Handling instance coreferencing in the KnoFuss architecture

Finding RDF individuals that refer to the same real-world entities but have different URIs is necessary for the efficient use of data across sources. The requirements for such instance-level integration of RDF data are different from both database record linkage and ontology schema matching scenarios. Flexible configuration and reuse of different methods is needed to achieve good performance. Our data integration architecture, called KnoFuss, implements a component-based approach, which allows flexible selection and tuning of methods and takes the ontological schemata into account to improve the reusability of methods

Fusing Automatically Extracted Annotations for the Semantic Web

This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination. Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories

KnowLife: A Versatile Approach for Constructing a Large Knowledge Graph for Biomedical Sciences

BACKGROUND: Biomedical knowledge bases (KB’s) have become important assets in life sciences. Prior work on KB construction has three major limitations. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects. RESULTS: We address these three limitations by a versatile and scalable approach to automatic KB construction. Using a small number of seed facts for distant supervision of pattern-based extraction, we harvest a huge number of facts in an automated manner without requiring any explicit training. We extend previous techniques for pattern-based IE with confidence statistics, and we combine this recall-oriented stage with logical reasoning for consistency constraint checking to achieve high precision. To our knowledge, this is the first method that uses consistency checking for biomedical relations. Our approach can be easily extended to incorporate additional relations and constraints. We ran extensive experiments not only for scientific publications, but also for encyclopedic health portals and online communities, creating different KB’s based on different configurations. We assess the size and quality of each KB, in terms of number of facts and precision. The best configured KB, KnowLife, contains more than 500,000 facts at a precision of 93% for 13 relations covering genes, organs, diseases, symptoms, treatments, as well as environmental and lifestyle risk factors. CONCLUSION: KnowLife is a large knowledge base for health and life sciences, automatically constructed from different Web sources. As a unique feature, KnowLife is harvested from different text genres such as scientific publications, health portals, and online communities. Thus, it has the potential to serve as one-stop portal for a wide range of relations and use cases. To showcase the breadth and usefulness, we make the KnowLife KB accessible through the health portal (http://knowlife.mpi-inf.mpg.de). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0549-5) contains supplementary material, which is available to authorized users

SERIMI: Class-Based Matching for Instance Matching Across Heterogeneous Datasets

State-of-the-art instance matching approaches do not perform well when used for matching instances across heterogeneous datasets. This shortcoming derives from their core operation depending on direct matching, which involves a direct comparison of instances in the source with instances in the target dataset. Direct matching is not suitable when the overlap between the datasets is small. Aiming at resolving this problem, we propose a new paradigm called class-based matching. Given a class of instances from the source dataset, called the class of interest, and a set of candidate matches retrieved from the target, class-based matching refines the candidates by filtering out those that do not belong to the class of interest. For this refinement, only data in the target is used, i.e., no direct comparison between source and target is involved. Based on extensive experiments using public benchmarks, we show our approach greatly improves the quality of state-of-the-art systems; especially on difficult matching tasks

Predicting Changes to Source Code

Organizations typically use issue tracking systems (ITS) such as Jira to plan software releases and assign requirements to developers. Organizations typically also use source control management (SCM) repositories such as Git to track historical changes to a code-base. These ITS and SCM repositories contain valuable data that remains largely untapped. As developers churn through an organization, it becomes expensive for developers to spend time determining which software artifact must be modified to implement a requirement. In this work we created, developed, tested and evaluated a tool called Class Change Predictor, otherwise known as CCP, for predicting which class will implement a requirement. Understanding which class will implement a requirement supports several software engineering tasks such as refactoring and assigning requirements to developers. CCP is a data-mining tool operating on top of ITS and SCM repositories which gathers a unique combination of metrics. CCP leverages requirement text to compare current requirements to past requirements and requirements to source code files. CCP performs static analysis on the code-base of each major release of the software artifact. We evaluated CCP on different open source datasets (and the Digital Democracy dataset) by using several machine learning classifiers and pre-processing procedures. Our results show that we can achieve high precision on three out of four datasets. We conclude that accurate class change prediction is feasible, and we propose numerous solutions to increase future accuracy

A modular, open-source information extraction framework for identifying clinical concepts and processes of care in clinical narratives

In this thesis, a synthesis is presented of the knowledge models required by clinical informa- tion systems that provide decision support for longitudinal processes of care. Qualitative research techniques and thematic analysis are novelly applied to a systematic review of the literature on the challenges in implementing such systems, leading to the development of an original conceptual framework. The thesis demonstrates how these process-oriented systems make use of a knowledge base derived from workflow models and clinical guidelines, and argues that one of the major barriers to implementation is the need to extract explicit and implicit information from diverse resources in order to construct the knowledge base. Moreover, concepts in both the knowledge base and in the electronic health record (EHR) must be mapped to a common ontological model. However, the majority of clinical guideline information remains in text form, and much of the useful clinical information residing in the EHR resides in the free text fields of progress notes and laboratory reports. In this thesis, it is shown how natural language processing and information extraction techniques provide a means to identify and formalise the knowledge components required by the knowledge base. Original contributions are made in the development of lexico-syntactic patterns and the use of external domain knowledge resources to tackle a variety of information extraction tasks in the clinical domain, such as recognition of clinical concepts, events, temporal relations, term disambiguation and abbreviation expansion. Methods are developed for adapting existing tools and resources in the biomedical domain to the processing of clinical texts, and approaches to improving the scalability of these tools are proposed and evalu- ated. These tools and techniques are then combined in the creation of a novel approach to identifying processes of care in the clinical narrative. It is demonstrated that resolution of coreferential and anaphoric relations as narratively and temporally ordered chains provides a means to extract linked narrative events and processes of care from clinical notes. Coreference performance in discharge summaries and progress notes is largely dependent on correct identification of protagonist chains (patient, clinician, family relation), pronominal resolution, and string matching that takes account of experiencer, temporal, spatial, and anatomical context; whereas for laboratory reports additional, external domain knowledge is required. The types of external knowledge and their effects on system performance are identified and evaluated. Results are compared against existing systems for solving these tasks and are found to improve on them, or to approach the performance of recently reported, state-of-the- art systems. Software artefacts developed in this research have been made available as open-source components within the General Architecture for Text Engineering framework

Towards a Linked Semantic Web: Precisely, Comprehensively and Scalably Linking Heterogeneous Data in the Semantic Web

The amount of Semantic Web data is growing rapidly today. Individual users, academic institutions and businesses have already published and are continuing to publish their data in Semantic Web standards, such as RDF and OWL. Due to the decentralized nature of the Semantic Web, the same real world entity may be described in various data sources with different ontologies and assigned syntactically distinct identifiers. Furthermore, data published by each individual publisher may not be complete. This situation makes it difficult for end users to consume the available Semantic Web data effectively. In order to facilitate data utilization and consumption in the Semantic Web, without compromising the freedom of people to publish their data, one critical problem is to appropriately interlink such heterogeneous data. This interlinking process is sometimes referred to as Entity Coreference, i.e., finding which identifiers refer to the same real world entity. In the Semantic Web, the owl:sameAs predicate is used to link two equivalent (coreferent) ontology instances. An important question is where these owl:sameAs links come from. Although manual interlinking is possible on small scales, when dealing with large-scale datasets (e.g., millions of ontology instances), automated linking becomes necessary. This dissertation summarizes contributions to several aspects of entity coreference research in the Semantic Web. First of all, by developing the EPWNG algorithm, we advance the performance of the state-of-the-art by 1% to 4%. EPWNG finds coreferent ontology instances from different data sources by comparing every pair of instances and focuses on achieving high precision and recall by appropriately collecting and utilizing instance context information domain-independently. We further propose a sampling and utility function based context pruning technique, which provides a runtime speedup factor of 30 to 75. Furthermore, we develop an on-the-fly candidate selection algorithm, P-EPWNG, that enables the coreference process to run 2 to 18 times faster than the state-of-the-art on up to 1 million instances while only making a small sacrifice in the coreference F1-scores. This is achieved by utilizing the matching histories of the instances to prune instance pairs that are not likely to be coreferent. We also propose Offline, another candidate selection algorithm, that not only provides similar runtime speedup to P-EPWNG but also helps to achieve higher candidate selection and coreference F1-scores due to its more accurate filtering of true negatives. Different from P-EPWNG, Offline pre-selects candidate pairs by only comparing their partial context information that is selected in an unsupervised, automatic and domain-independent manner.In order to be able to handle really heterogeneous datasets, a mechanism for automatically determining predicate comparability is proposed. Combing this property matching approach with EPWNG and Offline, our system outperforms state-of-the-art algorithms on the 2012 Billion Triples Challenge dataset on up to 2 million instances for both coreference F1-score and runtime. An interesting project, where we apply the EPWNG algorithm for assisting cervical cancer screening, is discussed in detail. By applying our algorithm to a combination of different patient clinical test results and biographic information, we achieve higher accuracy compared to its ablations. We end this dissertation with the discussion of promising and challenging future work