Network Representation Learning: A Survey

With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.Comment: Accepted by IEEE transactions on Big Data; 25 pages, 10 tables, 6 figures and 127 reference

Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data

Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D

Abstracts: HASTAC 2017: The Possible Worlds of Digital Humanities

The document contains abstracts for HASTAC 2017

How Visualization Supports the Daily Work in Traditional Humanities on the Example of Visual Analysis Case Studies

Attempts to convince humanities scholars of digital approaches are met with resistance, often. The so-called Digitization Anxiety is the phenomenon that describes the fear of many traditional scientists of being replaced by digital processes. This hinders not only the progress of the scientific domains themselves – since a lot of digital potential is missing – but also makes the everyday work of researchers unnecessarily difficult. Over the past eight years, we have made various attempts to walk the tightrope between 'How can we help traditional humanities to exploit their digital potential?' and 'How can we make them understand that their expertise is not replaced by digital means, but complemented?' We will present our successful interdisciplinary collaborations: How they came about, how they developed, and the problems we encountered. In the first step, we will look at the theoretical basics, which paint a comprehensive picture of the digital humanities and introduces us to the topic of visualization. The field of visualization has shown a special ability: It manages to walk the tightrope and thus keeps digitization anxiety at bay, while not only making it easier for scholars to access their data, but also enabling entirely new research questions. After an introduction to our interdisciplinary collaborations with the Musical Instrument Museum of Leipzig University, as well as with the Bergen-Belsen Memorial, we will present a series of user scenarios that we have collected in the course of 13 publications. These show our cooperation partners solving different research tasks, which we classify using Brehmer and Munzner’s Task Classification. In this way, we show that we provide researchers with a wide range of opportunities: They can answer their traditional research questions – and in some cases verify long-standing hypotheses about the data for the first time – but also develop their own interest in previously impossible, new research questions and approaches. Finally, we conclude our insights on individual collaborative ideas with perspectives on our newest projects. These have risen from the growing interest of collaborators in the methods we deliver. For example, we get insights into the music of real virtuosos of the 20th century. The necessary music storage media can be heard for the first time through digital tools without risking damage to the old material. In addition, we can provide computer-aided analysis capabilities that help musicologists in their work. In the course of the visualization project at the Bergen-Belsen memorial, we will see that what was once a small diary project has grown into a multimodal and international project with institutions of culture and science from eight countries. This is dedicated not only to the question of preserving cultural objects from Nazi persecution contexts but also to modern ways of disseminating and processing knowledge around this context. Finally, we will compile our experience and accumulated knowledge in the form of problems and challenges at the border between computer science and traditional humanities. These will serve as preparation and assistance for future and current interested parties of such interdisciplinary collaborative project

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies