Ontology alignment based on word embedding and random forest classification.

Ontology alignment is crucial for integrating heterogeneous data sources and forms an important component for realising the goals of the semantic web. Accordingly, several ontology alignment techniques have been proposed and used for discovering correspondences between the concepts (or entities) of different ontologies. However, these techniques mostly depend on string-based similarities which are unable to handle the vocabulary mismatch problem. Also, determining which similarity measures to use and how to effectively combine them in alignment systems are challenges that have persisted in this area. In this work, we introduce a random forest classifier approach for ontology alignment which relies on word embedding to discover semantic similarities between concepts. Specifically, we combine string-based and semantic similarity measures to form feature vectors that are used by the classifier model to determine when concepts match. By harnessing background knowledge and relying on minimal information from the ontologies, our approach can deal with knowledge-light ontological resources. It also eliminates the need for learning the aggregation weights of multiple similarity measures. Our experiments using Ontology Alignment Evaluation Initiative (OAEI) dataset and real-world ontologies highlight the utility of our approach and show that it can outperform state-of-the-art alignment systems

Semantic metrics

In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can be boiled down to one fundamental operation: computing the similarity and?or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a variety of research disciplines, and enrich them with semantics based on standard Description Logic constructs. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level and we speculate on the usability of our ideas through potential areas

Ontology alignment based on word embedding and random forest classification

Ontology alignment is crucial for integrating heterogeneous data sources and forms an important component for realising the goals of the semantic web. Accordingly, several ontology alignment techniques have been proposed and used for discovering correspondences between the concepts (or entities) of different ontologies. However, these techniques mostly depend on string-based similarities which are unable to handle the vocabulary mismatch problem. Also, determining which similarity measures to use and how to effectively combine them in alignment systems are challenges that have persisted in this area. In this work, we introduce a random forest classifier approach for ontology alignment which relies on word embedding to discover semantic similarities between concepts. Specifically, we combine string-based and semantic similarity measures to form feature vectors that are used by the classifier model to determine when concepts match. By harnessing background knowledge and relying on minimal information from the ontologies, our approach can deal with knowledge-light ontological resources. It also eliminates the need for learning the aggregation weights of multiple similarity measures. Our experiments using Ontology Alignment Evaluation Initiative (OAEI) dataset and real-world ontologies highlight the utility of our approach and show that it can outperform state-of-the-art alignment systems

Dealing with uncertain entities in ontology alignment using rough sets

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision

SANA NetGO: A combinatorial approach to using Gene Ontology (GO) terms to score network alignments

Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure the GO similarity between two proteins in isolation, but pairs of proteins in a network alignment are not isolated: each pairing is implicitly dependent upon every other pairing via the alignment itself. Current methods fail to take into account the frequency of GO terms across the networks, and attempt to account for common GO terms in an ad hoc fashion by imposing arbitrary rules on when to "allow" GO terms based on their location in the GO hierarchy, rather than using readily available frequency information in the PPI networks themselves. Here we develop a new measure, NetGO, that naturally weighs infrequent, informative GO terms more heavily than frequent, less informative GO terms, without requiring arbitrary cutoffs. In particular, NetGO down-weights the score of frequent GO terms according to their frequency in the networks being aligned. This is a global measure applicable only to alignments, independent of pairwise GO measures, in the same sense that the edge-based EC or S3 scores are global measures of topological similarity independent of pairwise topological similarities. We demonstrate the superiority of NetGO by creating alignments of predetermined quality based on homologous pairs of nodes and show that NetGO correlates with alignment quality much better than any existing GO-based alignment measures. We also demonstrate that NetGO provides a measure of taxonomic similarity between species, consistent with existing taxonomic measures--a feature not shared with existing GO-based network alignment measures. Finally, we re-score alignments produced by almost a dozen aligners from a previous study and show that NetGO does a better job than existing measures at separating good alignments from bad ones

A Large Scale Dataset for the Evaluation of Ontology Matching Systems

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. The paper has been accepted for publication in "The Knowledge Engineering Review", Cambridge Universty Press (ISSN: 0269-8889, EISSN: 1469-8005)

Ontology Alignment Architecture for Semantic Sensor Web Integration

Abstract: Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall

Introducing fuzzy trust for managing belief conflict over semantic web data

Interpreting Semantic Web Data by different human experts can end up in scenarios, where each expert comes up with different and conflicting ideas what a concept can mean and how they relate to other concepts. Software agents that operate on the Semantic Web have to deal with similar scenarios where the interpretation of Semantic Web data that describes the heterogeneous sources becomes contradicting. One such application area of the Semantic Web is ontology mapping where different similarities have to be combined into a more reliable and coherent view, which might easily become unreliable if the conflicting beliefs in similarities are not managed effectively between the different agents. In this paper we propose a solution for managing this conflict by introducing trust between the mapping agents based on the fuzzy voting model

Statistical analysis of the owl:sameAs network for aligning concepts in the linking open data cloud

The massively distributed publication of linked data has brought to the attention of scientific community the limitations of classic methods for achieving data integration and the opportunities of pushing the boundaries of the field by experimenting this collective enterprise that is the linking open data cloud. While reusing existing ontologies is the choice of preference, the exploitation of ontology alignments still is a required step for easing the burden of integrating heterogeneous data sets. Alignments, even between the most used vocabularies, is still poorly supported in systems nowadays whereas links between instances are the most widely used means for bridging the gap between different data sets. We provide in this paper an account of our statistical and qualitative analysis of the network of instance level equivalences in the Linking Open Data Cloud (i.e. the sameAs network) in order to automatically compute alignments at the conceptual level. Moreover, we explore the effect of ontological information when adopting classical Jaccard methods to the ontology alignment task. Automating such task will allow in fact to achieve a clearer conceptual description of the data at the cloud level, while improving the level of integration between datasets. <br/

Comparison of ontology alignment systems across single matching task via the McNemar's test

Ontology alignment is widely-used to find the correspondences between different ontologies in diverse fields.After discovering the alignments,several performance scores are available to evaluate them.The scores typically require the identified alignment and a reference containing the underlying actual correspondences of the given ontologies.The current trend in the alignment evaluation is to put forward a new score(e.g., precision, weighted precision, etc.)and to compare various alignments by juxtaposing the obtained scores. However,it is substantially provocative to select one measure among others for comparison.On top of that, claiming if one system has a better performance than one another cannot be substantiated solely by comparing two scalars.In this paper,we propose the statistical procedures which enable us to theoretically favor one system over one another.The McNemar's test is the statistical means by which the comparison of two ontology alignment systems over one matching task is drawn.The test applies to a 2x2 contingency table which can be constructed in two different ways based on the alignments,each of which has their own merits/pitfalls.The ways of the contingency table construction and various apposite statistics from the McNemar's test are elaborated in minute detail.In the case of having more than two alignment systems for comparison, the family-wise error rate is expected to happen. Thus, the ways of preventing such an error are also discussed.A directed graph visualizes the outcome of the McNemar's test in the presence of multiple alignment systems.From this graph, it is readily understood if one system is better than one another or if their differences are imperceptible.The proposed statistical methodologies are applied to the systems participated in the OAEI 2016 anatomy track, and also compares several well-known similarity metrics for the same matching problem