Linking in Context

This paper explores the idea of dynamically adding multi-destination links to Web pages, based on the context of the pages and users, as a way of assisting Web users in their information finding and navigation activities. The work does not make any preconceived assumptions about the information needs of its users. Instead it presents a method for generating links by adapting to the information needs of a community of users and for utilizing these in assisting users within this community based on their individual needs. The implementation of this work is carried out within a multi-agent framework where concepts from open hypermedia are extended and exploited. In this paper, the entities involved in the process of generating and using ?context links? as well as the techniques they employ to achieve their tasks, are described. The result of an experiment carried out to investigate the implications of linking in context on information finding, is also provided

Context and linking in retrieval from personal digital archives

Advances in digital capture and storage technologies mean that it is now possible to capture and store one’s entire life experiences in personal digital archives. These vast personal archives (or Human Digital Memories (HDMs)) pose new challenges and opportunities for the research community, not the least of which is developing effective means of retrieval from HDMs. Personal archive retrieval research is still in its infancy and there is much scope for novel research. My PhD proposes to develop effective HDM retrieval algorithms by combining rich sources of context associated with items, such as location and people present data, with information obtained by linking HDM items in novel ways

Towards Deep Semantic Analysis Of Hashtags

Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden), the segmentation of hashtags into constituent entities ("NSA" and "Edward Snowden" in this case) is not a trivial task. Most of the current state-of-the-art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. In this paper, we present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags such that the coherence between hashtag semantics and the tweet is maximized. To the best of our knowledge, no existing study addresses the issue of linking entities in hashtags for extracting semantic information. We evaluate our method on two different datasets, and demonstrate the effectiveness of our technique in improving the overall entity linking in tweets via additional semantic information provided by segmenting and linking entities in a hashtag.Comment: To Appear in 37th European Conference on Information Retrieva

Link Invariants for Flows in Higher Dimensions

Linking numbers in higher dimensions and their generalization including gauge fields are studied in the context of BF theories. The linking numbers associated to $n$-manifolds with smooth flows generated by divergence-free p-vector fields, endowed with an invariant flow measure are computed in different cases. They constitute invariants of smooth dynamical systems (for non-singular flows) and generalizes previous results for the 3-dimensional case. In particular, they generalizes to higher dimensions the Arnold's asymptotic Hopf invariant for the three-dimensional case. This invariant is generalized by a twisting with a non-abelian gauge connection. The computation of the asymptotic Jones-Witten invariants for flows is naturally extended to dimension n=2p+1. Finally we give a possible interpretation and implementation of these issues in the context of string theory.Comment: 21+1 pages, LaTeX, no figure