Matching large-scale biomedical ontologies with central concept based partitioning algorithm and Adaptive Compact Evolutionary Algorithm

The four-volume set LNAI 6881-LNAI 6884 constitutes the refereed proceedings of the 15th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2011, held in Kaiserslautern, Germany, in September 2011. Part 1: The total of 244 high-quality papers presented were carefully reviewed and selected from numerous submissions. The 61 papers of Part 1 are organized in topical sections on artificial neural networks, connectionists systems and evolutionary computation, machine learning and classical AI, agent, multi-agentsystems, knowledge based and expert systems, intelligent vision, image processing and signal processing, knowledge management, ontologies, and data mining.

Knowledge-Based and Intelligent Information and Engineering Systems, Part I: 15th International Conference, KES 2011, Kaiserslautern, Germany, ... / Lecture Notes in Artificial Intelligence)

Introducing a new hobby for other people may inspire them to join with you. Reading, as one of mutual hobby, is considered as the very easy hobby to do. But, many people are not interested in this hobby. Why? Boring is the reason of why. However, this feel actually can deal with the book and time of you reading. Yeah, one that we will refer to break the boredom in reading is choosing relationships in the organization of knowledge as the reading material.

Relationships in the Organization of Knowledge

Biomedical ontologies have gained particular relevance in the life science domain due to its prominent role in representing knowledge in this domain. However, the existing biomedical ontologies could define the same biomedical concept in different ways, which yields the biomedical ontology heterogeneous problem. To implement the inter-operability among the biomedical ontologies, it is critical to establish the semantic links between heterogenous biomedical concepts, so-called biomedical ontology matching. Since modeling the ontology matching problem is a complex and time-consuming task, swarm intelligent algorithm (SIA) becomes a state-of-the-art methodology for solving this problem. However, when addressing the biomedical ontology matching problem, the existing SIA-based matchers tend to be inefficient due to biomedical ontology’s large-scale concepts and complex semantic relationships. In this work, we propose a compact firefly algorithm (CFA), where the explicit representation of the population is replaced by a probability distribution and two compact movement operators are presented to save the memory consumption and runtime of the population-based SIAs. We exploit the anatomy track, disease and phenotype track and biodiversity and ecology track from the ontology alignment evaluation initiative (OAEI) to test CFA-based matcher’s performance. The experimental results show that CFA can improve the FA-based matcher’s memory consumption and runtime by, respectively, 68.92% and 38.97% on average, and its results significantly outperform other SIA-based matchers and OAEI’s participants.

A compact firefly algorithm for matching biomedical ontologies

Ontology mapping for semantically enabled applications

Biomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves. The biomedical tracks in the Ontology Matching Evaluation Initiative (OAEI) have spurred the development of matching systems able to tackle these challenges, and benchmarked their general performance. In this study, we dissect the strategies employed by matching systems to tackle the challenges of matching biomedical ontologies and gauge the impact of the challenges themselves on matching performance, using the AgreementMakerLight (AML) system as the platform for this study. We demonstrate that the linear complexity of the hash-based searching strategy implemented by most state-of-the-art ontology matching systems is essential for matching large biomedical ontologies efficiently. We show that accounting for all lexical annotations (e.g., labels and synonyms) in biomedical ontologies leads to a substantial improvement in F-measure over using only the primary name, and that accounting for the reliability of different types of annotations generally also leads to a marked improvement. Finally, we show that cross-references are a reliable source of information and that, when using biomedical ontologies as background knowledge, it is generally more reliable to use them as mediators than to perform lexical expansion. We anticipate that translating traditional matching algorithms to the hash-based searching paradigm will be a critical direction for the future development of the field. Improving the evaluation carried out in the biomedical tracks of the OAEI will also be important, as without proper reference alignments there is only so much that can be ascertained about matching systems or strategies. Nevertheless, it is clear that, to tackle the various challenges posed by biomedical ontologies, ontology matching systems must be able to efficiently combine multiple strategies into a mature matching approach.

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Tackling the challenges of matching biomedical ontologies

AgreementMakerLight (AML) is an automated ontology matching system that was developed with both extensibility and efficiency in mind. This paper describes its configuration for the OAEI 2017 competition and discusses its results. For this OAEI edition, we built upon the instance matching foundations we laid last year, and tackled the new Hobbit track and its new evaluation platform. AML was the only system to participate in all OAEI tracks this year, and was the top performing system or among the top performing ones in nearly all tracks, including the new Hobbit track and the new datasets in the Process Model and Disease and Phenotype tracks. 1 Presentation of the System 1.1 State, Purpose, General Statement AgreementMakerLight (AML) is an ontology matching system inspired by AgreementMaker [2, 3] but more concerned with efficiency, in order to tackle large-scale matching problems [7]. While it originally focused primarily on the biomedical domain, it has since been expanded to address a broad range of ontology and instance matching problems. AML relies heavily on lexical matching techniques [10], with an emphasis on the use of background knowledge [6], but also includes structural components for both matching and filtering – namely it features a logical repair algorithm [11] This year, our development of AML centered on the instance matching tasks from the new Hobbit track, and to a lesser degree on the new tasks in the Process Model Matching and Instance Matching tracks. We maintained the solution of using configuration files we adopted last year, but only for the instance matching tasks, as only for these is the goal of the matching tasks not always inferable from the datasets (e.g., it is generally not possible to infer when the goal is to match only instances of a given type). 1.2 Specific Techniques Used For the sake of brevity, this section focuses mainly on the features of AML that are new for this edition of the OAEI. For a complete description of AML’s matching strategy, please refer to the results papers of the last two OAEI editions [4, 5].

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Isabela Mott

Papers

Tackling the challenges of matching biomedical ontologies

Results of AML in OAEI 2017.