融合局部-全局歷史模式與歷史知識頻率的時序知識圖譜補全方法

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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

知識圖譜補全技術對現(xiàn)在熱門的輔助搜索、推薦系統(tǒng)、問答系統(tǒng)等領域都有重大的意義，只有當知識足夠完善，下游任務的準確率才能得到進一步的提升[1]。（剩余17658字）