We consider the problem of automatically extracting general lists from the web. Existing approaches are mostly dependent upon either the underlying HTML markup or the visual structure of the Web page. We present HyLiEn an unsupervised, Hybrid approach for automatic List discovery and Extraction on the Web. It employs general assumptions about the visual rendering of lists, and the structural representation of items contained in them. We show that our method significantly outperforms existing methods.

WINACS (Web-based Information Network Analysis for Computer Science) is a project that incorporates many recent, exciting developments in data sciences to construct a Web-based computer science information network and to discover, retrieve, rank, cluster, and analyze such an information network. With the rapid development of the Web, huge amounts of information are available in the form of Web documents, structures, and links. It has been a dream of the database and Web communities to harvest such information and reconcile the unstructured nature of the Web with the neat, semi-structured schemas of the database paradigm. Taking computer science as a dedicated domain, WINACS first discovers related Web entity structures, and then constructs a heterogeneous computer science information network in order to rank, cluster and analyze this network and support intelligent and analytical queries.