Holger Wache

Improving the accuracy of job search with semantic techniques

In this paper we introduce a prototype job portal which uses semantically annotated job offers and applicants. In our opinion, using Semantic Web technologies substantially increase market transparency, lower transaction costs and speed up the procurement process. However adding semantics is not a panacea for everything. We identify some outstanding problems in job search using the system and outline how the technique of query approximation can be the basis for a solution. Through an Industry-Research co-operation we are extending the prototype with these semantic techniques to demonstrate a more accurate job search.

Cloud Broker: Bringing Intelligence into the Cloud

Handling changes of business processes, and making sure systems are up and running after a change in the business process with minimum downtime is something which has been of interest to researchers for long and there have been several approaches proposed for it. With Cloud computing becoming increasingly popular businesses require a strong system for cloud based implementations which can handle change management of processes. In this paper we propose a change management approach for cloud backed business process models.

Scalable instance retrieval for the semantic web by approximation

Approximation has been identified as a potential way of reducing the complexity of logical reasoning. Here we explore approximation for speeding up instance retrieval in a Semantic Web context. For OWL ontologies, i.e., Description Logic (DL) Knowledge Bases, it is known that reasoning is a hard problem. Especially in instance retrieval when the number of instances that need to be retrieved becomes very large. We discuss two approximation methods for retrieving instances to conjunctive queries over DL T-Boxes and the results of experiments carried out with a modified version of the Instance Store System.

Approximating description logic classification for semantic web reasoning

In many application scenarios, the use of the Web ontology language OWL is hampered by the complexity of the underlying logic that makes reasoning in OWL intractable in the worst case. In this paper, we address the question whether approximation techniques known from the knowledge representation literature can help to simplify OWL reasoning. In particular, we carry out experiments with approximate deduction techniques on the problem of classifying new concept expressions into an existing OWL ontology using existing Ontologies on the web. Our experiments show that a direct application of approximate deduction techniques as proposed in the literature in most cases does not lead to an improvement and that these methods also suffer from some fundamental problems.

An integration method for the specification of rule-oriented mediators

Mediator specification and terminology development are two labor-intensive tasks that are serious obstacles to the widespread use of mediator-based systems. In this paper, we propose a three-step approach to acquire mediator specifications and to gradually build a domain terminology at the same time. The approach also allows the support of software assistants, which help a user to acquire and verify the semantics of information sources. We have implemented a first prototype. The integration approach (without the assistants) is currently being evaluated by some users. Furthermore, we have started the development of a second prototype which will include the couple of assistants mentioned in this article.

Relaxing RDF queries based on user and domain preferences

Research in cooperative query answering is triggered by the observation that users are often not able to correctly formulate queries to databases such that they return the intended result. Due to lacking knowledge about the contents and the structure of a database, users will often only be able to provide very broad queries. Existing methods for automatically refining such queries based on user profiles often overshoot the target resulting in queries that do not return any answer. In this article, we investigate methods for automatically relaxing such over-constrained queries based on domain knowledge and user preferences. We describe a framework for information access that combines query refinement and relaxation in order to provide robust, personalized access to heterogeneous resource description framework data as well as an implementation in terms of rewriting rules and explain its application in the context of e-learning systems.

Practical Context Transformation for Information System Interoperability

This paper discusses the use of contextual reasoning, i.e. context transformation for achieving semantic interoperability in heterogeneous information systems. We introduce terminological contexts and their explication in terms of formal ontologies. Using a real-world example, we compare two practical approaches for context transformation one based on transformation rule, the other on re-classification of information entities in a different terminological context. We argue that both approaches supplement each other and develop a unifying theory of context transformation. A sound and complete context transformation calculus is presented.

Enterprise Architecture Frameworks for Enabling Cloud Computing

Cloud computing has emerged as a strong factor driving companies to remarkable business success. Far from just being an IT level support solution cloud computing is triggering changes in their core business models by making them more efficient and cost-effective. This has generated an interest for a lot of companies to try and adopt cloud computing for their existing and new business process. In this research we present an approach which a company can use to analyze if its operations can be positively impacted by moving to the cloud. Further we describe our approach using which the company can make that transition to the cloud.

Using Consumer Behavior Data to Reduce Energy Consumption in Smart Homes: Applying Machine Learning to Save Energy without Lowering Comfort of Inhabitants

This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to existing algorithms regarding completeness/correctness of the results, run times as well as memory consumption and elaborates on the shortcomings of the different solutions. We also propose a recommender system based on the developed algorithm. This recommender provides recommendations to the users to reduce their energy consumption. The recommender system was deployed to a set of test homes. The test participants rated the impact of the recommendations on their comfort. We used this feedback to adjust the system parameters and make it more accurate during a second test phase. The historical dataset provided by digitalSTROM contained 33 homes with 3521 devices and over 4 million events. The system produced 160 recommendations on the first phase and 120 on the second phase. The ratio of useful recommendations was close to 10%.

Enterprise Architectures for Cloud Computing

In this paper we describe an approach to graphically externalize the cloud potential of a company, considering its architectural description. For this purpose it is shown how current architectural description can be extended, in terms of knowledge and graphical representation. The goal is to focus on the most important features and aspects to consider during the evaluation of shifting into a cloud environment. Even if each company has different strategies and approaches to its business activities, there are some domains related to the shift in a cloud environment that should be considered in any case. This paper shows how these main areas can be taken into account in order to extend the architectural representation of a company and express its cloud readiness.