Conor Hayes

Unsupervised graph-based topic labelling using dbpedia

Automated topic labelling brings benefits for users aiming at analysing and understanding document collections, as well as for search engines targetting at the linkage between groups of words and their inherent topics. Current approaches to achieve this suffer in quality, but we argue their performances might be improved by setting the focus on the structure in the data. Building upon research for concept disambiguation and linking to DBpedia, we are taking a novel approach to topic labelling by making use of structured data exposed by DBpedia. We start from the hypothesis that words co-occuring in text likely refer to concepts that belong closely together in the DBpedia graph. Using graph centrality measures, we show that we are able to identify the concepts that best represent the topics. We comparatively evaluate our graph-based approach and the standard text-based approach, on topics extracted from three corpora, based on results gathered in a crowd-sourcing experiment. Our research shows that graph-based analysis of DBpedia can achieve better results for topic labelling in terms of both precision and topic coverage.

Page-reRank: Using Trusted Links to Re-Rank Authority

Search engines like Google.com use the link structure of the Web to determine whether Web pages are authoritative sources of information. However, the linking mechanism provided by HTML does not allow the Web author to express different types of links, such as positive or negative endorsements of page content. As a consequence, search engine algorithms cannot discriminate between sites that are highly linked and sites that are highly trusted. We demonstrate our claim by running PageRank on a real world data set containing positive and negative links. We conclude that simple semantic extensions to the link mechanism would provide a richer semantic network from which to mine more precise Web intelligence.

The effect of user features on churn in social networks

Social sites and services rely on the continuing activity, good will and behaviour of the contributors to remain viable. There has been little empirical study of the mechanisms by which social sites maintain a viable user base. Such studies would provide a scientific understanding of the patterns that lead to user churn (i.e. users leaving the community) and the community dynamics that are associated with reduction of community members -- primary threats to the sustainability of any service. In this paper, we explore the relation between a users value within a community - constituted from various user features - and the probability of a user churning.

Smart Radio - Building Music Radio On the Fly

This paper describes the development of a networked music application at Trinity College Dublin. Smart Radio is a web based client-server application which uses streaming audio technology and collaborative recommendation techniques to allow users build, manage and share music programmes. While it is generally acknowledged that music distribution over the web will dramatically change how the music industry operates, there are few prototypes available to demonstrate how this could work in an managed way. The Smart Radio approach is to have people manage their music resources by putting together personalised music programmes. These programmes can then be swapped using techniques of collaborative recommendation to find similarities between users. The smart radio system currently runs within the Computer Science Intranet with permission from the Irish Music Rights Organisation (IMRO). It is a prototype system for an “always on” high bandwidth Internet connection such as ADSL.

Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity Disambiguation

Semantic relatedness and disambiguation are fundamental problems for linking text documents to the Web of Data. There are many approaches dealing with both problems but most of them rely on word or concept distribution over Wikipedia. They are therefore not applicable to concepts that do not have a rich textual description. In this paper, we show that semantic relatedness can also be accurately computed by analysing only the graph structure of the knowledge base. In addition, we propose a joint approach to entity and word-sense disambiguation that makes use of graph-based relatedness. As opposed to the majority of state-of-the-art systems that target mainly named entities, we use our approach to disambiguate both entities and common nouns. In our experiments, we first validate our relatedness measure on multiple knowledge bases and ground truth datasets and show that it performs better than related state-of-the-art graph based measures. Afterwards, we evaluate the disambiguation algorithm and show that it also achieves superior disambiguation accuracy with respect to alternative state-of-the-art graph-based algorithms.

Real time analysis of sensor data for the Internet of Things by means of clustering and event processing

Sensor technology and sensor networks have evolved so rapidly that they are now considered a core driver of the Internet of Things (IoT), however data analytics on IoT streams is still in its infancy. This paper introduces an approach to sensor data analytics by using the OpenIoT1 middleware; real time event processing and clustering algorithms have been used for this purpose. The OpenIoT platform has been extended to support stream processing and thus we demonstrate its flexibility in enabling real time on-demand application domain analytics. We use mobile crowd-sensed data, provided in real time from wearable sensors, to analyse and infer air quality conditions. This experimental evaluation has been implemented using the design principles and methods for IoT data interoperability specified by the OpenIoT project. We describe an event and clustering analytics server that acts as an interface for novel analytical IoT services. The approach presented in this paper also demonstrates how sensor data acquired from mobile devices can be integrated within IoT platforms to enable analytics on data streams. It can be regarded as a valuable tool to understand complex phenomena, e.g., air pollution dynamics and its impact on human health.

Churn in Social Networks: A Discussion Boards Case Study

Churn has been identified as an important issue in a wide range of industries. In social networks, churn represents a significant risk for the health and functioning of communities. However, the importance and actual meaning of churn in social networks is almost unexplored. This work provides a general view on these issues and discusses aspects that are especially relevant to discussion boards. We provide a broad literature review on “traditional” churn analysis and prediction and highlight the specialities of churn in social networks. We further present an empirical analysis of a churn definition particularly appropriate for discussion boards and propose future research directions for predicting churn in social networks, focusing on the importance of social roles, influence and influence diffusion.

Churn in Social Networks

In the past, churn has been identified as an issue across most industry sectors. In its most general sense it refers to the rate of loss of customers from a company’s customer base. There is a simple reason for the attention churn attracts: churning customers mean a loss of revenue. Emerging from business spaces like telecommunications (telcom) and broadcast providers, where churn is a major issue, it is also regarded as a crucial problem in many other businesses, such as online games creators, but also online social networks and discussion sites. Companies aim at identifying the risk of churn in its early stages, as it is usually much cheaper to retain a customer than to try to win him or her back. If this risk can be accurately predicted, marketing departments can target customers efficiently with tailored incentives to prevent them from leaving.

Extracting and Utilizing Social Networks from Log Files of Shared Workspaces

Log files of online shared workspaces contain rich information that can be further analyzed. In this paper, log-file information is used to extract object-centric and user-centric social networks. The object-centric social networks are used as a means for assigning concept-based expertise elements to users based on the documents that they created, revised or read. The user-centric social networks are derived from users working on common documents. Weights, called the Cooperation Index, are assigned to links between users in a user-centric social network, which indicates how closely two people have collaborated together, based on their history. We also present a set of tools that was developed to realize our approach.

Navigating and Annotating Semantically-Enabled Networks of People and Associated Objects

Social spaces such as blogs, wikis and online social networking sites are enabling the formation of online communities where people are linked to each other through direct profile connections and also through the content items that they are creating, sharing and tagging. As these spaces become bigger and more distributed, more intuitive ways of navigating the associated information become necessary. The Semantic Web aims to link identifiable objects to each other and to textual strings via relationships and attributes respectively, and provides a platform for gathering diverse information from heterogeneous sources and performing operations on such linked data. In this paper, we will demonstrate how this linked semantic data can provide an enhanced view of the activity in a social network, and how the Galaxy tool described in this work can augment objects from social spaces, by highlighting related people and objects, and suggesting relevant sources of knowledge.