Unsupervised learning of link discovery configuration

Discovering links between overlapping datasets on the Web is generally realised through the use of fuzzy similarity measures. Configuring such measures is often a non-trivial task that depends on the domain, ontological schemas, and formatting conventions in data. Existing solutions either rely on the user's knowledge of the data and the domain or on the use of machine learning to discover these parameters based on training data. In this paper, we present a novel approach to tackle the issue of data linking which relies on the unsupervised discovery of the required similarity parameters. Instead of using labeled data, the method takes into account several desired properties which the distribution of output similarity values should satisfy. The method includes these features into a fitness criterion used in a genetic algorithm to establish similarity parameters that maximise the quality of the resulting linkset according to the considered properties. We show in experiments using benchmarks as well as real-world datasets that such an unsupervised method can reach the same levels of performance as manually engineered methods, and how the different parameters of the genetic algorithm and the fitness criterion affect the results for different datasets

Crowdsourcing and Scholarly Culture: Understanding Expertise in an Age of Popularism

The increasing volume of digital material available to the humanities creates clear potential for crowdsourcing. However, tasks in the digital humanities typically do not satisfy the standard requirement for decomposition into microtasks each of which must require little expertise on behalf of the worker and little context of the broader task. Instead, humanities tasks require scholarly knowledge to perform and even where sub-tasks can be extracted, these often involve broader context of the document or corpus from which they are extracted. That is the tasks are macrotasks, resisting simple decomposition. Building on a case study from musicology, the In Concert project, we will explore both the barriers to crowdsourcing in the creation of digital corpora and also examples where elements of automatic processing or less-expert work are possible in a broader matrix that also includes expert microtasks and macrotasks. Crucially we will see that the macrotask–microtask distinction is nuanced: it is often possible to create a partial decomposition into less-expert microtasks with residual expert macrotasks, and crucially do this in ways that preserve scholarly values

Crowdsourcing and Scholarly Culture: Understanding Expertise in an Age of Popularism

The increasing volume of digital material available to the humanities creates clear potential for crowdsourcing. However, tasks in the digital humanities typically do not satisfy the standard requirement for decomposition into microtasks each of which must require little expertise on behalf of the worker and little context of the broader task. Instead, humanities tasks require scholarly knowledge to perform and even where sub-tasks can be extracted, these often involve broader context of the document or corpus from which they are extracted. That is the tasks are macrotasks, resisting simple decomposition. Building on a case study from musicology, the In Concert project, we will explore both the barriers to crowdsourcing in the creation of digital corpora and also examples where elements of automatic processing or less-expert work are possible in a broader matrix that also includes expert microtasks and macrotasks. Crucially we will see that the macrotask–microtask distinction is nuanced: it is often possible to create a partial decomposition into less-expert microtasks with residual expert macrotasks, and crucially do this in ways that preserve scholarly values

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

Active learning of link specifications using decision tree learning

In this work we presented an implementation that uses decision trees to learn highly accurate link specifications. We compared our approach with three state-of-the-art classifiers on nine datasets and showed, that our approach gives comparable results in a reasonable amount of time. It was also shown, that we outperform the state-of-the-art on four datasets by up to 30%, but are still behind slightly on average. The effect of user feedback on the active learning variant was inspected pertaining to the number of iterations needed to deliver good results. It was shown that we can get FScores above 0.8 with most datasets after 14 iterations

Scalable Data Integration for Linked Data

Linked Data describes an extensive set of structured but heterogeneous datasources where entities are connected by formal semantic descriptions. In thevision of the Semantic Web, these semantic links are extended towards theWorld Wide Web to provide as much machine-readable data as possible forsearch queries. The resulting connections allow an automatic evaluation to findnew insights into the data. Identifying these semantic connections betweentwo data sources with automatic approaches is called link discovery. We derivecommon requirements and a generic link discovery workflow based on similaritiesbetween entity properties and associated properties of ontology concepts. Mostof the existing link discovery approaches disregard the fact that in times ofBig Data, an increasing volume of data sources poses new demands on linkdiscovery. In particular, the problem of complex and time-consuming linkdetermination escalates with an increasing number of intersecting data sources.To overcome the restriction of pairwise linking of entities, holistic clusteringapproaches are needed to link equivalent entities of multiple data sources toconstruct integrated knowledge bases. In this context, the focus on efficiencyand scalability is essential. For example, reusing existing links or backgroundinformation can help to avoid redundant calculations. However, when dealingwith multiple data sources, additional data quality problems must also be dealtwith. This dissertation addresses these comprehensive challenges by designingholistic linking and clustering approaches that enable reuse of existing links.Unlike previous systems, we execute the complete data integration workflowvia a distributed processing system. At first, the LinkLion portal will beintroduced to provide existing links for new applications. These links act asa basis for a physical data integration process to create a unified representationfor equivalent entities from many data sources. We then propose a holisticclustering approach to form consolidated clusters for same real-world entitiesfrom many different sources. At the same time, we exploit the semantic typeof entities to improve the quality of the result. The process identifies errorsin existing links and can find numerous additional links. Additionally, theentity clustering has to react to the high dynamics of the data. In particular,this requires scalable approaches for continuously growing data sources withmany entities as well as additional new sources. Previous entity clusteringapproaches are mostly static, focusing on the one-time linking and clustering ofentities from few sources. Therefore, we propose and evaluate new approaches for incremental entity clustering that supports the continuous addition of newentities and data sources. To cope with the ever-increasing number of LinkedData sources, efficient and scalable methods based on distributed processingsystems are required. Thus we propose distributed holistic approaches to linkmany data sources based on a clustering of entities that represent the samereal-world object. The implementation is realized on Apache Flink. In contrastto previous approaches, we utilize efficiency-enhancing optimizations for bothdistributed static and dynamic clustering. An extensive comparative evaluationof the proposed approaches with various distributed clustering strategies showshigh effectiveness for datasets from multiple domains as well as scalability on amulti-machine Apache Flink cluster