The common critical barrier of the information integration techniques is the quality of the relationship sets that indicates the correlation among different concepts (or vocabularies) and their hierarchical relationships. Such data sets are needed and achieved by manual designs (via Entity-Relationship Model or UML model) in the traditional databases or data warehouses. Thus, the automatic construction of the relationship data for all web pages is demanded for web information integration. The objective of this project is to automatically construct the relationship data (using the Ontology model). Prof. Chen and her team propose a framework for automatically extracting various semantic relationships from heterogeneous unstructured Web data, such as query logs and Twitter. Based on the most common characteristics of these data, the embedded concepts are detected and associated based on semantic meanings. The experimental results demonstrate that our framework is able to extract satisfactory semantic relationships.