Contributions published at Dynamic and Distributed Information Systems (Abraham Bernstein)

Learning ontologies from large text corpora is a well understood task while evolving ontologies dynamically from user-input has rarely been adressed so far. Evolution of ontologies has to deal with vague or incomplete information. Accordingly, the formalism used for knowledge representation must be able to handle this kind of information. Classical logical approaches such as description logics are particularly poor in adressing uncertainty. Ontology evolution may benefit from exploring probabilistic or fuzzy approaches to knowledge representation. In this thesis an approach to evolve and update ontologies is developed which uses explicit and implicit user-input and extends probabilistic approaches to ontology engineering.

Currently proposed Semantic Web Services technologies allow the creation of ontology-based semantic annotations of Web services so that software agents are able to discover, invoke, compose and monitor these services with a high degree of automation. The OWL Services (OWL-S) ontology is an upper ontology in OWL language, providing essential vocabularies to semantically describe Web services. Currently OWL-S services can only be developed independently; if one service is unavailable then finding a suitable alternative would require an expensive and difficult global search/match. It is desirable to have a new OWL-S construct that can systematically support substitution tracing as well as incremental development and reuse of services. Introducing inheritance relationship (IR) into OWL-S is a natural solution. However, OWL-S, as well as most of the other currently discussed formalisms for SemanticWeb Services such as WSMO or SAWSDL, has yet to define a concrete and self-contained mechanism of establishing inheritance relationships among services, which we believe is very important for the automated annotation and discovery of Web services as well as human organization of services into a taxonomy-like structure. In this paper, we extend OWL-S with the ability to define and maintain inheritance relationships between services. Through the definition of an additional “inheritance profile”, inheritance relationships can be stated and reasoned about. Two types of IRs are allowed to grant service developers the choice to respect the “contract” between services or not. The proposed inheritance framework has also been implemented and the prototype will be briefly evaluated as well.

Recommender systems play an important role in supporting people when choosing items from an overwhelming huge number of choices. So far, no recommender system makes use of domain knowledge. We are modeling user preferences with a machine learning approach to recommend people items by predicting the item ratings. Specifically, we propose SemTree, an ontology-based decision tree learner, that uses a reasoner and an ontology to semantically generalize item features to improve the effectiveness of the decision tree built. We show that SemTree outperforms comparable approaches in recommending more accurate recommendations considering domain knowledge.

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Querying and storing huge amounts of Semantic Web data – this has usually required a lot of computational power. This is no longer true if one makes use of recent research outcomes like modern RDF indexing strategies. We present a mobile conference guide application that combines several different RDF data sets to present interlinked information about publications, conferences, authors, locations, and others to the user. With our application we show that it is possible to store a big amount of indexed data on an iPhone/iPod Touch device. That querying is also efficent is demonstrated by creating the application’s actual content out of real time queries on the data.

The java code that was worked out to generate synthetic bank transactions with real distribution figures is explained in the present bachelor thesis: The Transaction Evaluator analyses the structure of the original data, before the Transaction Builder generates the synthetic data based on the ascertained properties. Further, the performance of the program is evaluated on the basis of two test sets. It turns out that the implementation works and the results are pleasant; however it still remains to find the ideal settings.

The growing number of SemanticWeb applications producesmore and more data in RDF/OWLformat. The persistent storage of this strongly interlinked data is not trivial. The data is often mapped to relational databases, even if they are not relational, but rather graph-based. There are already various approaches to storing this data in a persistent way. Frequently these systems neglect some requirements. Hexastore was developed at the University of Zurich. This sixfold indexing approach for Semantic Web Data is scalable, does not discriminate any RDF-Elements and is applicable to any datasets without knowing anything about the dataset beforehand. The idea of Hexastore was implemented as an in-memory prototype. This work presents an approach to storing Hexastore persistently on disk. The suitability of this method is documented in several queries over two datasets and is evaluated against some reference systems.

In big financial institutions, the application landscape for collecting and controlling trading data is usually quite complex and versatile. Interfaces for data reconciliation between different systems namely front, middle and back office, hold the risk of incon-sistencies, particularly if the data is represented and pre-processed in many different ways. The process of analysis, validation and correction of issues in the reconciliation process usually requires high manual effort. Therefore, the topic of this diploma thesis is the discovery of patterns or correlations in these reconciliation inconsistencies. This allows optimizing the systems and minimizing the manual effort in long term. It is shown by theory and practical examples how to discover novel & potentially useful patterns and hidden logic by applying classic propositional as well as multi-relational data mining approaches. We check whether they make sense in the given live environment of Credit Suisse and how they bring an added value in terms of summarizing data into useful information and knowledge. While considering, applying and evaluating them, the main focus is on achieving a better understanding of the data and the systems.

Abstract for the diploma thesis “Application of Efficient Tree Similarity Algorithms on Graphs” written by Alexander Bucher This thesis presents subgraph indexing, a novel approach to query graph based datasets. To make subgraph indexing efficient on complex queries in large graph based datasets, the original graph is transformed into multiple directed subgraphs. Vector representations of these subgraphs are then stored in a tree structure. To answer a query, the subgraph representing the query is created and vectors containing similar subgraphs are retrieved from the stored data. Out of these vectors, the ones containing a valid result are filtered out and their results are presented. In the evaluation section it is shown that subgraph indexing can answer complex queries in large databases faster than some comparative approaches.

Von der herkömmlichen Suchmaschine bis zur Vision einer verständnisvollen Antwort: Potenziale und Begrenzungen. Die Entwicklung von Suchtechnologien für das World Wide Web gehört heute zu den zentralen Herausforderungen der Informatik. Eine Alternative zu den heutigen Algorithmen-basierten Suchmaschinen stellen hierbei Social-Search-Ansätze dar. Das Semantic Web beinhaltet schliesslich die Vision, komplexe natürlichsprachige Anfragen beantworten zu können.

This thesis presented how established cultural data can be used for the adaptation of websites. It also shows that different possibilities of adaption for cultures exist. The goal was to test and create adaptations and their reusability. Cultural data from past research has been used as a basis for the adaptation, and different approaches of adaptation have been collected, analyzed and compared.With the gained knowledge a cultural adaptive framework has been developed, which can be used to create cultural adaptive websites and web applications. An on-road test verified the framework of its capability and approved the reusability of the adaptations.

We propose an intelligent data mining (DM) assistant that will combine planning and meta-learning to provide support to users of virtual DM laboratory. A knowledge-driven planner will rely on a data mining ontology to plan the knowledge discovery workflow and determine the set of valid operators for each step of this workflow. A probabilistic meta-learner will select the most appropriate operators by using relational similarity measures and kernel functions over records of past sessions meta-data stored in a DM experiments repository.

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This proposal explores a general framework to solve software analysis tasks using ontologies. Our aim is to build semantically anno- tated, flexible, and extensible software repositories to overcome data representation, intra- and inter-project integration difficulties as well as to make the tedious and error-prone extraction and preparation of meta-data obsolete. We also outline a number of practical evaluation approaches for our propositions.

This research explores a new method for Semantic Web service matchmaking based on iSPARQL strategies, which enables to query the Semantic Web with techniques from traditional information retrieval. The strategies for matchmaking that we developed and evaluated can make use of a plethora of similarity measures and combination functions from SimPack---our library of similarity measures. We show how our combination of structured and imprecise querying can be used to perform hybrid Semantic Web service matchmaking. We analyze our approach thoroughly on a large OWL-S service test collection and show how our initial strategies can be improved by applying machine learning algorithms to result in very effective strategies for matchmaking.

Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers. We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our approach can be used for any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy.

Background: We recently reported the development of the AMIS (Acute Myocardial Infarction in Switzerland) risk stratification model for patients with acute coronary syndrome (ACS). This model predicts hospital mortality risk across the complete spectrum of ACS based on 7 parameters available in the prehospital phase. Since the AMIS model was developed on a Swiss dataset in which the majority of patients were treated by primary PCI, we sought validation on an external cohort treated with a more conservative strategy. Methods: The Krakow Region (Malopolska) ACS registry included patients treated with a non-invasive strategy in 29 hospitals in the greater Krakow (PL) area between 2002-2006. In-hospital mortality risk was calculated using the AMIS model (input parameters: age, Killip class, systolic blood pressure, heart rate, pre-hospital resuscitation, history of heart failure, and history of cerebrovascular disease; risk calculator available at www.amis-plus.ch). Discriminative performance was quantified as ""area under the curve"" (AUC, range 0–1) in a receiver operator characteristic, and was compared to the risk scores for ST-elevation myocardial infarction (STEMI) and Non-STE-ACS from the TIMI study group. Results: Among the 2635 patients included in the registry (57% male, mean age 68.2±11.5 years, 31% STEMI) hospital mortality was 7.6%. The AUC using the AMIS model was 0.842, compared to 0.724 for the TIMI risk score for STEMI or 0.698 for the TIMI risk score for Non-STE-ACS (Fig. A). Risk calibration was maintained with the AMIS model over the complete range of risks (Fig. B). The performance of the AMIS model in this cohort was comparable to that found in the AMIS validation cohort (n=2854, AUC 0.868). Conclusions: The AMIS risk prediction model for ACS displayed an excellent predictive performance in this non-invasively-treated external cohort, confirming the reliability of this bedside “point-of-care” model in everyday practice.