Results 1 - 10
of
103
Efficient semantic matching
, 2004
"... 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 semantic matching, correspondences are discovered by translating the natural language labels of nodes into prop ..."
Abstract
-
Cited by 855 (68 self)
- Add to MetaCart
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 semantic 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.
Rimom: A dynamic multistrategy ontology alignment framework
- IEEE Trans. on Knowl. and Data Eng
"... Abstract—Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dy ..."
Abstract
-
Cited by 100 (10 self)
- Add to MetaCart
(Show Context)
Abstract—Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely. We propose a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and propose a strategy selection method to automatically combine the matching strategies based on two estimated factors. In the approach, we consider both textual and structural characteristics of ontologies. With RiMOM, we participated in the 2006 and 2007 campaigns of the Ontology Alignment Evaluation Initiative (OAEI). Our system is among the top three performers in benchmark data sets. Index Terms—Heterogeneous databases, knowledge and data engineering tools and techniques, ontology languages. Ç 1
Ten challenges for ontology matching
, 2008
"... This paper aims at analyzing the key trends and challenges of the ontology matching field. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which is robust enough ..."
Abstract
-
Cited by 76 (3 self)
- Add to MetaCart
(Show Context)
This paper aims at analyzing the key trends and challenges of the ontology matching field. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which is robust enough to be the basis for future development, and which is usable by non expert users. In this paper we first provide the basics of ontology matching with the help of examples. Then, we present general trends of the field and discuss ten challenges for ontology matching, thereby aiming to direct research into the critical path and to facilitate progress of the field.
Ontology change: classification and survey
, 2007
"... Ontologies play a key role in the advent of the Semantic Web. An important problem when dealing with ontologies is the modification of an existing ontology in response to a certain need for change. This problem is a complex and multifaceted one, because it can take several different forms and includ ..."
Abstract
-
Cited by 51 (5 self)
- Add to MetaCart
Ontologies play a key role in the advent of the Semantic Web. An important problem when dealing with ontologies is the modification of an existing ontology in response to a certain need for change. This problem is a complex and multifaceted one, because it can take several different forms and includes several related subproblems, like heterogeneity resolution or keeping track of ontology versions. As a result, it is being addressed by several different, but closely related and often overlapping research disciplines. Unfortunately, the boundaries of each such discipline are not clear, as the same term is often used with different meanings in the relevant literature, creating a certain amount of confusion. The purpose of this paper is to identify the exact relationships between these research areas and to determine the boundaries of each field, by performing a broad review of the relevant literature.
Formal Model for Ontology Mapping Creation
- In: International Semantic Web Conference
, 2006
"... Abstract. In a semantic environment data is described by ontologies and heterogeneity problems have to be solved at the ontological level. This means that alignments between ontologies have to be created, most probably during design-time, and used in various run-time processes. Such alignments descr ..."
Abstract
-
Cited by 23 (6 self)
- Add to MetaCart
(Show Context)
Abstract. In a semantic environment data is described by ontologies and heterogeneity problems have to be solved at the ontological level. This means that alignments between ontologies have to be created, most probably during design-time, and used in various run-time processes. Such alignments describe a set of mappings between the source and target ontologies, where the mappings show how instance data from one ontology can be expressed in terms of another ontology. We propose a formal model for mapping creation. Starting from this model we explore how such a model maps onto a design-time graphical tool that can be used in creating alignments between ontologies. We also investigate how such a model helps in expressing the mappings in a logical language, based on the semantic relationships identified using the graphical tool. 1
H.: A study in empirical and casuistic analysis of ontology mapping results
- In: Proc. ESWC
, 2007
"... Abstract. Many ontology mapping systems nowadays exist. In order to evaluate their strengths and weaknesses, benchmark datasets (ontology collections) have been created, several of which have been used in the most recent edition of the Ontology Alignment Evaluation Initiative (OAEI). While most OAEI ..."
Abstract
-
Cited by 21 (7 self)
- Add to MetaCart
(Show Context)
Abstract. Many ontology mapping systems nowadays exist. In order to evaluate their strengths and weaknesses, benchmark datasets (ontology collections) have been created, several of which have been used in the most recent edition of the Ontology Alignment Evaluation Initiative (OAEI). While most OAEI tracks rely on straightforward comparison of the results achieved by the mapping systems with some kind of reference mapping created a priori, the ’conference ’ track (based on the OntoFarm collection of heterogeneous ’conference organisation’ ontologies) instead encompassed multiway manual as well as automated analysis of mapping results themselves, with ‘correct ’ and ‘incorrect ’ cases determined a posteriori. The manual analysis consisted in simple labelling of discovered mappings plus discussion of selected cases (‘casuistics’) within a face-to-face consensus building workshop. The automated analysis relied on two different tools: the DRAGO system for testing the consistency of aligned ontologies and the LISp-Miner system for discovering frequent associations in mapping meta-data including the phenomenon of graph-based mapping patterns. The results potentially provide specific feedback to the developers and users of mining tools, and generally indicate that automated mapping can rarely be successful without considering the larger context and possibly deeper semantics of the entities involved. 1
Improving ontology matching using meta-level learning
- in Proc. 6th European Semantic Web Conference (ESWC), 2009
"... Abstract. Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-off precision and recall and have their specific strengths and weaknesses. This leads to problems when the right ..."
Abstract
-
Cited by 20 (1 self)
- Add to MetaCart
(Show Context)
Abstract. Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-off precision and recall and have their specific strengths and weaknesses. This leads to problems when the right matcher for a given task has to be selected. In this paper, we present a method for improv-ing matching results by not choosing a specific matcher but applying machine learning techniques on an ensemble of matchers. Hereby we learn rules for the correctness of a correspondence based on the output of different matchers and additional information about the nature of the elements to be matched, thus lever-aging the weaknesses of an individual matcher. We show that our method always performs significantly better than the median of the matchers used and in most cases outperforms the best matcher with an optimal threshold for a given pair of ontologies. As a side product of our experiments, we discovered that the major-ity vote is a simple but powerful heuristic for combining matchers that almost reaches the quality of our learning results. 1
A flexible approach for planning schema matching algorithms
- In OTM Conferences
, 2008
"... Abstract. Most of the schema matching tools are assembled from multiple match algorithms, each employing a particular technique to improve matching accuracy and making matching systems extensible and customizable to a particular domain. Recently, it has been pointed out that the main issue is how to ..."
Abstract
-
Cited by 19 (6 self)
- Add to MetaCart
(Show Context)
Abstract. Most of the schema matching tools are assembled from multiple match algorithms, each employing a particular technique to improve matching accuracy and making matching systems extensible and customizable to a particular domain. Recently, it has been pointed out that the main issue is how to select the most suitable match algorithms to execute for a given domain and how to adjust the multiple knobs (e.g. threshold, performance, quality, etc.). The solutions provided by current schema matching tools consist in aggregating the results obtained by several match algorithms to improve the quality of the discovered matches. However, aggregation entails several drawbacks. In this article, we present a novel method for combining schema matching algorithms. The matching engine makes use of a decision tree to combine the most appropriate match algorithms. As a first consequence of using the decision tree, the performance of the system is improved since the complexity is bounded by the height of the decision tree. Thus, only a subset of these match algorithms is used during the matching process. The second advantage is the improvement of the quality of matches. Indeed, for a given domain, only the most suitable match algorithms are used. The experiments show the effectiveness of our approach w.r.t. other matching tools. 1
A large scale dataset for the evaluation of ontology matching systems
- The Knowledge Engineering Review Journal
, 2008
"... 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 ..."
Abstract
-
Cited by 18 (6 self)
- Add to MetaCart
(Show Context)
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. 1
