Ontology-based data integration

About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.

Correspondence Patterns for Ontology Alignment

Abstract: We introduce in this paper correspondence patternsas templates to model ontology alignments. Correspondence patterns capture regularities recurring when aligning ontologies. They come in complement of ontology matching algorithms and graphical mapping tools, and facilitate the task of the engineer building the alignment between a pair of ontologies. We develop an ontology mediation framework based on three ontology correspondence abstraction levels. We particularly detail the most abstract level: correspondence patterns.

The Evaluation of Ontologies: Toward Improved Semantic Interoperability

Abstract: Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account representation of individual ontologies, performance and accuracy on tasks for which the ontology is designed and used, degree of alignment with other ontologies and their compatibility with automated reasoning. A sound and systematic approach to ontology evaluation is required to transform ontology engineering into a true scientific and engineering discipline. This chapter discusses issues and problems in ontology evaluation, describes some current strategies, and suggests some approaches that might be useful in the future.

Migrating integration processes among data integration platforms

Abstract: Methods and systems are provided for migrating a data integration facility, such as an ETL job, from a source data integration platform to a target data integration platform. For example, systems and methods are provided for migrating a data integration job from a source data integration platform having a source native format to a target data integration platform having a target native format; wherein the target native format is different than the source native format. The systems and methods may involve analyzing a source language construct of the source data integration platform to determine a logical syntax; constructing a target language construct of the target data integration platform adapted to perform the same logical operation on the target data integration platform as the source language construct performs on the source data integration platform; and substituting the target language construct for the source language construct in the source code for the data integration job.

Hierarchical, perceptron-like learning for ontology-based information extraction

Abstract: Recent work on ontology-based Information Extraction (IE) has tried to make use of knowledge from the target ontology in order to improve semantic annotation results. However, very few approaches exploit the ontology structure itself, and those that do so, have some limitations. This paper introduces a hierarchical learning approach for IE, which uses the target ontology as an essential part of the extraction process, by taking into account the relations between concepts. The approach is evaluated on the largest available semantically annotated corpus. The results demonstrate clearly the benefits of using knowledge from the ontology as input to the information extraction process. We also demonstrate the advantages of our approach over other state-of-the-art learning systems on a commonly used benchmark dataset.

Object Matching for Information Integration: A Profiler-Based Approach.

Abstract: Object matching is a fundamental problem that arises in numerous information integration scenarios. Virtually all existing solutions to this problem have assumed that the objects to be matched share the same set of attributes, and that they can be matched by comparing the similarities of the attributes. We consider the more general problem where the objects can also have disjoint attributes, such as matching tuples that come from relational tables with schemas (age,name) and (name,salary), respectively. We describe PROM, a solution that also exploits the disjoint attributes to improve matching accuracy. In the above example, PROM begins by matching any two given tuples based on the shared attribute name. Then it applies a set of profilers, each of which contains some knowledge about what constitutes a typical person. The profilers examine the tuple pair to see if it can plausibly make up a person. For example, a profiler may state that because the age is 9 and the salary is 200K, the tuples do not make up a person and thus do not match. Profilers can be manually specified by domain experts, learned from training data, transferred from other matching tasks, or constructed from external data. Thus, the PROM approach is distinguished in that it not only can exploit disjoint attributes to improve matching accuracy, but can also reuse knowledge from previous object matching tasks.