COMA — A system for flexible combination of schema matching approaches

Yatskevich

Lecture Notes in Computer Science

2005

Abstract. We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. We concentrate on classifications with tree structures. In s emantic matching, correspondences are discovered by translating the natural language labels of nodes into propositional formulas, and by codifying matching into a propositional unsatisfiability problem. We distinguish between problems with conjunctive formulas and problems with disjunctive formulas, and present various optimizations. For instance, we propose a linear time algorithm which solves the first class of problems. According to the tests we have done so far, the optimizations substantially improve the time performance of the system.

Section: Introductionmentioning

confidence: 91%

Efficient Semantic Matching

Yatskevich

Lecture Notes in Computer Science

2005

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2016

“…Since our approach focuses on the similarity between RDF Schema elements, we compare our method to similar works such as Leme et al [17], Do et al [18], and Algergawy et al [12]. The precision, recall and F-measure values among R2Sim and related work are presented in Fig.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Yan et al [8] and Kling et al [9] extend the distance-based method to find the similarity between XML elements for querying purpose. Do et al [18] compute the name similarity between elements of two XML Schemas. Yang et al [19] use linguistic taxonomy based on entity definitions in WordNet [10] to gain the most accurate semantics for the element names.…”

Section: Related Workmentioning

confidence: 99%

“…We evaluate the proposed measures in the context of matching two RDF Schemas to determine the number of matches between them and then compare with other approaches. The criteria for evaluating the quality of matching system are precision and recall, which originate from information retrieval [22] and were adapted to ontology matching [18].…”

Section: Data Set and Setupmentioning

confidence: 99%

See 1 more Smart Citation

An Improvement in Measuring the Semantic Similarity Between RDF Ontologies

Thúy¹,

Tien²

2017

Abstract-RDF (Resource Description Framework) ontologies has been playing an important role for many knowledge applications because they support a source of precisely defined terms. However, the wide-spread of RDF ontologies creates a demand for automatic way of assessing their similarity. In this paper, we present a novel method to measure the semantic similarity between elements in different RDF ontologies. This measure is designed so as to enable extraction of information encoded in RDF element descriptions and to take into account the element relationships with its ancestors and children. We evaluate the proposed measures in the context of matching two RDF ontologies to determine the number of matches between them and then compare with human estimation and the related methods. The experimental results show that our similarity values are better than other approaches with regard to the accuracy of semantics and structure similarities.

Artificial Intelligence in Medicine

“…For example, the Cupid algorithm [7] uses a first phase that computes a linguistic match and then a second phase to incorporate structural information. The COMA system offers a platform where matching techniques can be flexibly combined in different ways [8].…”

Section: Approaches To Aligning Ontologiesmentioning

confidence: 99%

Comparing two approaches for aligning representations of anatomy

Zhang

Mork

Bodenreider

et al. 2007

Objective-To analyze the comparison, through their results, of two distinct approaches applied to aligning two representations of anatomy.Materials-Both approaches use a combination of lexical and structural techniques. In addition, the first approach takes advantage of domain knowledge, while the second approach treats alignment as a special case of schema matching. The same versions of FMA and GALEN were aligned by each approach. 2199 concept matches were obtained by both approaches.Methods and results-For matches identified by one approach only (337 and 336 respectively), we analyzed the reasons that caused the other approach to fail. Conclusions-The first approach could be improved by addressing partial lexical matches and identifying matches based solely on structural similarity. The second approach may be improved by taking into account synonyms in FMA and identifying semantic mismatches. However, only 33% of the possible one-to-one matches among anatomical concepts were identified by the two approaches together. New directions need to be explored in order to handle more complex matches.