Comparison of ontology alignment algorithms across single matching task via the McNemar test
نویسندگان
چکیده
Ontology alignment is widely used to nd the correspondences between dierent ontologies in diverse elds. Aer discovering the alignment by methods, several performance scores are available to evaluate them. e scores require the produced alignment by amethod and the reference alignment containing the underlying actual correspondences of the given ontologies. e current trend in alignment evaluation is to put forward a new score and to compare various alignments by juxtaposing their performance scores. However, it is substantially provocative to select one performance score among others for comparison. On top of that, claiming if one method has a beer performance than one another can not be substantiated by solely comparing the scores. In this paper, we propose the statistical procedures which enable us to theoretically favor one method over one another. e McNemar test is considered as a reliable and suitable means for comparing two ontology alignment methods over one matching task. e test applies to a 2 × 2 contingency table which can be constructed in two dierent ways based on the alignments, each of which has their own merits/pitfalls. e ways of the contingency table construction and various apposite statistics from the McNemar test are elaborated in minute detail. In the case of having more than two alignment methods for comparison, the family-wise error rate is expected to happen. us, the ways of preventing such an error are also discussed. A directed graph visualizes the outcome of the McNemar test in the presence of multiple alignment methods. From this graph, it is readily understood if one method is beer than one another or if their dierences are imperceptible. Our investigation on the methods participated in the anatomy track of OAEI 2016 demonstrates that AML and CroMatcher are the top two methods and DKP-AOM and Alin are the boom two ones. Moreover, the Levenstein and N-gram string-based distances discover the most correspondences while SMOA and Hamming distance are the ones with the least found correspondences.
منابع مشابه
Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems
Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory con...
متن کاملImage alignment via kernelized feature learning
Machine learning is an application of artificial intelligence that is able to automatically learn and improve from experience without being explicitly programmed. The primary assumption for most of the machine learning algorithms is that the training set (source domain) and the test set (target domain) follow from the same probability distribution. However, in most of the real-world application...
متن کاملImage Classification via Sparse Representation and Subspace Alignment
Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...
متن کاملOntology Correspondence via Theory Interpretation
We report on ongoing work to apply techniques of automated theory morphism search in first-order logic to ontology matching and alignment problems. Such techniques are able to discover ‘structural similarities’ across different ontologies by providing theory interpretations of one ontology into another. We sketch the techniques currently available for automating the task of finding theory inter...
متن کاملComparing Similarity Combination Methods for Schema Matching
A recurring manual task in data integration or ontology alignment is finding mappings between complex schemas. In order to reduce the manual effort, many matching algorithms for semi-automatically computing mappings were introduced. In the last decade it turned out that a combination of matching algorithms often improves mapping quality. Many possible combination methods can be found in literat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1704.00045 شماره
صفحات -
تاریخ انتشار 2017