Ontology matching consists of finding correspondences between entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. Test cases can use ontologies of different nature (from simple directories to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation, consensus. OAEI-2010 builds over previous campaigns by having 4 tracks with 6 test cases followed by 15 participants. This year, the OAEI campaign introduces a new evaluation modality in association with the SEALS project. A subset of OAEI test cases is included in this new modality which provides more automation to the evaluation and more direct feedback to the participants. This paper is an overall presentation of the OAEI 2010 campaign.
In ontology alignment, there is no single best performing matching algorithm for every matching problem. Thus, most modern matching systems combine several base matchers and aggregate their results into a final alignment. This combination is often based on simple voting or averaging, or uses existing matching problems for learning a combination policy in a supervised setting. In this paper, we present the COMMAND matching system, an unsupervised method for combining base matchers, which uses anomaly detection to produce an alignment from the results delivered by several base matchers. The basic idea of our approach is that in a large set of potential mapping candidates, the scarce actual mappings should be visible as anomalies against the majority of non-mappings. The approach is evaluated on different OAEI datasets and shows a competitive performance with state-of-the-art systems.
This paper presents the results of Clona in the Ontology Alignment Evaluation Initiative campaign (OAEI) 2015. We only participated in Multifarm track, since Clona develops speci c techniques for aligning multilingual ontologies. We rst give an overview of our alignment system; then we detail the techniques used in our contribution to deal with cross-lingual ontology alignment. Last, we present the results with a thorough analysis and discussion, then we conclude by listing some future work on Clona. 1 Presentation of the system Multilingualism has become an issue of major interest for the Semantic Web community. This process has been accelerated due to a few initiatives which encourage all the active participants to make their data available to the public. Multilingualism is identi ed as one of the six challenges of the Semantic Web. Consequently, some solutions were proposed at the ontology level, annotation level and the interface level [1]. At the ontology level, the support should be conceived by the ontology designers to create knowledge representations in diverse natural languages. At the annotation level, tools should be developed to assist users in ontologies annotating independently of the natural languages adopted in their design and development. At the interface level, users should be able to have access to the information in natural languages of their own choice, without any linguistic restriction. The absence of the multilingual aspect coverage can be a real handicap during the information exchange in between various services o ered by the Semantic Web [2]. So, application elds are more and more numerous and they put in front very speci c di culties. Moreover, the multilingualism coverage allows the reasoning on the context intersections of various ontological representations. In this register, the issue of reasoning on overlapping context domains led to support multilingual information retrieval and digital content management. Multilingual
Ontology matching is the key challenge to achieve semantic interoperability in building the Semantic Web. We present an alternative probabilistic scheme, called GMap, which combines the sum-product network and the noisy-or model. More precisely, we employ the sum-product network to encode the similarities based on individuals and disjointness axioms across ontologies and calculate the contributions by the maximum a posterior inference. The noisy-or model is used to encode the probabilistic matching rules, which are independent of each other as well as the value calculated by the sum-product network. Experiments show that GMap is competitive with many OAEI top-ranked systems. Futhermore, GMap, benefited from these two graphical models, can keep inference tractable in the whole matching process.
The InsMT+ is an improved version of InsMT system participated at OAEI 2014. The InsMT+ an automatic instance matching system which con- sists in identifying the instances that describe the same real-world objects. The InsMT+ applies different string-based matchers with a local filter. This is the second participation of our system and we have improved somehow the results obtained by the previous version.
This paper presents the results of EXONA in the Ontology Alignment Evaluation Initiative (OAEI) 2015. EXONA is an automatic instance-based ontology alignment systems in which we parse ontology as rst step. In the second step, we index instances of the rst ontology. These indexed instances will be applied for the querying phase. In the last step, our system aligns instances based by aggregating score of di erent terminological matchers. We rst describe the overall framework of our matching System (EXONA) then we detail the techniques used in the framework for instance matching. Last, we give a thorough analysis on our results and discuss some future work on our system. It's our rst participation in the OAEI instance matching, the results are good in terms of recall, precision and F-measure. 1 Presentation of the system Ontology matching is a key interoperability enabler for the semantic web, as well as a useful tactic in data integration tasks. Knowledge about one object may be contained in multiple and di erent knowledge bases. Therefore, a lot of work has already been built to obtain more complete knowledge about things existing in di erent domains. This is in order to exceed the area of divergence obstacle, by creating cross-domain knowledge. Accordingly, it's strongly recommended to focus on the more active element of ontology which it called instance. Many instances matching approaches have been proposed, and among which is ours. In fact, our system is proposed for large scale instance matching. It operates on three successive modules, namely : transformation, indexation and correspondence. Transformation consists in transforming separately both of knowledge bases on an exploitable form and then creating our own instance object as a profession object. Indexation is the process of indexing instances of knowledge base; only instances of the source base knowledge
GMap is an alternative probabilistic scheme for ontology matching, which combines the sum-product network and the noisy-or model. More pre- cisely, we employ the sum-product network to encode the similarities based on individuals and disjointness axioms. The noisy-or model is utilized to encode the probabilistic matching rules, which describe the influences among entity pairs across ontologies. In this paper, we briefly introduce GMap and its results of four tracks (i.e.,Benchmark, Conference, Anatomy and Ontology Alignment for Query Answering) on OAEI 2015.
Entity resolution is the problem of finding co-referent instances, which at the same time describe the same topic. It is an important component of data integration systems and is indispensable in linked data publication process. Entity resolution has been a subject of extensive research; however, seeking for a perfect resolution algorithm remains a work in progress. Many approaches have been proposed for entity resolution. Among them, supervised entity resolution has been revealed as the most accurate approach [6, 2]. Meanwhile, configuration-based matching [2, 3, 5, 4] attracts most studies because of its advantages in scalability and interpretation. In order to match two instances of different repositories, configuration-based matching algorithms estimate the similarities between the values of the same attributes. After that, these similarities are aggregated into one matching score. This score is used to determine whether two instances are co-referent or not. The declarations of equivalent attributes, similarity measures, similarity aggregation, and acceptance threshold are specified by a matching configuration, which can be automatically optimized by a learning algorithm. Configuration learning using genetic algorithm has been a research topic of some studies [2, 5, 3]. The limitation of genetic algorithm is that it costs numerous iterations for reaching the convergence. We propose cLearn as a heuristic algorithm that is effective and more efficient. cLearn can be used to enhance the performance of any configuration-based entity resolution system.
AgreementMakerLight (AML) is an automated ontology matching system based primarily on element-level matching and on the use of external resources as background knowledge. This paper describes its configuration for the OAEI 2015 competition and discusses its results. For this OAEI edition, we focused mainly on the Interactive Matching track due to its expansion, as handling user interactions on large-scale tasks is a critical challenge in ontology matching. AML's participation in the OAEI 2015 was successful, as it obtained the highest F-measure in 6 of the 7 ontology matching tracks. Notably, it obtained the highest F-measure in all tasks of the Interactive Matching track while posing less queries to the user than comparable participating systems.
The aim of the research presented in the article is the mapping between the English Wikipedia categories and OpenCyc types. The mapping algorithm is heuristic and it takes into account structural similarities between the categories and the corresponding types. The achieved mapping precision ranges from 82 to 92 % (depending on the evaluation scheme), recall from 67 to 76%. The results of the algorithm and its code are available at http://cycloped.io.
Instance matching frameworks that identify links between instances, expressed as owl:sameAs assertions, have achieved a high performance while the performance of property matching lags behind. In this paper, we leverage owl:sameAs links and show how these links can help for property matching.
In this paper, we propose an approach for mapping properties in two RDF datasets between different languages, using a triplebased similarity propagation that can be adapted to find potential property matches. This approach does not need any language dependent information during the process, and thus can be applied to arbitrary languages without requiring translation.
Ontology matching techniques that are based on the analysis of names usually create first a set of matching hypotheses annotated with similarity weights followed by the extraction or selection of a set of correspondences. We propose to model this last step as an optimization problem. Our proposal differs fundamentally from other approaches since both logical and linguistic entities appear as first class citizens in the optimization problem. The extraction step will not only result in a set of correspondences but will also entail assumptions related to the meaning of the tokens that appeared in the involved labels. We discuss examples that illustrate the benefits of our approach and present a Markov Logic formalization. We conduct an experimental evaluation and present first results.
In this paper, we present ADOM, a dataset in Arabic language describing the conference domain. This dataset was created for two purposes (1) analysis of the behavior of matchers specially designed for Arabic language, (2) integration with the multifarm dataset of the Ontology Alignment Evaluation Initiative (OAEI). The multifarm track evaluates the ability of matching systems to deal with ontologies described in different natural languages. We have tested the ADOM dataset with the LogMap ontology matching system. The experiment shows that the ADOM dataset works correctly for the task of evaluating cross multilingual ontology alignment systems.
Enterprise architecture aligns business and information technology through the management of different elements and domains. Performing an integrated analysis of EA models using automated techniques is necessary when EA model representations grow in complexity, in order to support, for example, benchmarking of business processes or assessing compliance with requirements. Moreover, heterogeneity challenges arise from the frequent usage of multiple modelling languages, each based on a specific meta-model that cross-cuts distinct architectural domains. The motivation of this paper is, therefore, to investigate to what extent ontology matching techniques can be used as a means to improve the execution of automated analysis of EA model representations, based on the syntax, structure and semantic heterogeneities of these models. For that, we used AgreementMakerLight, an ontology matching system, to evaluate the matching of EA models based on the ArchiMate and BPMN languages.
We propose a semi-automatic ontology matching system using a hybrid active learning and online learning approach. Following the former paradigm, those mappings whose validation is estimated to lead to greater quality gain are selected for user validation, a process that occurs in each iteration, following the online learning paradigm. Experimental results demonstrate the effectiveness of our approach.
The interest of instance matching grows everyday with the emergence of linked data. This task is very necessary to interlink semantically data together in order to be reused and shared. In this paper, we introduce STRIM, an automatic instance matching tool designed to identify the instances that describe the same real-world objects. The STRIM system participates for the first time at OAEI 2015 in order to be evaluated and tested. The results of the STRIM system on instance matching tracks are so far quite promising. In effect, the STRIM system is the top system on SPIMBENCH tracks.
Extracting and analyzing the vast amount of structured tabular data available on the Web is a challenging task and has received a significant attention in the past few years. In this paper, we present the results of our analysis of the contents of a large corpus of over 90 million Web Tables through matching table contents with instances from a public cross-domain ontology such as DBpedia. The goal of this study is twofold. First, we examine how a large-scale matching of all table contents with a knowledge base can help us gain a better understanding of the corpus beyond what we gain from simple statistical measures such as distribution of table sizes and values. Second, we show how the results of our analysis are affected by the choice of the ontology and knowledge base. The ontologies studied include DBpedia Ontology, Schema.org, YAGO, Wikidata, and Freebase. Our results can provide a guideline for practitioners relying on these knowledge bases for data analysis.
State-of-the-art multilingual ontology matchers use machine translation to reduce the problem to the monolingual case. We investigate an alternative, self-contained solution based on semantic matching where labels are parsed by multilingual natural language processing and then matched using a language-independent knowledge base acting as an interlingua. As the method relies on the availability of domain vocabularies in the languages supported, matching and vocabulary enrichment become joint, mutually reinforcing tasks. In particular, we propose a vocabulary enrichment method that uses the matcher’s output to detect and generate missing items semi-automatically. Vocabularies developed in this manner can then be reused for other domain-specific natural language understanding tasks.
