融合局部-全局历史模式与历史知识频率的时序知识图谱补全方法

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Time-series knowledge graph completion method combining local-global historica pattern and historical knowledge frequency

Jia Kai a，b† ， Wang Yangpinga，b， Yang Jingyu a，b ， Zhang Xiquan a，b （aSchoolofcoic&fioingolValilExpetalcgCtefilsit&Control，LanzhouJiaotongUniversity，Lanzhou73oo7O，China）

Abstract：TKGsare dynamicrepresentations of evolvingfacts，and theircompletion task involvespredicting futureunkown factsbasedonhistoricaldata.Thekeyliesinunderstanding historicaldata.However，existingmodelshavelimitationsincapturingthefeaturesofhistoricaleventsandcannotaccuratelyextractusefulinformationfromtimestamps.Fromtheperspective of historical evolution，consideringthesequence，frequency，andperiodicpaternsofhistorical factscomprehensivelysbeneficialfor predicting future facts.Therefore，thispaper proposeda temporal knowledgegraphcompletion algorithm（LGHHKF）thatintegratedlocal-globalhistoricalpaternsandhistoricalknowledgefrequency.Specificall，itfrstlyusedalocale current graph encoder network to modelthe intrinsicassciationsand dynamic evolutionof eventsatadjacent timestamps. Then，ituseda global historicalencodernetworktoconsiderallrelevantfactsatprevious timestampstoavoidlosingntitiesor relationsthatdidn'tappearatadjacenttimestamps.Next，itlearedthefrequencyscoresof thesefactsthroughahistorical knowledge frequencylearning module toenrich the model’sprediction basis.Finally，afterbalancing between thetwo encoders，itusedaperiodicdecodertoperforminferenceandcompletion.Thepaperusedfourbenchmark datasets to evaluate theproposedmethod，andtheexperimentalresultsprovethatLGH-HKFishighlycompetitivecomparedtoothercurrentmodels in most cases.

Key words：temporal knowledge graph；completionalgorithm;local cyclic graph encoder；global history encoder；frequency of historical knowledge

0 引言

知识图谱补全技术对现在热门的辅助搜索、推荐系统、问答系统等领域都有重大的意义，只有当知识足够完善，下游任务的准确率才能得到进一步的提升[1]。（剩余17658字）