Results 1 -
8 of
8
AgreementMakerLight Results for OAEI 2013
"... Abstract. AgreementMakerLight (AML) is an automated ontology matching framework based on element-level matching and the use of external resources as background knowledge. This paper describes the configuration of AML for the OAEI 2013 competition and discusses its results. Being a newly developed an ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
(Show Context)
Abstract. AgreementMakerLight (AML) is an automated ontology matching framework based on element-level matching and the use of external resources as background knowledge. This paper describes the configuration of AML for the OAEI 2013 competition and discusses its results. Being a newly developed and still incomplete system, our focus in this year’s OAEI were the anatomy and large biomedical ontologies tracks, wherein background knowledge plays a critical role. Nevertheless, AML was fairly successful in other tracks as well, showing that in many ontology matching tasks, a lightweight approach based solely on element-level matching can compete with more complex approaches. 1 Presentation of the system 1.1 State, purpose, general statement AgreementMakerLight (AML) is an automated ontology matching framework derived from the AgreementMaker system [2, 4]. It was developed with the main goal of tackling very large ontology matching problems such as those in the life science domain,
User Involvement for Large-Scale Ontology Alignment
"... Abstract. Currently one of the challenges for the ontology alignment commu-nity is the user involvement in the alignment process. At the same time, the focus of the community has shifted towards large-scale matching which introduces an additional dimension to this issue. This paper aims to provide a ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Abstract. Currently one of the challenges for the ontology alignment commu-nity is the user involvement in the alignment process. At the same time, the focus of the community has shifted towards large-scale matching which introduces an additional dimension to this issue. This paper aims to provide a set of require-ments that foster the user involvement for large-scale ontology alignment tasks and a state of the art overview. 1
Pay-As-You-Go Multi-User Feedback Model for Ontology Matching
"... Abstract. Using our multi-user model, a community of users provides feedback in a pay-as-you-go fashion to the ontology matching process by validating the mappings found by automatic methods, with the following advantages over having a single user: the e↵ort required from each user is reduced, user ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
Abstract. Using our multi-user model, a community of users provides feedback in a pay-as-you-go fashion to the ontology matching process by validating the mappings found by automatic methods, with the following advantages over having a single user: the e↵ort required from each user is reduced, user errors are corrected, and consensus is reached. We propose strategies that dynamically determine the order in which the candidate mappings are presented to the users for validation. These strategies are based on mapping quality measures that we define. Further, we use a propagation method to leverage the validation of one mapping to other mappings. We use an extension of the AgreementMaker ontology match-ing system and the Ontology Alignment Evaluation Initiative (OAEI) Benchmarks track to evaluate our approach. Our results show how F-measure and robustness vary as a function of the number of user valida-tions. We consider di↵erent user error and revalidation rates (the latter measures the number of times that the same mapping is validated). Our results highlight complex trade-o↵s and point to the benefits of dynam-ically adjusting the revalidation rate. 1
Visual Analytics for Ontology Matching Using Multi-Linked Views
"... Abstract. Ontology matching is the key to data integration on the Semantic Web. Advanced ontology matching systems incorporate a variety of algorithms. However, they do not always guarantee a complete and correct alignment (set of mappings). Hence, user involvement in the matching process is essent ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract. Ontology matching is the key to data integration on the Semantic Web. Advanced ontology matching systems incorporate a variety of algorithms. However, they do not always guarantee a complete and correct alignment (set of mappings). Hence, user involvement in the matching process is essential for complex ontologies. In this paper, we explore the power of multi-linked views, where actions in one view affect the display of the other views, thereby extending significantly the state of the art in ontology matching visualization in general and that of visual analytics for ontology matching in particular. A preliminary assessment of our approach that uses the ontologies of the OAEI Conference Track points to the effectiveness of our approach.
New Ways of Mapping Knowledge Organization Systems. Using a SemiAutomatic MatchingProcedure for Building Up Vocabulary Crosswalks
"... Abstract: Crosswalks between different vocabularies are an indispensable prerequisite for integrated and highquality search scenarios in distributed data environments. Offered through the web and linked with each other they act as a central link so that users could move back and forth between differ ..."
Abstract
- Add to MetaCart
Abstract: Crosswalks between different vocabularies are an indispensable prerequisite for integrated and highquality search scenarios in distributed data environments. Offered through the web and linked with each other they act as a central link so that users could move back and forth between different data sources being online available. In the past, crosswalks between different thesauri have been primarily developed manually. In the long run the intellectual updating of such crosswalks requires huge personnel expenses. Therefore, an integration of automatic matching procedures, as for example Ontology Matching Tools, seems pretty obvious. On the basis of computergenerated correspondences between the Thesaurus for Economics (STW) and the Thesaurus for the Social Sciences (TheSoz) our contribution will explore crossborder approaches between ITassisted tools and procedures on the one hand and external quality measurements via domain experts on the other hand. Thus, we will present techniques to semiautomatically perform vocabulary crosswalks. Due to intellectually evaluated results of multiple matching tools in the forerun, quality statements concerning the reliability of further computergenerated crosswalks can be made. This way, the application of various tools and procedures gradually contributes to an increase in quality. Moreover, on the longterm it facilitates a continuous update of highquality vocabulary crosswalks.
IOS Press Quality-Based Model For Effective and Robust Multi-User Pay-As-You-Go Ontology Matching
"... Using a pay-as-you-go strategy, we allow for a community of users to validate mappings obtained by an automatic ontology matching system using consensus for each mapping. The ultimate objectives are effectiveness—improving the quality of the obtained alignment (set of mappings) measured in terms of ..."
Abstract
- Add to MetaCart
(Show Context)
Using a pay-as-you-go strategy, we allow for a community of users to validate mappings obtained by an automatic ontology matching system using consensus for each mapping. The ultimate objectives are effectiveness—improving the quality of the obtained alignment (set of mappings) measured in terms of F-measure as a function of the number of user interactions—and robustness—making the system as much as possible impervious to user validation errors. Our strategy consisting of two major steps: candidate mapping selection, which ranks mappings based on their perceived quality, so as to present first to the users those mappings with lowest quality, and feedback propagation, which seeks to validate or invalidate those mappings that are perceived to be “similar ” to the mappings already presented to the users. The purpose of these two strategies is twofold: achieve greater improvements earlier and minimize overall user interaction. There are three important features of our approach. The first is that we use a dynamic ranking mechanism to adapt to the new conditions after each user interaction, the second is that we may need to present each mapping for validation more than once—revalidation—because of possible user errors, and the third is that we propagate a user’s input on a mapping immediately without first achieving consensus for that mapping. We study extensively the effectiveness and robustness of our approach as several of these parameters change, namely the error and revalidation rates, as a function of the number of iterations, to provide conclusive guidelines for the design and implementation of multi-user feedback ontology matching systems. 1.
Speeding Up Iterative Ontology Alignment using Block-Coordinate Descent
"... In domains such as biomedicine, ontologies are prominently utilized for annotating data. Con-sequently, aligning ontologies facilitates integrating data. Several algorithms exist for automati-cally aligning ontologies with diverse levels of performance. As alignment applications evolve and exhibit o ..."
Abstract
- Add to MetaCart
(Show Context)
In domains such as biomedicine, ontologies are prominently utilized for annotating data. Con-sequently, aligning ontologies facilitates integrating data. Several algorithms exist for automati-cally aligning ontologies with diverse levels of performance. As alignment applications evolve and exhibit online run time constraints, performing the alignment in a reasonable amount of time with-out compromising the quality of the alignment is a crucial challenge. A large class of alignment algorithms is iterative and often consumes more time than others in delivering solutions of high quality. We present a novel and general approach for speeding up the multivariable optimization process utilized by these algorithms. Specifically, we use the technique of block-coordinate descent (BCD), which exploits the subdimensions of the alignment problem identified using a partitioning scheme. We integrate this approach into multiple well-known alignment algorithms and show that the enhanced algorithms generate similar or improved alignments in significantly less time on a comprehensive testbed of ontology pairs. Because BCD does not overly constrain how we partition or order the parts, we vary the partitioning and ordering schemes in order to empirically determine the best schemes for each of the selected algorithms. As biomedicine represents a key application domain for ontologies, we introduce a comprehensive biomedical ontology testbed for the com-munity in order to evaluate alignment algorithms. Because biomedical ontologies tend to be large, default iterative techniques find it difficult to produce a good quality alignment within a reasonable amount of time. We align a significant number of ontology pairs from this testbed using BCD-enhanced algorithms. Our contributions represent an important step toward making a significant class of alignment techniques computationally feasible. 1.
Towards a Cluster-based Approach for User Participation in Ontology Maching
"... Abstract. User participation is a promising approach for Ontology Matching; however, determining the most representative pairs of entities is still a challenge. This paper delineates an Ontology Matching approach for user participation employing a clustering algorithm. ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract. User participation is a promising approach for Ontology Matching; however, determining the most representative pairs of entities is still a challenge. This paper delineates an Ontology Matching approach for user participation employing a clustering algorithm.
