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ROCKIT is a maximum a-posteriori (MAP) query engine for statistical relational models. MAP inference in graphical models is an optimization problem which can be compiled to integer linear programs (ILPs). We describe several advances in translating MAP queries to ILP instances and present the novel meta-algorithm cutting plane aggregation (CPA). CPA(More)
In this paper, we describe our probabilistic-logical alignment system CODI (Combinatorial Optimization for Data Integration). The system provides a declarative framework for the alignment of individuals, concepts, and properties of two heterogeneous ontologies. CODI leverages both logical schema information and lexical similarity measures with a(More)
Existing modeling languages lack the expressiveness or efficiency to support many modern and successful machine learning (ML) models such as structured prediction or matrix factorization. We present WOLFE, a probabilistic programming language that enables practitioners to develop such models. Most ML approaches can be formulated in terms of scalar(More)
It has been argued that linked open data is the major benefit of semantic technologies for the web as it provides a huge amount of structured data that can be accessed in a more effective way than web pages. While linked open data avoids many problems connected with the use of expressive ontologies such as the knowledge acquisition bottleneck , data(More)
The evaluation of matching applications is becoming a major issue in the semantic web and it requires a suitable methodological approach as well as appropriate benchmarks. In particular, in order to evaluate a matching application under different experimental conditions, it is crucial to provide a test dataset characterized by a controlled variety of(More)
The integration of both distributed schemas and data repositories is a major challenge in data and knowledge management applications. Instances of this problem range from mapping database schemas to object reconciliation in the linked open data cloud. We present a novel approach to several important data integration problems that combines logical and(More)
Nowadays, the availability of large collections of data requires techniques and tools capable of linking data together, by retrieving potentially useful relations among them and helping in associating together data representing same or similar real objects. One of the main problems in developing data linking techniques and tools is to understand the quality(More)
In this paper, we show how to model the matching problem as a problem of joint inference. In opposite to existing approaches , we distinguish between the layer of labels and the layer of concepts and properties. Entities from both layers appear as first class citizens in our model. We present an example and explain the benefits of our approach. Moreover, we(More)