Friederike Klan

A Conversational Approach to Semantic Web Service Selection

Service consumers typically have no clear goal in mind when looking for service functionality and are not able to formulate their service needs in a formal or semi-formal language. We approach those issues by proposing a mechanism that implements semantic service selection as an incremental and interactive process alternating phases of intermediate service recommendation and requirements refinement by critiquing the presented alternatives. It thus facilitates the incremental construction of service requirements and their specification at an informal level. Our evaluation results demonstrate the effectiveness and efficiency of the proposed approach in an e-commerce domain.

A Personalized Approach to Experience-Aware Service Ranking and Selection

Existing approaches to service ranking and selection evaluate the suitability of available services for a given request based on the advertisement created by the service provider. They will compare how well the advertisement matches the service request and will choose the service with the best matching advertisement. Unfortunately, at this point in time, it is uncertain whether the service that will actually be performed will match the request as well as the advertisement promised. In this paper, we present an approach that reduces the degree of this uncertainty by taking previous experiences with the service provider (which reflect the performance of the actual service notthe advertisement) into account. Contrary to many other approaches our solution accounts for the subjective nature of rating-based experiences by considering the preferences of the experience creators. Moreover it exploits the number of available experiences more effectively by considering not only experiences for a given service, but also experiences for similar services of the same provider. Our solution utilizes indirect user information and avoids explicit sharing of personal consumer information.

Honey Bee Versus Apis Mellifera: A Semantic Search for Biological Data

While literature portals in the biomedical domain already enhance their search applications with ontological concepts, data portals offering biological primary data still use a classical keyword search. Similar to publications, biological primary data are described along meta information such as author, title, location and time which is stored in a separate file in XML format. Here, we introduce a semantic search for biological data based on metadata files. The search is running over 4.6 million datasets from GFBio - The German Federation for Biological Data (GFBio, https://www.gfbio.org), a national infrastructure for long-term preservation of biological data. The semantic search method used is query expansion. Instead of looking for originally entered keywords the search terms are expanded with related concepts from different biological vocabularies. Hosting our own Terminology Service with vocabularies that are tailored to the datasets, we demonstrate how ontological concepts are integrated into the search and how it improves the search result.

Selecting and Tailoring Ontologies with JOYCE

We present Joyce, a scalable tool for identifying and assembling relevant (pieces of) ontologies from a repository of source ontologies, thus enabling the effective and efficient reuse of formalized domain knowledge. Joyce includes a conceptual filter to identify relevant classes, minimizes unintended redundancies, i.e. concept duplicates, and excludes knowledge considered irrelevant for the specific conceptual design task.

Enabling trust-aware semantic web service selection a flexible and personalized approach

In todays online markets, consumers need support in finding providers that offer the products or services they need and that are trustworthy. While Semantic Web Services (SWS) research addresses the first problem (discovering functionally suitable service providers), it neglects the second. Hence, several attempts have been made to complement service retrieval techniques based on semantic matchmaking with trust-establishing techniques that leverage collaborative consumer feedback. However, the diversity and multi-faceted nature of SWS impose special requirements on the underlying feedback mechanism, in particular w.r.t. their flexibility and expressiveness. Existing approaches only partially meet those requirements. In this paper, we will therefore propose a trust-establishing mechanism for Semantic Web Services that allows to assess a service providers trustworthiness with respect to various service aspects and is flexible enough to adjust to various kinds of services and consumer requirements.

A sequence-based tree similarity search

Tree-structured data are pervasively growing and exploiting them based on similarity is essential for a broad number of applications. Therefore, there has been a growing need to develop high-performance techniques to efficiently look for similar trees across a large number of trees. To this end, in this paper, we present a new sequence-based approach for tree similarity search that exploits both the structural and the content characteristics of tree-structured data. In particular, we transform tree data into sequence representations using a modified Prüfer sequence that constructs a one-to-one mapping between tree data and their sequence representations. We introduce a new tree sequence distance based on the structural information of the data tree, which filters out a set of false positive candidates. We then introduce a refinement step exploiting the content information of data trees. The preliminarily experimental results show that our algorithm achieves high performance. Our method is especially suitable for accelerating similarity computation in clustering and/or classification of large numbers of trees in massive datasets.

A User-Centered Methodology for the Evaluation of (Semantic) Web Service Discovery and Selection

Recently, a new breed of user-centric solutions to Web Service discovery and selection that applies Semantic Web Service technology in B2C settings such as e-Commerce has evolved. They significantly differ from traditional Web Service frameworks and have to cope with new challenges such as assisting consumers in specifying service requirements and providing effective decision support for service selection. Existing evaluation efforts within the scope of Semantic Web Services do not account for these user-specific requirements and hence are not appropriate for assessing the quality of solutions that are dedicated to end-users, i.e. service consumers. In this paper, we address this issue by proposing a user-centered methodology for the evaluation of Semantic Web Service retrieval and demonstrating its feasibility.

Recommending Judgment Targets for Rating Provision

Existing rating and reviewing schemes typically come in the in the flavor of a single rating and/or a textual review. While a single judgment evaluating the overall quality of a product is of limited significance, textual customer reviews typically deliver more informative feedback at the attribute level. However, reading and comparing them to extract relevant information is time-consuming and mentally-demanding. Moreover, they are often biased and selective in the product features they consider and thus are less helpful for making informed buying decisions. We therefore propose a rating elicitation scheme that supports consumers in providing meaningful and machine-comprehensible, i.e. automatically process able, responses in terms of multi-criteria ratings judging several features of a purchased product or used service. This is achieved by recommending suitable judgment targets and thereby accounting for a customers willingness to provide ratings. Our evaluation results show that the proposed procedure effectively adjusts to a consumers personal judgment preferences and thus provides helpful support for the elicitation of meaningful multi-criteria feedback.