BEOMAPS: Ad-hoc topic maps for enhanced search in social network data. by Peter Dolog, Martin Leginus, and ChengXiang Zhai.

From the webpage:

The aim of this project is to develop a novel system – a proof of concept that will enable more effective search, exploration, analysis and browsing of social media data. The main novelty of the system is an ad-hoc multi-dimensional topic map. The ad-hoc topic map can be generated and visualized according to multiple predefined dimensions e.g., recency, relevance, popularity or location based dimension. These dimensions will provide a better means for enhanced browsing, understanding and navigating to related relevant topics from underlying social media data. The ad-hoc aspect of the topic map allows user-guided exploration and browsing of the underlying social media topics space. It enables the user to explore and navigate the topic space through user-chosen dimensions and ad-hoc user-defined queries. Similarly, as in standard search engines, we consider the possibility of freely defined ad-hoc queries to generate a topic map as a possible paradigm for social media data exploration, navigation and browsing. An additional benefit of the novel system is an enhanced query expansion to allow users narrow their difficult queries with the terms suggested by an ad-hoc multi-dimensional topic map. Further, ad-hoc topic maps enable the exploration and analysis of relations between individual topics, which might lead to serendipitous discoveries.

This looks very cool and accords with some recent thinking I have been doing on waterfall versus agile authoring of topic maps.

The conference paper on this project is lodged behind a paywall at:

Beomap: Ad Hoc Topic Maps for Enhanced Exploration of Social Media Data, with this abstract:

Social media is ubiquitous. There is a need for intelligent retrieval interfaces that will enable a better understanding, exploration and browsing of social media data. A novel two dimensional ad hoc topic map is proposed (called Beomap). The main novelty of Beomap is that it allows a user to define an ad hoc semantic dimension with a keyword query when visualizing topics in text data. This not only helps to impose more meaningful spatial dimensions for visualization, but also allows users to steer browsing and exploration of the topic map through ad hoc defined queries. We developed a system to implement Beomap for exploring Twitter data, and evaluated the proposed Beomap in two ways, including an offline simulation and a user study. Results of both evaluation strategies show that the new Beomap interface is better than a standard interactive interface.

It has attracted 224 downloads as of today so I would say it is a popular chapter on topic maps.

I have contacted the authors in an attempt to locate a copy that isn’t behind a paywall.


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