http://hdl.handle.net/10379/383 2017-10-29T22:44:08Z http://hdl.handle.net/10379/6560 Enabling case-based reasoning on the web of data Heitmann, Benjamin; Hayes, Conor While Case-based reasoning (CBR) has successfully been deployed on the Web, its data models are typically inconsistent with existing information infrastructure and standards. In this paper, we examine how CBR can operate on the emerging Web of Data, with mutual benefits. The expense of knowledge engineering and curating a case base can be reduced by using Linked Data from the Web of Data. While Linked Data provides experiential data from many different domains, it also contains inconsistencies, missing data and noise which provide challenges for logic-based reasoning. CBR is well suited to provide alternative and robust reasoning approaches. We introduce (i) a lightweight CBR vocabulary which is suited for the open ecosystem of the emerging Web of Data, and provide (ii) a detailed example of a case base using data from multiple sources. We propose that for the first time the Web of Data provides data and a real context for open CBR systems. 2010-07-20T00:00:00Z http://hdl.handle.net/10379/5862 Semantic representation and enrichment of information retrieval experimental data Bordea, Georgeta; Buitelaar, Paul Experimental evaluation carried out in international large-scale campaigns is a fundamental pillar of the scientific and technological advancement of information retrieval (IR) systems. Such evaluation activities produce a large quantity of scientific and experimental data, which are the foundation for all the subsequent scientific production and development of new systems. In this work, we discuss how to semantically annotate and interlink this data, with the goal of enhancing their interpretation, sharing, and reuse. We discuss the underlying evaluation workflow and propose a resource description framework model for those workflow parts. We use expertise retrieval as a case study to demonstrate the benefits of our semantic representation approach. We employ this model as a means for exposing experimental data as linked open data (LOD) on the Web and as a basis for enriching and automatically connecting this data with expertise topics and expert profiles. In this context, a topic-centric approach for expert search is proposed, addressing the extraction of expertise topics, their semantic grounding with the LOD cloud, and their connection to IR experimental data. Several methods for expert profiling and expert finding are analysed and evaluated. Our results show that it is possible to construct expert profiles starting from automatically extracted expertise topics and that topic-centric approaches outperform state-of-the-art language modelling approaches for expert finding. Journal article 2016-05-28T00:00:00Z http://hdl.handle.net/10379/5560 Targeting online communities to maximise information diffusion Belák, Václav; Lam, Samantha; Hayes, Conor In recent years, many companies have started to utilise online social communities as a means of communicating with and targeting their employees and customers. Such online communities include discussion fora which are driven by the conversational activity of users. For example, users may respond to certain ideas as a result of the influence of their neighbours in the underlying social network. We analyse such influence to target communities rather than individual actors because information is usually shared with the community and not just with individual users. In this paper, we study information diffusion across communities and argue that some communities are more suitable for maximising spread than others. In order to achieve this, we develop a set of novel measures for cross-community influence, and show that it outperforms other targeting strategies on 51 weeks of data of the largest Irish online discussion system, Boards.ie. 2012-04-01T00:00:00Z http://hdl.handle.net/10379/5392 Life-cycles and mutual effects of scientific communities Belák, Václav; Karnstedt, Marcel; Hayes, Conor Cross-community effects on the behaviour of individuals and communities themselves can be observed in a wide range of applications. While previous work has tried to explain and analyse such phenomena, there is still a great potential for increasing the quality and accuracy of this analysis. In this work, we propose a general framework consisting of several different techniques to analyse and explain cross-community effects and the underlying dynamics. The proposed methodology works with arbitrary community algorithms, incorporates meta-data to improve the overall quality and expressiveness of the analysis and identifies particular phenomena in an automated manner. We illustrate the benefits and strengths of our approach by exposing in-depth details of cross-community effects between two closely related and well established areas of scientific research. This work focuses on techniques for understanding, defining and eventually predicting typical life-cycles and events in the context of cross-community dynamics. 2011-01-01T00:00:00Z