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Exploiting Semantic Web Knowledge Graphs in Data Mining
Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation st...
Machine Learning and Knowledge Discovery in Databases
Annotation. This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2010, held in Barcelona, Spain, in September 2010. The 120 revised full papers presented in three volumes, together with 12 demos (out of 24 submitted demos), were carefully reviewed and selected from 658 paper submissions. In addition, 7 ML and 7 DM papers were distinguished by the program chairs on the basis of their exceptional scientific quality and high impact on the field. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. A topic widely explored from both ML and DM perspectives was graphs, with motivations ranging from molecular chemistry to social networks.
Information Processing and Management of Uncertainty in Knowledge-Based Systems
This two-volume set (CCIS 1601-1602) constitutes the proceedings of the 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2021, held in Milan, Italy, in July 2022. The 124 papers were carefully reviewed and selected from 188 submissions. The papers are organized in topical sections as follows: aggregation theory beyond the unit interval; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy mathematical analysis and its applications; generalized sets and operators; information fusion techniques based on aggregation functions, pre-aggregation functions, and their generalizations; interval uncertainty; k...
Mediatizing the Nation, Ordering the World
This book offers a timely and engaging account of how technologies of communication media impact nationalist challenges to global order, shedding new light on how they matter, how they have changed, and how their evolution transforms the conditions of possibility for nationalist order challengers. In the 21st century, we have become accustomed to close entanglements between resurgent nationalism and digital media. In Mediatizing the Nation, Ordering the World, Andrew Dougall shows that the relationship between media and nationalist order contestation is far older. Comparing Trump's breakthrough in the 21st century United States with a similar - but unsuccessful - movement in 19th century Bri...
Conspiracy Theory Discourses
Conspiracy Theory Discourses addresses a crucial phenomenon in the current political and communicative context: conspiracy theories. The social impact of conspiracy theories is wide-ranging and their influence on the political life of many nations is increasing. Conspiracy Theory Discourses bridges an important gap by bringing discourse-based insights to existing knowledge about conspiracy theories, which has so far developed in research areas other than Linguistics and Discourse Studies. The chapters in this volume call attention to conspiracist discourses as deeply ingrained ways to interpret reality and construct social identities. They are based on multiple, partly overlapping analytical frameworks, including Critical Discourse Analysis, rhetoric, metaphor studies, multimodality, and corpus-based, quali-quantitative approaches. These approaches are an entry point to further explore the environments which enable the proliferation of conspiracy theories, and the paramount role of discourse in furthering conspiracist interpretations of reality.
Discovery Science
This book constitutes the proceedings of the 21st International Conference on Discovery Science, DS 2018, held in Limassol, Cyprus, in October 2018, co-located with the International Symposium on Methodologies for Intelligent Systems, ISMIS 2018. The 30 full papers presented together with 5 abstracts of invited talks in this volume were carefully reviewed and selected from 71 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Classification; meta-learning; reinforcement learning; streams and time series; subgroup and subgraph discovery; text mining; and applications.
Digital Dominance
Across the globe, Google, Amazon, Facebook, Apple and Microsoft have accumulated power in ways that existing regulatory and intellectual frameworks struggle to comprehend. A consensus is emerging that the power of these new digital monopolies is unprecedented, and that it has important implications for journalism, politics, and society. It is increasingly clear that democratic societies require new legal and conceptual tools if they are to adequately understand, and if necessary check the economic might of these companies. Equally, that we need to better comprehend the ability of such firms to control personal data and to shape the flow of news, information, and public opinion. In this volum...
Discovery Science
This book constitutes the refereed proceedings of the 14th International Conference on Discovery Science, DS 2011, held in Espoo, Finland, in October 2011 - co-located with ALT 2011, the 22nd International Conference on Algorithmic Learning Theory. The 24 revised full papers presented together with 5 invited lectures were carefully revised and selected from 56 submissions. The papers cover a wide range including the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their application to knowledge discovery.
