Isabel F. Cruz

The AgreementMakerLight Ontology Matching System

AgreementMaker is one of the leading ontology matching systems, thanks to its combination of a flexible and extensible framework with a comprehensive user interface. In many domains, such as the biomedical, ontologies are becoming increasingly large thus presenting new challenges. We have developed a new core framework, AgreementMakerLight, focused on computational efficiency and designed to handle very large ontologies, while preserving most of the flexibility and extensibility of the original AgreementMaker framework. We evaluated the efficiency of AgreementMakerLight in two OAEI tracks: Anatomy and Large Biomedical Ontologies, obtaining excellent run time results. In addition, for the Anatomy track, AgreementMakerLight is now the best system as measured in terms of F-measure. Also in terms of F-measure, AgreementMakerLight is competitive with the best OAEI performers in two of the three tasks of the Large Biomedical Ontologies track that match whole ontologies.

Interactive User Feedback in Ontology Matching Using Signature Vectors

When compared to a gold standard, the set of mappings that are generated by an automatic ontology matching process is neither complete nor are the individual mappings always correct. However, given the explosion in the number, size, and complexity of available ontologies, domain experts no longer have the capability to create ontology mappings without considerable effort. We present a solution to this problem that consists of making the ontology matching process interactive so as to incorporate user feedback in the loop. Our approach clusters mappings to identify where user feedback will be most beneficial in reducing the number of user interactions and system iterations. This feedback process has been implemented in the Agreement Maker system and is supported by visual analytic techniques that help users to better understand the matching process. Experimental results using the OAEI benchmarks show the effectiveness of our approach. We will demonstrate how users can interact with the ontology matching process through the Agreement Maker user interface to match real-world ontologies.

Automatic Background Knowledge Selection for Matching Biomedical Ontologies

Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the background knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting background knowledge sources for any given ontologies to match. This methodology measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of background knowledge that led to the highest improvements over the baseline alignment (i.e., without background knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on background knowledge.

Building linked ontologies with high precision using subclass mapping discovery

The creation of links between schemas of published datasets is a key part of the Linked Open Data (LOD) paradigm. The ability to discover these links “on the go” requires that ontology matching techniques achieve good precision and recall within acceptable execution times. In this paper, we add similarity-based and mediator-based ontology matching methods to the Agreementmaker ontology matching system, which aim to efficiently discover high precision subclass mappings between LOD ontologies. Similarity-based matching methods discover subclass mappings by extrapolating them from a set of high quality equivalence mappings and from the interpretation of compound concept names. Mediator-based matching methods discover subclass mappings by comparing polysemic lexical annotations of ontology concepts and by considering external web ontologies. Experiments show that when compared with a leading LOD approach, Agreementmaker achieves considerably higher precision and F-measure, at the cost of a slight decrease in recall.

GIVA: a semantic framework for geospatial and temporal data integration, visualization, and analytics

The availability of a wide variety of geospatial datasets demands new mechanisms to perform their integrated analysis and visualization. In this demo paper, we describe our semantic framework, GIVA, for Geospatial and temporal data Integration, Visualization, and Analytics. Given a geographic region and a time interval, GIVA addresses the problem of accessing simultaneously several datasets and of establishing mappings between the underlying concepts and instances, using automatic methods. These methods must consider several challenges, such as those that arise from heterogeneous formats, lack of metadata, and multiple spatial and temporal data resolutions. A web interface lets users interact with a map and select datasets to be integrated, displaying as a result reports where values pertaining to different datasets are compared, analyzed, and visualized.

Automatic configuration selection using ontology matching task profiling

An ontology matching system can usually be run with different configurations that optimize the systems effectiveness, namely precision, recall, or F-measure, depending on the specific ontologies to be aligned. Changing the configuration has potentially high impact on the obtained results. We apply matching task profiling metrics to automatically optimize the systems configuration depending on the characteristics of the ontologies to be matched. Using machine learning techniques, we can automatically determine the optimal configuration in most cases. Even using a small training set, our system determines the best configuration in 94% of the cases. Our approach is evaluated using the AgreementMaker ontology matching system, which is extensible and configurable.

What's in a 'nym'? Synonyms in Biomedical Ontology Matching

To bring the Life Sciences domain closer to a Semantic Web realization it is fundamental to establish meaningful relations between biomedical ontologies. The successful application of ontology matching techniques is strongly tied to an effective exploration of the complex and diverse biomedical terminology contained in biomedical ontologies. In this paper, we present an overview of the lexical components of several biomedical ontologies and investigate how different approaches for their use can impact the performance of ontology matching techniques. We propose novel approaches for exploring the different types of synonyms encoded by the ontologies and for extending them based both on internal synonym derivation and on external ontologies. We evaluate our approaches using AgreementMaker, a successful ontology matching platform that implements several lexical matchers, and apply them to a set of four benchmark biomedical ontology matching tasks. Our results demonstrate the impact that an adequate consideration of ontology synonyms can have on matching performance, and validate our novel approach for combining internal and external synonym sources as a competitive and in many cases improved solution for biomedical ontology matching.

Semantic extraction of geographic data from web tables for big data integration

There are millions of web tables with geographic data that are pertinent for big data integration in a variety of domain applications, such as urban sustainability, transportation networks, policy studies, and public health. These tables, however, are heterogeneous in structure, concepts, and metadata. One of the challenges in semantically extracting geographic data is the need to resolve these heterogeneities so as to uncover a conceptual hierarchy, metadata associated with instances, and geographic information---corresponding respectively to ontologies, elements that we call features, and cell values that can be used to identify geographic coordinates. In this paper, we present an architecture with methods to: (1) extract feature-rich web tables; (2) identify features; (3) construct a schema and instances using RDF; (4) perform geocoding. Preliminary experiments led to high accuracy in table identification and feature naming even when compared to manual evaluation.

Pay-As-You-Go Multi-user Feedback Model for Ontology Matching

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 effort 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 matching system and the Ontology Alignment Evaluation Initiative (OAEI) Benchmarks track to evaluate our approach. Our results show how Fmeasure and robustness vary as a function of the number of user validations. We consider different user error and revalidation rates (the latter measures the number of times that the same mapping is validated). Our results highlight complex trade-offs and point to the benefits of dynamically adjusting the revalidation rate.

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 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.