This paper explores the interoperability between Description Logics (DL) and Logic Programs (LP) in the context of the Semantic Web. The authors define a new intermediate knowledge representation (KR) called Description Logic Programs (DLP) and Description Horn Logic (DHL), which lies within the expressive intersection of DL and LP. DLP is designed to combine the strengths of both DL and LP, allowing for the fusion of rules and ontologies. The paper discusses the mapping between DL and DHL, and between DLP and DHL, enabling bidirectional translation of premises and inferences. This fusion technique enables the use of rule-based systems on top of ontologies and vice versa, facilitating efficient reasoning over large-scale DL ontologies using LP inferencing algorithms. The paper also provides a detailed analysis of the expressive power of DLP and DHL, and discusses the computational complexity of the translation algorithms. Finally, it explores how DL and LP inferencing can be reduced to each other using the defined mappings, making it possible to perform DL reasoning using LP engines and vice versa.This paper explores the interoperability between Description Logics (DL) and Logic Programs (LP) in the context of the Semantic Web. The authors define a new intermediate knowledge representation (KR) called Description Logic Programs (DLP) and Description Horn Logic (DHL), which lies within the expressive intersection of DL and LP. DLP is designed to combine the strengths of both DL and LP, allowing for the fusion of rules and ontologies. The paper discusses the mapping between DL and DHL, and between DLP and DHL, enabling bidirectional translation of premises and inferences. This fusion technique enables the use of rule-based systems on top of ontologies and vice versa, facilitating efficient reasoning over large-scale DL ontologies using LP inferencing algorithms. The paper also provides a detailed analysis of the expressive power of DLP and DHL, and discusses the computational complexity of the translation algorithms. Finally, it explores how DL and LP inferencing can be reduced to each other using the defined mappings, making it possible to perform DL reasoning using LP engines and vice versa.