Novel Algorithms for Cross-Ontology Multi-Level Data Mining

The wide spread use of ontologies in many scientific areas creates a wealth of ontologyannotated data and necessitates the development of ontology-based data mining algorithms. We have developed generalization and mining algorithms for discovering cross-ontology relationships via ontology-based data mining. We present new interestingness measures to evaluate the discovered cross-ontology relationships. The methods presented in this dissertation employ generalization as an ontology traversal technique for the discovery of interesting and informative relationships at multiple levels of abstraction between concepts from different ontologies. The generalization algorithms combine ontological annotations with the structure and semantics of the ontologies themselves to discover interesting crossontology relationships. The first algorithm uses the depth of ontological concepts as a guide for generalization. The ontology annotations are translated to higher levels of abstraction one level at a time accompanied by incremental association rule mining. The second algorithm conducts a generalization of ontology terms to all their ancestors via transitive ontology relations and then mines cross-ontology multi-level association rules from the generalized transactions. Our interestingness measures use implicit knowledge conveyed by the relation semantics of the ontologies to capture the usefulness of cross-ontology relationships. We describe the use of information theoretic metrics to capture the interestingness of cross-ontology relationships and the specificity of ontology terms with respect to an annotation dataset. Our generalization and data mining agorithms are applied to the Gene Ontology and the postnatal Mouse Anatomy Ontology. The results presented in this work demonstrate that our generalization algorithms and interestingness measures discover more interesting and better quality relationships than approaches that do not use generalization. Our algorithms can be used by researchers and ontology developers to discover inter-ontology connections. Additionally, the cross-ontology relationships discovered using our algorithms can be used by researchers to understand different aspects of entities that interest them

Developing semantic pathway comparison methods for systems biology

Systems biology is an emerging multi-disciplinary field in which the behaviour of complex biological systems is studied by considering the interaction of many cellular and molecular constituents rather than using a “traditional” reductionist approach where constituents are studied individually. Systems are often studied over time with the ultimate goal of developing models which can be used to understand and predict complex biological processes, such as human diseases. To support systems biology, a large number of biological pathways are being derived for many different organisms, and these are stored in various databases. This pathway collection presents an opportunity to compare and contrast pathways, and to utilise the knowledge they represent. This thesis presents some of the first algorithms that are designed to explore this opportunity. It is argued that the methods will be useful to biologists in order to assess the biological plausibility of derived pathways, compare different biological pathways for semantic similarities, and to derive putative pathways that are semantically similar to documented biological pathways. The methods will therefore extend the systems biology toolbox that biologists can use to make new biological discoveries.Knowledge Foundation. Grant No. 2003/0215Information Fusion Research Program (University of Skovde, Sweden) Grant No 2003/010

Pharmacovigilance Decision Support : The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology

The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that may be used in the analysis of drug safety data. These methods differ significantly from the statistical methods in that they utilize co variability methods of association to define drug-reaction relationships. Co variability algorithms were developed in collaboration with Musa Mammadov to discover drugs associated with adverse reactions and possible drug-drug interactions. This method uses the system organ class (SOC) classification in the Australian Adverse Drug Reaction Advisory Committee (ADRAC) data to stratify reactions. The text categorization algorithm BoosTexter was found to work with the same drug safety data and its performance and modus operandi was compared to our algorithms. These alternative methods were compared to a standard disproportionality analysis methods for signal detection in drug safety data including the Bayesean mulit-item gamma Poisson shrinker (MGPS), which was found to have a problem with similar reaction terms in a report and innocent by-stander drugs. A classification of drug terms was made using the anatomical-therapeutic-chemical classification (ATC) codes. This reduced the number of drug variables from 5081 drug terms to 14 main drug classes. The ATC classification is structured into a hierarchy of five levels. Exploitation of the ATC hierarchy allows the drug safety data to be stratified in such a way as to make them accessible to powerful existing tools. A data mining method that uses association rules, which groups them on the basis of content, was used as a basis for applying the ATC and SOC ontologies to ADRAC data. This allows different views of these associations (even very rare ones). A signal detection method was developed using these association rules, which also incorporates critical reaction terms.Doctor of Philosoph

개인화 검색 및 파트너쉽 선정을 위한 사용자 프로파일링

학위논문 (박사)-- 서울대학교 대학원 : 치의과학과, 2014. 2. 김홍기.The secret of change is to focus all of your energy not on fighting the old, but on building the new. - Socrates The automatic identification of user intention is an important but highly challenging research problem whose solution can greatly benefit information systems. In this thesis, I look at the problem of identifying sources of user interests, extracting latent semantics from it, and modelling it as a user profile. I present algorithms that automatically infer user interests and extract hidden semantics from it, specifically aimed at improving personalized search. I also present a methodology to model user profile as a buyer profile or a seller profile, where the attributes of the profile are populated from a controlled vocabulary. The buyer profiles and seller profiles are used in partnership match. In the domain of personalized search, first, a novel method to construct a profile of user interests is proposed which is based on mining anchor text. Second, two methods are proposed to builder a user profile that gather terms from a folksonomy system where matrix factorization technique is explored to discover hidden relationship between them. The objective of the methods is to discover latent relationship between terms such that contextually, semantically, and syntactically related terms could be grouped together, thus disambiguating the context of term usage. The profile of user interests is also analysed to judge its clustering tendency and clustering accuracy. Extensive evaluation indicates that a profile of user interests, that can correctly or precisely disambiguate the context of user query, has a significant impact on the personalized search quality. In the domain of partnership match, an ontology termed as partnership ontology is proposed. The attributes or concepts, in the partnership ontology, are features representing context of work. It is used by users to lay down their requirements as buyer profiles or seller profiles. A semantic similarity measure is defined to compute a ranked list of matching seller profiles for a given buyer profile.1 Introduction 1 1.1 User Profiling for Personalized Search . . . . . . . . 9 1.1.1 Motivation . . . . . . . . . . . . . . . . . . . 10 1.1.2 Research Problems . . . . . . . . . . . . . . 11 1.2 User Profiling for Partnership Match . . . . . . . . 18 1.2.1 Motivation . . . . . . . . . . . . . . . . . . . 19 1.2.2 Research Problems . . . . . . . . . . . . . . 24 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . 25 1.4 System Architecture - Personalized Search . . . . . 29 1.5 System Architecture - Partnership Match . . . . . . 31 1.6 Organization of this Dissertation . . . . . . . . . . 32 2 Background 35 2.1 Introduction to Social Web . . . . . . . . . . . . . . 35 2.2 Matrix Decomposition Methods . . . . . . . . . . . 40 2.3 User Interest Profile For Personalized Web Search Non Folksonomy based . . . . . . . . . . . . . . . . 43 2.4 User Interest Profile for Personalized Web Search Folksonomy based . . . . . . . . . . . . . . . . . . . 45 2.5 Personalized Search . . . . . . . . . . . . . . . . . . 47 2.6 Partnership Match . . . . . . . . . . . . . . . . . . 52 3 Mining anchor text for building User Interest Profile: A non-folksonomy based personalized search 56 3.1 Exclusively Yours' . . . . . . . . . . . . . . . . . . . 59 3.1.1 Infer User Interests . . . . . . . . . . . . . . 61 3.1.2 Weight Computation . . . . . . . . . . . . . 64 3.1.3 Query Expansion . . . . . . . . . . . . . . . 67 3.2 Exclusively Yours' Algorithm . . . . . . . . . . . . 68 3.3 Experiments . . . . . . . . . . . . . . . . . . . . . . 71 3.3.1 DataSet . . . . . . . . . . . . . . . . . . . . 72 3.3.2 Evaluation Metrics . . . . . . . . . . . . . . 73 3.3.3 User Profile Efficacy . . . . . . . . . . . . . 74 3.3.4 Personalized vs. Non-Personalized Results . 76 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . 80 4 Matrix factorization for building Clustered User Interest Profile: A folksonomy based personalized search 82 4.1 Aggregating tags from user search history . . . . . 86 4.2 Latent Semantics in UIP . . . . . . . . . . . . . . . 90 4.2.1 Computing the tag-tag Similarity matrix . . 90 4.2.2 Tag Clustering to generate svdCUIP and modSvdCUIP 98 4.3 Personalized Search . . . . . . . . . . . . . . . . . . 101 4.4 Experimental Evaluation . . . . . . . . . . . . . . . 103 4.4.1 Data Set and Experiment Methodology . . . 103 4.4.1.1 Custom Data Set and Evaluation Metrics . . . . . . . . . . . . . . . 103 4.4.1.2 AOL Query Data Set and Evaluation Metrics . . . . . . . . . . . . . 107 4.4.1.3 Experiment set up to estimate the value of k and d . . . . . . . . . . 107 4.4.1.4 Experiment set up to compare the proposed approaches with other approaches . . . . . . . . . . . . . . . 109 4.4.2 Experiment Results . . . . . . . . . . . . . . 111 4.4.2.1 Clustering Tendency . . . . . . . . 111 4.4.2.2 Determining the value for dimension parameter, k, for the Custom Data Set . . . . . . . . . . . . . . . 113 4.4.2.3 Determining the value of distinctness parameter, d, for the Custom data set . . . . . . . . . . . . . . . 115 4.4.2.4 CUIP visualization . . . . . . . . . 117 4.4.2.5 Determining the value of the dimension reduction parameter k for the AOL data set. . . . . . . . . . . . 119 4.4.2.6 Determining the value of distinctness parameter, d, for the AOL data set . . . . . . . . . . . . . . . . . . 120 4.4.2.7 Time to generate svdCUIP and modSvd-CUIP . . . . . . . . . . . . . . . . 122 4.4.2.8 Comparison of the svdCUIP, modSvd-CUIP, and tfIdfCUIP for different classes of queries . . . . . . . . . . 123 4.4.2.9 Comparing all five methods - Improvement . . . . . . . . . . . . . . 124 4.4.3 Discussion . . . . . . . . . . . . . . . . . . . 126 5 User Profiling for Partnership Match 133 5.1 Supplier Selection . . . . . . . . . . . . . . . . . . . 137 5.2 Criteria for Partnership Establishment . . . . . . . 140 5.3 Partnership Ontology . . . . . . . . . . . . . . . . . 143 5.4 Case Study . . . . . . . . . . . . . . . . . . . . . . 147 5.4.1 Buyer Profile and Seller Profile . . . . . . . 153 5.4.2 Semantic Similarity Measure . . . . . . . . . 155 5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . 160 5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . 162 6 Conclusion 164 6.1 Future Work . . . . . . . . . . . . . . . . . . . . . . 167 6.1.1 Degree of Personalization . . . . . . . . . . . 167 6.1.2 Filter Bubble . . . . . . . . . . . . . . . . . 168 6.1.3 IPR issues in Partnership Match . . . . . . . 169 Bibliography 170 Appendices 193 .1 Pairs of Query and target URL . . . . . . . . . . . 194 .2 Examples of Expanded Queries . . . . . . . . . . . 197 .3 An example of svdCUIP, modSvdCUIP, tfIdfCUIP 198Docto

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

LWA 2013. Lernen, Wissen & Adaptivität ; Workshop Proceedings Bamberg, 7.-9. October 2013

LWA Workshop Proceedings: LWA stands for "Lernen, Wissen, Adaption" (Learning, Knowledge, Adaptation). It is the joint forum of four special interest groups of the German Computer Science Society (GI). Following the tradition of the last years, LWA provides a joint forum for experienced and for young researchers, to bring insights to recent trends, technologies and applications, and to promote interaction among the SIGs