Stefan Nadschläger

Content-Based Recommendations within a QA System Using the Hierarchical Structure of a Domain-Specific Taxonomy

In this paper we present several content-based recommendation methods for a QA system that rely and use extensively the structure of a domain-specific taxonomy. Our goal is to add semantics to a typical content-based RS in order to improve the quality of the recommendations by mapping relevant keywords from the existing taxonomy to the available questions. In order to test and evaluate the effectiveness of the above mentioned methods, we conducted a supervised survey where we asked several users to rate the recommendations delivered using these methods. The results show that by combining the results retrieved by these methods, we obtain a range of recommendations that satisfy a variety of user expectations.

A pattern collection for knowledge processing system architecture

Many architecture and design patterns exist for enterprise software development. Nowadays interest of knowledge processing systems has been heightened, as these technologies can provide a valuable benefit for a company (e.g., supporting decision making). Nevertheless, the algorithms and technologies used in this domain can be complex and difficult to implement. Some parts can even outreach standard software development. This paper tries to identify similarities to enterprise systems and present a selection of existing design patterns that can be used to solve knowledge processing difficulties. The aim is to provide a pattern collection to allow also software designers and developers not familiar with knowledge processing principles, to easily design, implement and integrate such systems.

Architecture of an Extendable and Cloud-Ready Knowledge Management and Processing Framework for the Agricultural Domain

In this paper we present a software architecture for a Knowledge Management and Processing Framework initially for usage in the agricultural domain, but customizable for any domain. In contrast to existing Knowledge Management and Processing Systems, this proposed architecture mainly focuses on the usage of a cloud platform as execution environment and therefore pays special attention to the design aspects to utilize the benefits of a cloud infrastructure, by designing the system parallelizable and distributable. We identified the main aspects of a cloud-ready system platform and combined them with the needed functionality for a custom Knowledge Management and Processing Framework.

Range-Based Clustering Supporting Similarity Search in Big Data

Thanks to state-of-the-art technologies, we have more and more modern infrastructures as well as automatic processes supporting the agricultural domain. Data collected from parcels by these systems and remote sensors for further analysis result in facing the three main challenges which are known as big volume, big variety, and big velocity, in the era of big data. In terms of similarity search, we propose a range-based clustering method that finds objects which are the most similar compared to the given object in a large-scale computing with Map Reduce. The proposed method groups objects into different clusters which are considered as pivots to perform pre-checking before computing similarity. Furthermore, we conduct some basic experiments to evaluate the performance of the proposed method and observe the influences of the clusters in similarity search.

An Efficient Document Indexing-Based Similarity Search in Large Datasets

In this paper, we principally devote our effort to proposing a novel MapReduce-based approach for efficient similarity search in big data. Specifically, we address the drawbacks of using inverted index in similarity search with MapReduce and then propose a simple yet efficient redundancy-free MapReduce scheme, which not only takes advantages over the baseline inverted index-based procedures but also adapts to various similarity measures and similarity searches. Additionally, we present other strategic methods in order to potentially contribute to eliminating unnecessary data and computations. Last but not least, empirical evaluations are intensively conducted with real massive datasets and Hadoop framework in the cluster of commodity machines to verify the proposed methods, whose promising results show how much beneficial they are when dealing with big data.

Knowledge-intensive Business Processes—A Case Study for Disease Management in Farming

Knowledge-intensive business processes (KIBPs) are strongly connected with knowledge work (KW). Thus, the definition of KW determines the relevant area of KIBPs. KW characteristics such as rather unstructured processes, user-driven, relying on knowledge, need for flexibility, adaptability, creativity and autonomy of knowledge workers are also associated with KIBPs. However, several authors argue based on their empirical findings that KW often also involves predefined, repetitive tasks besides a lot of creative work. Furthermore, latest trends put more emphasis on the practice of knowing. Based on our understanding of KW, we study a farming business process, which is not regarded as a typical KW domain. However, when looking at the details, many KIBP characteristics can be identified. Based on a use case dealing with disease management, particularly plant protection, in farming, we evaluate our understanding of KIBPs and thus, prepare the basis for the requirements definition concerning supporting models and methods with respect to adequate IT support.

Data, Information & Knowledge Sources in the Agricultural Domain

We try to make a first step towards merging sources in the agricultural domain with experts and methods from the IT sector. The result should help people in this domain to profit from a better and more productive way of using existing experiences by sharing and making them easier accessible. After a short definition of several knowledge-related terms we present existing and possibly useful standards for sources in the agricultural domain. Based on the standards, we give a short overview on existing sources and present a way for automated extraction of information and knowledge from selected sources. Finally we show the usage of some sources, which are implemented in our current research work.

Semantic Data Integration and Relationship Identification Using the Hierarchical Structure of a Domain-Specific Taxonomy

In this paper we present an architecture for integrating IT-Service-Management-data and a method for relationship identification using a domain-specific taxonomy. This knowledge can be used for semantic data integration by automatically accessing different data sources and identifying dependencies between various IT-Service-items. This can be done by using existing categories and documents to build up a taxonomy, extracting keywords and mapping these keywords to existing data assets. To show the functionality, we created a simple taxonomy and tried to identify the relationships between various assets.

Analysis of GoF Design Patterns used in Knowledge Processing Systems

To increase the quality of knowledge processing systems and provide help to software developers, selected existing knowledge processing systems are analysed for the occurrence of used object-oriented design patterns (especially from the Gang-of-Four catalogue). This analysis intends to draw attention to the lack of good software design in the area of knowledge processing systems and at the same time provides a smaller catalogue of design patterns with proven usage in practice, to support development. The design patterns were identified manually in a structured analysis by reverse engineering the source code, supported by a design pattern detection tool. As a result, Gang-of-Four design patterns, suitable for developing custom knowledge processing systems, are presented and discussed.

Towards a pattern language for knowledge processing systems: expert systems

Existing knowledge processing systems, especially expert systems, do not always fit to a companys needs. This reduces the benefits of such a technology, or even completely prevents their usage. Therefore, an architectural guideline is needed to enable software engineers to design and implement custom knowledge processing systems. In this paper a first approach via a pattern language for knowledge processing systems, consisting of five patterns covering the basic components needed, is presented. The patterns were extracted from three different open source expert systems / rule engines. The applicability of the patterns is discussed by applying them on an example custom knowledge processing system project that shows how the pattern language supports the design and implementation.