraph reasoning score; and fusing the score semantic score and the sub-graph reasoning score to obtain a final answer. According to the method, the capability of identifying the time information contained in the problem is improved; and reasoning answers meeting time limitation through the time sub-graph to obtain reliable answers of the questions.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a question and answer method based on a subgraph of a temporal knowledge graph. Background technique [0002] Knowledge Graph Question Answering (KGQA) requires the system to reason over the Knowledge Graph (KG) to provide answers given a natural language question. However, in real life, questions often contain time constraints, such as, “Who won the Nobel Prize in 2019?” Due to the lack of time information, traditional knowledge graphs are difficult to support inferences about answers. In recent years, Temporal Knowledge Graph (TKG) has flourished, in which the facts involved have a temporal property. When faced with temporal problems, temporal knowledge graphs can serve as a knowledge base to help locate potential answers, the task says It is Temporal Knowledge Graph Question Answering (TKGQA). Temporal knowledge graph question answering divide...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/33G06F16/332G06F16/36G06F40/30G06N5/04
CPCG06F16/3344G06F16/3329G06F16/367G06F40/30G06N5/04
Inventor 陈子阳胡升泽王军波赵翔徐浩谭真黄宏斌
Owner NAT UNIV OF DEFENSE TECH
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