SIKS concentrates on seven research areas (“foci”) in the field of Information and Knowledge Systems.

The current SIKS foci are:

1. Knowledge Representation and Reasoning (focus director: Prof. Dr. F. Van Harmelen, VUA)
2. Computational Intelligence (focus director: Prof. Dr. E.O. Postma, UvT)
3. Agent technology (focus director: Prof. Dr. J.-J. Ch. Meyer, UU)
4. Computational Linguistics (focus director: Prof. dr. A, van den Bosch, Meertens Instituut, UVA)
5. Web-based information systems (focus director: Prof. Dr. A. Bozzon, TUD)
6. Datamanagement, storage and retrieval (focus director: Dr. D. Hiemstra, UT)
7. Human computer interaction (focus director: Prof. Dr. V. Evers, UT)
8. Process Mining & Business Process management (focus director: Prof. Dr.H. Reijers, UT)
9. Enterprise Information Systems (focus director: Dr. H. Weigand, UvT)

1. Knowledge Representation & Reasoning

This focus is the foundational area within Artificial Intelligence that studies fundamental and theoretical properties of methods for symbolically representing and manipulating knowledge. This field is firmly placed in the symbolic branch of AI, and typically uses formal logic (in the broad sense of the word “logic”) as its guiding paradigm. Besides “logic for representation”, the second major ingredient of KR&R is the study of theory of and implemented systems for `reasoning’, i.e., the use of computation for manipulating the logical symbols to derive new information. Thus, KR&R is both concerned with which inferences are sanctioned, and with which inferences can be efficiently made.

Research themes
In particular the following topics will be addressed within the School:

Reasoning:
– deduction, abduction and induction
– automated reasoning
– reasoning with uncertainty, Bayesian nertworks approximate reasoning, common-sense reasoning
– qualitative modelling reasoning – causal reasoning and causal inference – diagnostic reasoning
– planning
– complexity of reasoning

Logic and representation:
– logics for artificial intelligence, modal logics, deontic logic, non-monotonic logic, fuzzy logic, epistemic logic, description logic, representation of belief, intention, time, space, action, events, emotions
– belief/theory revision
– knowledge representation for databases, updates in databases, linked open data
– ontologies, shared and distributed knowledge, conceptual modelling
– knowledge and concept structures for the web, semantic web
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2. Computational Intelligence

Traditional AI research is strongly oriented to symbolic representations (and reasoning) in a top-down manner. The structure of a problem or environment is analysed beforehand and the construction of an intelligent system is based upon this system. Roughly spoken, Computational Intelligence comprises a number of techniques and methods that share the property of being non-symbolic (or rather sub-symbolic) and operate in a bottom-up fashion, where structure usually emerges from an unordered begin, rather than being imposed from above.

Research themes
In particular the following topics will be addressed within the School:
– advances in data science and big data – machine learning
– neural and evolutionary computing
– datamining / intelligent data analysis
– adaptive / self-organizing / fuzzy systems
– quantitative / statistical empirical research
– probabilistic reasoning / Bayesian networks
– pattern and image recognition
– intelligent search algorithms / games
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3. Agent-technology

Agents are software (or hardware) entities that display a certain degree of autonomy while operating in a dynamic, distributed environment (possibly inhabited by other agents). Agents possess properties like reactiveness, pro-activeness and social behavior, often thought of as being brought about by mental or cognitive attitudes involving knowledge, beliefs, desires, goals, intentions, emotions,… As such there is a relation with cognitive modelling in psychology. Agents are capable of interacting with their environment and communicating with other agents. The area of agent technology covers the foundations as well as the design, implementation, and application of intelligent agents, both stand-alone and within the context of multi-agent systems, in very diverse domains. Agent theories and, in particular agent logics, provide the formal foundations for agent technology and a basis for the specification and verification of agent applications. Distributed and parallel systems research provides the more technological foundation for agent architectures and programming languages.

Research themes
Important topics of research are thus: synchronization, communication, shared memory, co-ordination, negotiation, distributed reasoning/problem solving and task execution (e.g. distributed/cooperative planning and resource allocation), and electronic institutions enforcing norms on the agents in an (open) MAS. Also the methodology of constructing agent programs and systems is an important topic of research. Applications of agent technology are numerous, and range from intelligent personal assistants in various environments to cognitive robots and trading agents in e-commerce settings.
In particular the following topics will be addressed within the School:
– agent theories and logics
– agent-oriented programming including agent verification
– multi-agent systems (MAS), distributed and parallel systems
– agent communication, co-ordination, planning and negotiation
– applications of agent-based systems, such as e-commerce, cognitive robotics, virtual characters in video games, companion robots.
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4. Computational Linguistics

According to the The Association for Computational Linguistics (ACL) Computational linguistics is the scientific study of language from a computational perspective. Computational linguists are interested in providing computational models of various kinds of linguistic phenomena. These models may be “knowledge-based” (“hand-crafted”) or “data-driven” (“statistical” or “empirical”) a well-known distinction in the field of AI. In the early days computational intelligence was sometimes identified with machine translation and the topic was acknowledged by AI in the early fifties. Today much research in the field of CL is firmly rooted in Artificial Intelligence and Cognitive Science. Traditionally, computational linguistics research was conducted by computer scientists who had specialized in the application of computers to the processing of a natural language or by linguists who had adopted computational techniques to answer traditional research questions in the filed of linguistics and literary studies with the new techniques, or to raise new questions that had not yet been raised before.
Today, computational linguists often work as members of interdisciplinary teams, which may include regular linguists, experts in the target language, and computer scientists. the involvement of linguists, computer scientists, experts in artificial intelligence, mathematicians, logicians, philosophers, cognitive scientists, cognitive psychologists, psycholinguists, anthropologists and even neuroscientists. .
Work in computational linguistics is often motivated from a scientific perspective in that one is trying to provide a computational account or explanation for a specific linguistic phenomenon; and in other cases the motivation may be primarily technological in that one wants to provide a working component of a speech or natural language system, including speech recognition systems, text-to-speech synthesizers, automated voice response systems, web search engines, text editors, text mining tools

Research themes
Important topics of research are:
– the intersection of machine learning and language technology: computers that learn to understand and generate natural language. Special topics include include memory-based learning, machine translation, the relation between written and spoken language, text mining
– the intersection of Natural Language Processing (NLP) and Information Retrieval: this may include information search in complex domains, text mining applications
– the intersection of computational linguistics, and psycholinguistics (text and discourse, multimodal communication, embodied cognition, medical informatics, and spatio-temporal models)
– computational lexicology and lexicography
– computational research in literary studies, stylometrics
– grammatical inference, i.e. the learning of grammars or structure in sequences
– computational humanities and cultural heritage
– computational Semantics and Natural Language Generation, algorithmic models of human language production, the automatic processing of face-to-face interactions between humans or humans and social computing systems such as robots or virtual agents. On the one hand this involves the analysis of verbal and nonverbal behaviors in human conversations. On the other hand it involves the creation of virtual humans and humanoid robots that can engage in natural interaction with humans.
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5. Web-based information systems

This research focus covers the broad terrain of retrieval and presentation of semi-structured document-centric information. In essence, stored information is retrieved and presented to a user. A defining feature is that the information is primarily intended for human, rather than machine, consumption. The work investigates methodologies and techniques for selecting and manipulating the information, rather than concentrating on implementation details of the underlying software. We assume that document (fragments) are stored and are available for processing. The core of the work is on filtering and retrieving relevant documents, with emphasis on methods for specifying and increasing relevancy and topic coverage, and on presenting these to a user. Presentation includes synthesising relevant fragments into a coherent document that conveys the intended semantics to the user. Delivery media include computer-based presentation methods such as hypertext, multimedia and hypermedia.

Research themes
The method of retrieval falls within the scope and includes text-based techniques as well as more recent image, and other media-specific, techniques. Text-based (symbolic) techniques include carrying out static analyses of collections and creating indices based on discriminating terms. Other media-based techniques (data-based, e.g. intensity) require first an analysis of the raw data before more symbolic oriented techniques (e.g. shapes) can be applied to find desired objects. More recently, attention has been given to semantic-based techniques, where human-meaningful labels are assigned to parts of the raw data. For example, the annotation of images in a image collection or the archival of television broadcasts. The annotations can then be used to improve the retrieval process. Presenting the information includes any processing required on the retrieved information to make it suitable for display to the end-user. For example, selecting an HTML document for display on a Web browser is a simple example. A slightly more complex scenario is the display of an XML document (which contains no default presentation information) with the use of style sheet processing by the browser. Yet more complex processing could be carried out by transforming stored textual information to synthesised voice for “display” on a mobile phone. Presentation of information can be more complex, and include the use of interactive information, such as hypertext, or time-based information, such as multimedia. Document transformation techniques as detailed above can also be applied to more complex document types. A further step of abstraction away from the final presentation (where XML can be seen as an abstraction step away from XHTML) is to retrieve semantic-based information. The information represented by the semantics then has to be conveyed to the user somehow, e.g. a visualisation of the graph structure of the underlying relationships, or as document fragments incorporated into a (hyper/multi-media) document. Relevant technologies include: Web document languages (XHTML, SMIL, SVG, MathML), Web document transformation languages (XSLT, XSL), Semantic Web languages (RDF, RDFS, DAML+OIL, OWL), tools and applications.
In particular the following topics will be addressed within the School:
– information retrieval
– semi-structured data
– hypertext and hypermedia
– multimedia
– the semantic web
– web document (transformation) languages
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6. Datamanagement, storage and retrieval

The scope of the SIKS research focus Data Management, Storage and Retrieval is the theory and the application of computers to the management of information, including the aspects of data acquisition, organization, storage, querying and retrieval, security and privacy, ranging from highly structured databases to unstructured natural language texts.

Research themes
The research focus Data Management, Storage and Retrieval is shaped by two major success stories in Computer Science: 1) the development of relational database systems in the 1970’s and 1980’s mainly influenced by office automation and enterprise information systems, and 2) the development of large scale information retrieval systems at the end of the 1990’s, influenced by the development of the world wide web. The storage and retrieval component of today’s information system is formed by database management systems (DBMSs), which abstract the peculiarities of storage media and processing components into a data model, integrity rules, and query facilities. Although strong relational DBMSs have become a commodity product for administrative information system applications, they have been proven rather inadequate for storing and search semi-structured data such as web data. The storage and retrieval component of today’s search engines is formed by information retrieval (IR) systems, that provide effective ranking strategies, efficient indexes, data compression focusing on user satisfaction rather than on integrity of the data.
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7. Human Computer Interaction (HCI)

Interactive Systems (IS) are systems where humans communicate and cooperate with, and through, information technology (IT). Designing IS means designing communication and cooperation technology. Designing IS requires a design approach that is based on understanding human information processing, human communication and cooperation and distributed cognition, human experience while working with technology, and the art and craft of designing the interface between human users and IT, the user interface (UI). The design of the UI has to be based on understanding the human needs, tasks, emotions, the culture of technology use, and the situation of application of the IS. In fact the result of the design of the UI can be understood as a set of requirements for the IT part of the IS. From the point of view of the users and their situation, this set of requirements is all that matters, and apart from this they do not care “what is inside”. For the human users and their situation, the UI is the IT system.
The science of designing the IS requires a basis in
-humanities (human information processing, human emotion, organisational psychology, distributed cognition, anthropology and ethnography);
– interaction design (artistic design knowledge and crafts at many modalities: sound, graphics, animation, theatre);
– IT (software engineering, multimedia technology, web technology, information architecture);
– hardware design (industrial and artistic design in relation to the UI);
– usability engineering (the science of HCI, cognitive ergonomics, UI design, task analysis and task design, knowledge management and knowledge engineering).
Research in the domain of HCI requires a strong multidisciplinary basis and a view on the integration of concepts, methods, and techniques from these basic sciences.

Research themes
In particular the following topics will be addressed within the School:

-Relation between UI design and Requirements Engineering
– Mental models and distributed knowledge in IS
– Modelling of verbal and non-verbal interaction
– Virtual reality and multi-agent systems in the UI
– Embodied conversational agents
– The notion of experience as a goal for the client of design, as well as an aspect of technology use in context
– Role of externalisation (e.g. visualisation) of information during interaction
– Enhancement in usability of mobile devices
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8. Process Mining & Business Process Management

under construction
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9. Enterprise Information Systems

Enterprise Information Systems (EIS) are systems that provide automated support for business processes in complex organizations. Organizations can be commercial or non-commercial, e.g. government, healthcare, NGOs, or communities of practice. EIS are used inside the organization but can also be interorganizational, supporting for instance (e-)business collaborations or virtual networks. To be effective, EIS require an optimal alignment of the business system and the information system based on architecture-driven design.

Research themes
EIS research can be both design-oriented and empirical, and flourishes by a combination of both. By its nature, there is a tight connection with other disciplines, such as organization theory and management science. Research topics addressed in SIKS include:
-business
-services science
-business process management
-ICT-enabled innovation
-method engineering
-architectures (description languages, design, validation)
-modeling of organizations, communication and coordination mechanisms
-IT governance
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