A Large Scale Dataset for the Evaluation of Ontology Matching Systems

Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large scale matching tasks. In this paper we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two dozen of state of the art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state of the art ontology matching systems. The paper has been accepted for publication in "The Knowledge Engineering Review", Cambridge Universty Press (ISSN: 0269-8889, EISSN: 1469-8005)

S-Match: an algorithm and an implementation of semantic matching

We think of Match as an operator which takes two graph-like structures and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall

Discovering Missing Background Knowledge in Ontology Matching

Semantic matching determines the mappings between the nodes of two graphs (e.g., ontologies) by computing logical relations (e.g., subsumption) holding among the nodes that correspond semantically to each other. We present an approach to deal with the lack of background knowledge in matching tasks by using semantic matching iteratively. Unlike previous approaches, where the missing axioms are manually declared before the matching starts, we propose a fully automated solution. The benefits of our approach are: (i) saving some of the pre-match efforts, (ii) improving the quality of match via iterations, and (iii) enabling the future reuse of the newly discovered knowledge. We evaluate the implemented system on large real-world test cases, thus, proving empirically the benefits of our approach

Introduction to the Ontology Alignment Evaluation 2005

euzenat2005dInternational audienceNo abstract available

Design of the second evaluation campaign

wrigley2011aThis deliverable is concerned with the implementation of the second evaluation campaign based upon the methodology and design recommendations made in SEALS Deliverable D3.1. This deliverable covers the initial preparation of the second SEALS Evaluation Campaign and describes the tasks that have been performed during the Initiation and Involvement phases. Furthermore, the deliverable describes the steps to be taken over the next few months and the actors who are responsible for those steps

Results of the Ontology Alignment Evaluation Initiative 2007

euzenat2007gInternational audienceWe present the Ontology Alignment Evaluation Initiative 2007 campaign as well as its results. The OAEI campaign aims at comparing ontology matching systems on precisely defined test sets. OAEI-2007 builds over previous campaigns by having 4 tracks with 7 test sets followed by 17 participants. This is a major increase in the number of participants compared to the previous years. Also, the evaluation results demonstrate that more participants are at the forefront. The final and official results of the campaign are those published on the OAEI web site

Description of alignment evaluation and benchmarking results

shvaiko2007aNo abstract available

Description of alignment implementation and benchmarking results

stuckenschmidt2005aThis deliverable presents the evaluation campaign carried out in 2005 and the improvement participants to these campaign and others have to their systems. We draw lessons from this work and proposes improvements for future campaigns

Preliminary Evaluation of Schema Matching Systems

This evaluation of the state-of-the-art schema matching approaches is based on a comprehensive testing of modern systems on various real-world schemas. The results obtained show that there is no matcher that performs best on all types of schemas used. The quality of mappings depends significantly on the approaches to schema matching used and on the tuning of the matcher to the particular schema type. The approach proposed in COMA that combines results of different schema matchers proved its effectiveness in our evaluation

Schema-based Semantic Matching: Algorithms, a System and a Testing Methodology

Schema/ontology/classification matching is a critical problem in many application domains, such as, schema/ontology/classification integration, data warehouses, e-commerce, web services coordination, Semantic Web, semantic query processing, etc. We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. Semantic matching is a novel approach where semantic correspondences are discovered by computing and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. At present, the semantic matching approach is limited to the case of tree-like structures e.g., classifications, taxonomies, etc. The main focus of this PhD thesis, supervised by Prof. Fausto Giunchiglia is the development of the schema-based algorithm for semantic matching of tree-like structures; the development of the semantic matching system implementing the algorithm; and the development of the testing methodology allowing for a comprehensive evaluation of the semantic matching systems