Structured data clouding across multiple webs

TL;DR: This paper proposes matching and clustering techniques specifically conceived for linked data classification, by focusing on the high level of heterogeneity of data descriptions in terms of the number and kind of their descriptive features.

TL;DR: This paper proposes aggregation and abstraction techniques for thematic clustering and exploration of linked data that transform a basic, flat view of a potentially large set of messy linked data for a given search target, into a high-level, thematic view called inCloud.

TL;DR: A social recommender system that follows user’s preferences to provide recommendation based on the similarity among users participating in the social network is proposed, based on integration of major characteristics of content-based and collaborative filtering techniques.

TL;DR: This work examines necessary mechanisms and means to support the proposed framework seamlessly working with existing infrastructures, and a prototype integration platform is built and comprehensive simulations are implemented to test and prove its high feasibility.

TL;DR: In this article, the authors present a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective, which provides an up-to-date student oriented treatment of the subject.

TL;DR: This work proposes and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages” that join them together in the link structure, and has connections to the eigenvectors of certain matrices associated with the link graph.

TL;DR: This paper proposes generalizations that combine tie strength and node centrality, and illustrates the benefits of this approach by applying one of them to Freeman’s EIES dataset.