In the humanities and social sciences, bibliometric methods for the assessment of research performance are (so far) less common. This study uses a concrete example in an attempt to evaluate a research institute from the area of social sciences and humanities with the help of data from G oogle S cholar ( GS ). In order to use GS for a bibliometric study, we developed procedures for the normalization of citation impact, building on the procedures of classical bibliometrics. In order to test the convergent validity of the normalized citation impact scores, we calculated normalized scores for a subset of the publications based on data from the Web of Science ( WoS) and S copus. Even if scores calculated with the help of GS and the WoS / S copus are not identical for the different publication types (considered here), they are so similar that they result in the same assessment of the institute investigated in this study: For example, the institute's papers whose journals are covered in the WoS are cited at about an average rate (compared with the other papers in the journals).

Product matching is a challenging variation of entity resolution to identify representations and offers referring to the same product. Product matching is highly difficult due to the broad spectrum of products, many similar but different products, frequently missing or wrong values, and the textual nature of product titles and descriptions. We propose the use of tailored approaches for product matching based on a preprocessing of product offers to extract and clean new attributes usable for matching. In particular, we propose a new approach to extract and use so-called product codes to identify products and distinguish them from similar product variations. We evaluate the effectiveness of the proposed approaches with challenging real-life datasets with product offers from online shops. We also show that the UPC information in product offers is often error-prone and can lead to insufficient match decisions.

Programmatic data integration approaches such as mashups have become a viable approach to dynamically integrate web data at runtime. Key data sources for mashups include entity search engines and hidden databases that need to be queried via source-specific search interfaces or web forms. Current mashups are typically restricted to simple query approaches such as using keyword search. Such approaches may need a high number of queries if many objects have to be found. Furthermore, the effectiveness of the queries may be limited, i.e., they may miss relevant results. We therefore propose more advanced search strategies that aim at finding a set of entities with high efficiency and high effectiveness. Our strategies use different kinds of queries that are determined by source-specific query generators. Furthermore, the queries are selected based on the characteristics of input entities. We introduce a flexible model for entity search strategies that includes a ranking of candidate queries determined by different query generators. We describe different query generators and outline their use within four entity search strategies. These strategies apply different query ranking and selection approaches to optimize efficiency and effectiveness. We evaluate our search strategies in detail for two domains: product search and publication search. The comparison with a standard keyword search shows that the proposed search strategies provide significant improvements in both domains.

The effectiveness and scalability of MapReduce-based implementations of complex data-intensive tasks depend on an even redistribution of data between map and reduce tasks. In the presence of skewed data, sophisticated redistribution approaches thus become necessary to achieve load balancing among all reduce tasks to be executed in parallel. For the complex problem of entity resolution, we propose and evaluate two approaches for such skew handling and load balancing. The approaches support blocking techniques to reduce the search space of entity resolution, utilize a preprocessing MapReduce job to analyze the data distribution, and distribute the entities of large blocks among multiple reduce tasks. The evaluation on a real cloud infrastructure shows the value and effectiveness of the proposed load balancing approaches.

Annotation graph datasets are a natural representation of scientific knowledge. They are common in the life sciences and health sciences, where concepts such as genes, proteins or clinical trials are annotated with controlled vocabulary terms from ontologies. We present a tool, PAnG (Patterns in Annotation Graphs), that is based on a complementary methodology of graph summarization and dense subgraphs. The elements of a graph summary correspond to a pattern and its visualization can provide an explanation of the underlying knowledge. Scientists can use PAnG to develop hypotheses and for exploration.

We demonstrate a new powerful mashup tool called WETSUIT (Web EnTity Search and fUsIon Tool) to search and integrate web data from diverse sources and domain-specific entity search engines. WETSUIT supports adaptive search strategies to query sets of relevant entities with a minimum of communication overhead. Mashups can be composed using a set of high-level operators based on the Java-compatible language Scala. The operator implementation supports a high degree of parallel processing, in particular a streaming of entities between all data transformation operations facilitating a fast presentation of intermediate results. WETSUIT has already been applied to solve challenging integration tasks from different domains.

We demonstrate a powerful and easy-to-use tool called Dedoop (Deduplication with Hadoop) for MapReduce-based entity resolution (ER) of large datasets. Dedoop supports a browser-based specification of complex ER workflows including blocking and matching steps as well as the optional use of machine learning for the automatic generation of match classifiers. Specified workflows are automatically translated into MapReduce jobs for parallel execution on different Hadoop clusters. To achieve high performance Dedoop supports several advanced load balancing strategies.

The effectiveness and scalability of MapReduce-based implementations of complex data-intensive tasks depend on an even redistribution of data between map and reduce tasks. In the presence of skewed data, sophisticated redistribution approaches thus become necessary to achieve load balancing among all reduce tasks to be executed in parallel. For the complex problem of entity resolution with blocking, we propose BlockSplit, a load balancing approach that supports blocking techniques to reduce the search space of entity resolution. The evaluation on a real cloud infrastructure shows the value and effectiveness of the proposed approach.

Despite the huge amount of recent research efforts on entity resolution (matching) there has not yet been a comparative evaluation on the relative effectiveness and efficiency of alternate approaches. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from online shops are not sufficiently solved with conventional approaches based on the similarity of attribute values.

We present FEVER, a new evaluation platform for entity resolution approaches. The modular structure of the FEVER framework supports the incorporation or reconstruction of many previously proposed approaches for entity resolution. A distinctive feature of FEVER is that it not only evaluates traditional measures such as precision and recall but also the effort for configuring (e.g., parameter tuning, training) a good entity resolution approach. FEVER thus strives for a fair comparative evaluation of different approaches by considering both the effectiveness and configuration effort.