Goal　modeling　plays　an　imperative　role　in　early　requirements　engineering,　which　has　been　investigated　for　decades.　There　have　been　many　studies　that　show　the　usefulness　of　requirements　goal　models.　However,　the　establishment　of　goal　models　is　typically　done　manually,　which　is　time-consuming　and　has　a　steep　learning　curve.　In　this　paper,　we　propose　a　semi-automatic　framework　for　constructing　iStar　models,　which　is　a　well-known　goal　modeling　language.　Specifically,　we　first　investigate　the　practical　needs　of　iStar　modelers　on　the　automation　of　iStar　modeling　by　holding　interviews,　based　on　which　we　propose　an　interactive　and　iterative　modeling　process.　Our　proposal　takes　advantage　of　human　decisions　and　artificial　intelligence　algorithms,　respectively,　aiming　at　achieving　low　modeling　costs　while　maintaining　the　quality　of　models.　We　then　propose　a　hybrid　approach　for　automatically　extracting　goal　model　snippets　from　requirements　text,　which　implements　the　automatic　tasks　of　our　proposed　process.　The　proposed　method　combines　logical　reasoning　with　deep　learning　techniques　so　as　to　unleash　the　power　of　domain　knowledge　to　assist　with　automation　tasks.　We　have　performed　a　series　of　experiments　for　evaluation.　The　experimental　results　show　that　our　method　achieves　the　F1-measure　of　90.34%　for　actor　entity　extraction,　93.14%　for　intention　entity　extraction,　and　83.18%　for　actor　relation　extraction,　which　can　efficiently　establish　high-quality　goal　models.　The　artifacts　are　available　at　Zenodo1.　©　2022　Owner/Author.