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In recent years, Semantic web has become a topic of active research in several fields of computer science and has applied in a wide range of domains such as bioinformatics, life sciences, and knowledge management. The two fast-developing…

Data privacy and ownership are significant in social data science, raising legal and ethical concerns. Sharing and analyzing data is difficult when different parties own different parts of it. An approach to this challenge is to apply…

How do social networks differ across platforms? How do information networks change over time? Answering questions like these requires us to compare two or more graphs. This task is commonly treated as a measurement problem, but numerical…

This document considers the counteracting requirements of privacy and accountability applied to identity management. Based on the requirements of GDPR applied to identity attributes, two forms of identity, with differing balances between…

Decentralized Autonomous Organizations (DAOs) have seen exponential growth and interest due to their potential to redefine organizational structure and governance. Despite this, there is a discrepancy between the ideals of autonomy and…

Knowledge bases such as Wikidata amass vast amounts of named entity information, such as multilingual labels, which can be extremely useful for various multilingual and cross-lingual applications. However, such labels are not guaranteed to…

Large networks are useful in a wide range of applications. Sometimes problem instances are composed of billions of entities. Decomposing and analyzing these structures helps us gain new insights about our surroundings. Even if the final…

The collection and use of personal data are becoming more common in today's data-driven culture. While there are many advantages to this, including better decision-making and service delivery, it also poses significant ethical issues around…

The growing expanse of e-commerce and the widespread availability of online databases raise many fears regarding loss of privacy and many statistical challenges. Even with encryption and other nominal forms of protection for individual…

High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. These representations have different degrees of…

Due to the widespread use of data-powered systems in our everyday lives, concepts like bias and fairness gained significant attention among researchers and practitioners, in both industry and academia. Such issues typically emerge from the…

Knowledge graphs are an efficient method for representing and connecting information across various concepts, useful in reasoning, question answering, and knowledge base completion tasks. They organize data by linking points, enabling…

The large size and fast growth of data repositories, such as data lakes, has spurred the need for data discovery to help analysts find related data. The problem has become challenging as (i) a user typically does not know what datasets…

While the biomedical community has published several "open data" sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from…

In recent years, rather than enclosing data within a single organization, exchanging and combining data from different domains has become an emerging practice. Many studies have discussed the economic and utility value of data and data…

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific…

In modern ranking problems, different and disparate representations of the items to be ranked are often available. It is sensible, then, to try to combine these representations to improve ranking. Indeed, learning to rank via combining…

The amount of useful information available on the Web has been growing at a dramatic pace in recent years and people rely more and more on the Web to fulfill their information needs. In this paper, we study truthfulness of Deep Web data in…

Federated clouds raise a variety of challenges for managing identity, resource access, naming, connectivity, and object access control. This paper shows how to address these challenges in a comprehensive and uniform way using a data-centric…

Science mapping (SM), the study of the organization and development of science and technology, is a rapidly developing field within information science. The volume of available data allows this methodology to empirically address such issues…