Maintaining　traceability　links　of　software　systems　is　a　crucial　task　for　software　management　and　development.　Unfortunately,　dealing　with　traceability　links　are　typically　taken　as　afterthought　due　to　time　pressure.　Some　studies　attempt　to　use　information　retrieval-based　methods　to　automate　this　task,　but　they　only　concentrate　on　calculating　the　textual　similarity　between　various　software　artifacts　and　do　not　take　into　account　the　properties　of　such　artifacts.　In　this　paper,　we　propose　a　novel　traceability　link　recovery　approach,　which　comprehensively　measures　the　similarity　between　use　cases　and　source　code　by　exploring　their　particular　properties.　To　this　end,　we　leverage　and　combine　machine　learning　and　logical　reasoning　techniques.　On　the　one　hand,　our　method　extracts　features　by　considering　the　semantics　of　the　use　cases　and　source　code,　and　uses　a　classification　algorithm　to　train　the　classifier.　On　the　other　hand,　we　utilize　the　relationships　between　artifacts　and　define　a　series　of　rules　to　recover　traceability　links.　In　particular,　we　not　only　leverage　source　code＇s　structural　information,　but　also　take　into　account　the　interrelationships　between　use　cases.　We　have　conducted　a　series　of　experiments　on　multiple　datasets　to　evaluate　our　approach　against　existing　approaches,　the　results　of　which　show　that　our　approach　is　substantially　better　than　other　methods.