Large　language　models　(LLMs),　such　as　ChatGPT　and　GPT4,　are　making　new　waves　in　the　field　of　natural　language　processing　and　artificial　intelligence,　due　to　their　emergent　ability　and　generalizability.　However,　LLMs　are　black-box　models,　which　often　fall　short　of　capturing　and　accessing　factual　knowledge.　In　contrast,　Knowledge　Graphs　(KGs),　Wikipedia　and　Huapu　for　example,　are　structured　knowledge　models　that　explicitly　store　rich　factual　knowledge.　KGs　can　enhance　LLMs　by　providing　external　knowledge　for　inference　and　interpretability.　Meanwhile,　KGs　are　difficult　to　construct　and　evolve　by　nature,　which　challenges　the　existing　methods　in　KGs　to　generate　new　facts　and　represent　unseen　knowledge.　Therefore,　it　is　complementary　to　unify　LLMs　and　KGs　together　and　simultaneously　leverage　their　advantages.　In　this　article,　we　present　a　forward-looking　roadmap　for　the　unification　of　LLMs　and　KGs.　Our　roadmap　consists　of　three　general　frameworks,　namely,　1)　KG-enhanced　LLMs,　which　incorporate　KGs　during　the　pre-training　and　inference　phases　of　LLMs,　or　for　the　purpose　of　enhancing　understanding　of　the　knowledge　learned　by　LLMs;　2)　LLM-augmented　KGs,　that　leverage　LLMs　for　different　KG　tasks　such　as　embedding,　completion,　construction,　graph-to-text　generation,　and　question　answering;　and　3)　Synergized　LLMs　+　KGs,　in　which　LLMs　and　KGs　play　equal　roles　and　work　in　a　mutually　beneficial　way　to　enhance　both　LLMs　and　KGs　for　bidirectional　reasoning　driven　by　both　data　and　knowledge.　We　review　and　summarize　existing　efforts　within　these　three　frameworks　in　our　roadmap　and　pinpoint　their　future　research　directions.　IEEE