Latent community models are successful at statistically modeling network data by assigning network entities to communities and modelling entity relations as the relations of their communities. In this paper, we describe the limitation of these models in inferring relations between two communities when the entity relations between these communities are unobserved. We propose a solution to this problem by factorizing the community relations matrix into two community feature matrices, thereby adding a dependency between community relations. We introduce the deep dynamic mixed membership stochastic blockmodel based network (DDBN) to demonstrate the feasibility of such an approach. Our model marries the mixed membership stochastic blockmodel (MMSB) with deep neural networks for rich feature extraction and introduces a temporal dependency in latent features using a long short-term memory unit for dynamic network modeling. We evaluate our model on the link prediction task in static and dynamic networks and find that our model achieves comparable results with state-of-the-art methods.

Introduction of graph-based data representation formats, that resulted in Knowledge Graphs and Linked Open Data, enables new ways of processing and analyzing relations between individual pieces of data. One of the most important features of such representation is its ability to represent data semantics. We state that an important step towards obtaining a full utilization of graph-based semantics is to create a formal process of extracting underlying structures of data from Knowledge Graphs and Linked Open Data, as well as building data models. The paper proposes a methodology, based on category theory, for representing graph-based data as a topos category. Construction of topos give us the ability to identify two types of features: ones that are involved in definitions of other concepts; and ones that show how other concepts are involved in a definition of a given concept. Topos and structures of features allow for reasoning about concepts and their interrelations. Further, mechanisms of category theory enable to synthesize new concepts. A simple example is included.

Linked Open Data (LOD) consists of numerous data stores that are highly interconnected. LOD stores use Resource Description Framework (RDF) as a data representation format. A graph-based nature of RDF brings an opportunity to develop new approaches for accumulating data from multiple sources characterized by different levels of confidence in them. Recently, a participatory learning mechanism has been extended to cope with RDF. It is an attractive way of integrating new pieces of information with already known ones. Further, it has been recognized that pieces of information describing entities can have a disjunctive or conjunctive form. This paper uses an RDF-based participatory learning process to aggregate information obtained from multiple data stores. This process provides mechanisms that determine overall certainty in combined data based on levels of confidence in already known pieces of information and new ones. The behavior of such a process used for integrating information equipped with different levels of uncertainty is presented, and a simple case study is included.

The amount of information stored on the Internet is constantly growing, users become more involved in Web activities, and the Web utilization is on the rise.The Internet becomes a place for variety of actions and events related to work, education, and entertainment.The users become aware of the enormous amount of data and information available to them, and in the case of tools supporting utilization of such repository their needs and expectations are growing.Search engines and recommender systems-although helpful-provide limited assistance.The users implore for more-they are looking for tools that filter out irrelevant information, search through nonstructural data, and provide suitable answers to their questions, all in a personalized way and according to their needs.Furthermore, users want to interact with the Web in a more natural way.The soft computing technologies developed around Zadeh's concept of fuzzy sets provide a framework suitable for processing and presenting data in a more human-like way.A significant amount of research has been conducted in the applications of soft computing to analysis and processing of data, modeling, decision making, and knowledge extraction.Maturity of these methods and the growing importance of the Web create a setting suitable for applying soft computing technologies to develop methods and techniques addressing the users' needs and concerns mentioned above.This special issue gives the readers a glimpse how soft computing techniques can be applied to processing data in the environment of the Web.All contributions are extended versions of the papers presented during the First World Conference on Soft Computing, May 23-26, 2011, San Francisco, CA.The paper entitled "A Fuzzy Ontology for Database Querying with Bipolar Preferences" by Nouredine Tamani, Ludovic Lietard, and Daniel Rocacher focuses on obtaining relevant information from data stored in distributed and heterogeneous databases.Queries to the databases result in responses containing relevant as well as irrelevant information.To separate the responses, so the user is provided only with the most relevant data, the paper introduces a semantic-based personalized data access method.The proposed method is governed by evaluating user preferences represented as fuzzy bipolar conditions, i.e. conditions containing both negative and positive declarations.The paper describes a new approach for querying complex information systems that combines a reasoning mechanism (an ontology based on

The Internet holds huge amount of documents available for users. Effective utilization of this enormous repository means a need for systems supporting users in a process of finding related documents. An ontology defined in the framework of the Semantic Web (Berners, 2001) allows for specification of concepts, their instances, and relationships existing between concepts. A hierarchy of concepts (Yager, 2000) is a graph-like structure providing a means for representing human-like dependencies. The article proposes an approach for utilization of a hierarchy of concepts to perform categorization of web pages in the Semantic Web. A user provides a hierarchy that can only partially “cover” their domain of interest. The hierarchy is treated as a “seed” representing user’s initial knowledge about the domain. Ontologies are treated as supplementary knowledge bases. They are used to instantiate the hierarchy with concrete information, as well as to enhance it with new concepts initially unknown to a user.

Software maintenance engineers need tools to support their work. To make such tools relevant, they should provide engineers with quantitative input, as well as the knowledge needed to understand factors influencing maintenance activities. This article proposes an approach leading to multitechnique knowledge extraction and development of a comprehensive meta-model prediction system in the area of corrective maintenance. It dwells on elements of evidence theory and a number of fuzzy-based models. The models are developed using an evolutionary-based approach with different objectives applied to different subsets of data. Evidence theory–based Transferable Belief Model and belief function values assigned to generated models are used for reasoning purposes. The study comprises a detailed case for estimating the number of defects in a medical imaging system. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1093–1115, 2005.