Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.

Harnessing linked knowledge sources for topic classification in social media

Topic classification (TC) of short text messages offers an effective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolution). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the topics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detection (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets. Copyright 2013 ACM.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

The integration of multi-source data to improve the classification of remotely sensed images

The number of remote sensing platforms and sensors rises almost every year, yet much work on the interpretation of land cover is still carried out using either single images or images from the same source taken at different dates. Two questions could be asked of this proliferation of images: can the information contained in different scenes be used to improve the classification accuracy and, what is the best way to combine the different imagery? Two of these multiple image sources are MODIS on the Terra platform and ETM+ on board Landsat7, which are suitably complementary. Daily MODIS images with 36 spectral bands in 250-1000 m spatial resolution and seven spectral bands of ETM+ with 30m and 16 days spatial and temporal resolution respectively are available. In the UK, cloud cover may mean that only a few ETM+ scenes may be available for any particular year and these may not be at the time of year of most interest. The MODIS data may provide information on land cover over the growing season, such as harvest dates, that is not present in the ETM+ data. Therefore, the primary objective of this work is to develop a methodology for the integration of medium spatial resolution Landsat ETM+ image, with multi-temporal, multi-spectral, low-resolution MODIS \Terra images, with the aim of improving the classification of agricultural land. Additionally other data may also be incorporated such as field boundaries from existing maps. When classifying agricultural land cover of the type seen in the UK, where crops are largely sown in homogenous fields with clear and often mapped boundaries, the classification is greatly improved using the mapped polygons and utilising the classification of the polygon as a whole as an apriori probability in classifying each individual pixel using a Bayesian approach. When dealing with multiple images from different platforms and dates it is highly unlikely that the pixels will be exactly co-registered and these pixels will contain a mixture of different real world land covers. Similarly the different atmospheric conditions prevailing during the different days will mean that the same emission from the ground will give rise to different sensor reception. Therefore, a method is presented with a model of the instantaneous field of view and atmospheric effects to enable different remote sensed data sources to be integrated.

Leadership through intellect:the meaning of leadership in the intellectual history of nineteenth and early twentieth century Germany

This study examines the understanding of leadership in Germany, as it developed throughout the nineteenth and early twentieth century. The investigation is based on the work of contemporary writers and thinkers, as well as on the leadership styles of key political figures. Given the ideological connotations of the term "Führung" in post-war Germany, the aim is to reconsider the meaning of leadership, with particular reference to the alternative notion of spiritual guidance. The rise to power of Napoleon I fundamentally influenced the understanding of leadership in Germany, as is demonstrated through an analysis of the Napoleonic reception in contemporary literature. Despite polarised responses, the formation of the heroic ideal may be identified, the quest for spiritual guidance having become subordinate to the charismatic legitimisation of political authority. As advocated by Thomas Carlyle, the mid to late nineteenth century witnessed the realisation of this ideal through Bismarck. The intellectual response to this development is characterised by the work of Wagner, Burckhardt and Nietzsche. In different ways each figure emphasised the need to redefine greatness and to seek spiritual guidance from alternative sources. The reflection on leadership in the early twentieth century is traced through the work of Harry Graf Kessler and the circles around Stefan George. Hitherto unpublished material is examined, revealing both the influences of nineteenth century thought and reactions to the "persönliches Regiment" of Wilhelm II. The intellectual debate culminates in Max Kommerell's 1928 study Der Dichter als Führer. Read in conjunction with unpublished notes and correspondence, this provides new insights into Kommerell's thought. The concept of poetic leadership constitutes a potential spiritual and intellectual alternative to the ideal of the political "Führer" which dominated the forthcoming era. It therefore remains of contemporary significance and may contribute to a broader discussion of the leadership dilemma in modern Germany.

Research strategies for organizational history:a dialogue between historical theory and organization theory

If history matters for organization theory, then we need greater reflexivity regarding the epistemological problem of representing the past; otherwise, history might be seen as merely a repository of ready-made data. To facilitate this reflexivity, we set out three epistemological dualisms derived from historical theory to explain the relationship between history and organization theory: (1) in the dualism of explanation, historians are preoccupied with narrative construction, whereas organization theorists subordinate narrative to analysis; (2) in the dualism of evidence, historians use verifiable documentary sources, whereas organization theorists prefer constructed data; and (3) in the dualism of temporality, historians construct their own periodization, whereas organization theorists treat time as constant for chronology. These three dualisms underpin our explication of four alternative research strategies for organizational history: corporate history, consisting of a holistic, objectivist narrative of a corporate entity; analytically structured history, narrating theoretically conceptualized structures and events; serial history, using replicable techniques to analyze repeatable facts; and ethnographic history, reading documentary sources "against the grain." Ultimately, we argue that our epistemological dualisms will enable organization theorists to justify their theoretical stance in relation to a range of strategies in organizational history, including narratives constructed from documentary sources found in organizational archives. Copyright of the Academy of Management, all rights reserved.