Approach For Ontology Alignment Research Articles

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

This study presents an in-depth study and analysis of IoT semantic association and decision-making using a partial differential fuzzy unsupervised approach. It focuses on a semantic annotation framework for device metadata and a knowledge base construction method to further improve the interoperability of IoT domain knowledge by building a unified IoT domain knowledge base and designing and implementing a semantic IoT knowledge management and application generation system. The main proposal is an IoT generic domain ontology, which reuses the existing excellent ontologies of IoT as much as possible, extracts the commonly used concepts of the domain and combines them, and provides a unified semantic template for IoT applications. On the other hand, by applying the entity linking technique to the extension of the knowledge base and linking the structured metadata of devices to the corresponding entities of the background knowledge base, the domain knowledge base can be made to share the rich background knowledge. At the same time, the interoperability of heterogeneous IoT metadata between applications is enhanced by unifying data and concepts from different device applications to the same background knowledge base through entity alignment techniques. The semantic representation of events applicable to IoT application scenarios is investigated, and an IoT event ontology for representing abstract events and event relationships in IoT is designed; next, a domain ontology with IoT sensing and control event representation capability is constructed based on the IoT event ontology, in which the typical domain ontology (SSN) that can be used for IoT applications is followed by the ontology reuse principle is improved and extended to support the description of event types and interevent relationships, and the IoT event model is associated with the improved IoT base ontology through an ontology alignment approach. Finally, the IoT sensing and control ontology are validated by semantic modeling of device composition, component relationships, and operational processes based on the IoT sensing and control ontology.

Measuring Clusters of Labels in an Embedding Space to Refine Relations in Ontology Alignment

Ontology alignment plays a key role in the management of heterogeneous data sources and metadata. In this context, various ontology alignment techniques have been proposed to discover correspondences between the entities of different ontologies. This paper proposes a new ontology alignment approach based on a set of rules exploiting the embedding space and measuring clusters of labels to discover the relationship between entities. We tested our system on the OAEI conference complex alignment benchmark track and then applied it to aligning ontologies in a real-world case study. The experimental results show that the combination of word embedding and a measure of dispersion of the clusters of labels, which we call the radius measure, makes it possible to determine, with good accuracy, not only equivalence relations, but also hierarchical relations between entities.

A novel meta-matching approach for ontology alignment using grasshopper optimization

Ontology alignment is a fundamental task to support information sharing and reuse in heterogeneous information systems. Optimizing the combination of matchers by evolutionary algorithms to align ontology is an effective method. However, such methods have two significant shortcomings: weights need to be set manually to combine matchers, and a reference alignment is required during the optimization process. In this paper, a meta-matching approach GSOOM for automatically configuring weights and threshold using grasshopper optimization algorithm (GOA) has been proposed. In this approach, the ontology alignment problem is modeled as optimizing individual fitness of GOA. A fitness function is proposed, which includes two goals: maximizing the number of matching and the similarity score. Since it does not require an expert to provide a reference alignment, it is more suitable for real-world scenarios. To demonstrate the advantages of the approach, we conduct exhaustive experiments tasks on several standard datasets and compare its performance to other state-of-the-art methods. The experimental results illustrate that our approach is more efficiently and is significantly superior to other metaheuristic-based methods.

A learning-based ontology alignment approach using inductive logic programming

Ontologies are key concepts in the semantic web and have an impressive role which comprise the biggest and the most prominent part of the infrastructure in this realm of web research. By fast growth of the semantic web and also, the variety of its applications, ontology mapping (ontology alignment) has been transformed into a crucial issue in the realm of computer science. Several approaches are introduced for ontology alignment during these last years, but developing more accurate and efficient algorithms and finding new effective techniques and algorithms for this problem is an interesting research area since real-world applications with respect to their more complicated concepts need more efficient algorithms. In this paper, we illustrated a new ontology mapping method based on learning using Inductive Logic Programming (ILP), and show how the ILP can be used to solve the ontology mapping problem. As a matter of fact, in this approach, an ontology which is described in OWL format is interpreted to first-order logic. Then, with the use of learning based on inductive logic, the existing hidden rules and relationships between concepts are discovered and presented. Since the inductive logic has high flexibility in solving problems such as discovering relationships between concepts and links, it also can be performed effectively in solving the ontology alignment problem. Our experimental results show that this technique yield to more accurate results comparing to other matching algorithms and systems, achieving an F-measure of 95.6% and 91% on two well-known reference datasets the Anatomy and the Library, respectively.

Ontology Knowledge Mining for Ontology Alignment

AbstractAs the ontology alignment facilitates the knowledge exchange among the heterogeneous data sources, several methods have been introduced in literature. Nevertheless, few of them have been interested in decreasing the problem complexity and reducing the research space of correspondences between the input ontologies.This paper presents a new approach for ontology alignment based on the ontology knowledge mining. The latter consists on producing for each ontology a hierarchical structure of fuzzy conceptual clusters, where a concept can belong to several clusters simultaneously. Each level of the hierarchy reflects the knowledge granularity degree of the knowledge base in order to improve the effectiveness and speediness of the information retrieval. Actually, such method allows the knowledge granularity analyze between the ontologies and facilitates several ontology engineering techniques. The ontology alignment process is performed iteratively over the produced hierarchical structure of the fuzzy cl...

A session-based ontology alignment approach enabling user involvement1

One of the current challenges in ontology alignment is the user involvement in the alignment process. To obtain high-quality alignments user involvement is needed for validation of matching results ...

A New Instance-Based Approach for Ontology Alignment

Due to the increasing number of information sources available on the web and their distribution and heterogeneity, ontology alignment became a very important and inevitable problem to resolve in order to ensure semantic interoperability between these sources. Instance-based ontology alignment represents a very promising technique to find semantic correspondences between entities of different ontologies. In practice, two situations may arise: ontologies that share common instances and those share few or do not share common instances. In this paper, the authors describe a new approach to manage the latter case. This approach exploits the reasoning on ontologies in order to create a corpus of common instances. They have used the Biblio and Finance tests of Benchmark series of the OAEI 2012 evaluation campaign to evaluate the performance of their approach. The results obtained show the good performance of the authors' approach compared to ontology alignment systems and improves significantly the instance-based and reasoning-based methods.

Boosting the Reasoning-Based Approach by Applying Structural Metrics for Ontology Alignment

The amount of sources of information available on the web using ontologies as support continues to increase and is often heterogeneous and distributed. Ontology alignment is the solution to ensure semantic interoperability. In this paper, we describe a new ontology alignment approach, which consists of combining structure-based and reasoning-based approaches in order to discover new semantic correspondences between entities of different ontologies. We used the biblio test of the benchmark series and anatomy series of the Ontology Alignment Evaluation Initiative (OAEI) 2012 evaluation campaign to evaluate the performance of our approach. We compared our approach successively with LogMap and YAM++ systems. We also analyzed the contribution of our method compared to structural and semantic methods. The results obtained show that our performance provides good performance. Indeed, these results are better than those of the LogMap system in terms of precision, recall, and F-measure. Our approach has also been proven to be more relevant than YAM++ for certain types of ontologies and significantly improves the structure-based and reasoning-based methods.

Towards a representation for multi-viewpoints ontology alignments

In a heterogeneous multi-communities organisation, several communities or groups of users exist with their own viewpoints. Multi-viewpoint ontologies are becoming a crucial aspect in overcoming the limitations of sharing knowledge and keeping the viewpoint of each member of the community. Various ontology alignment approaches have been proposed; however, no system has explored the problem of ontology alignment in the specific context of multi-viewpoints. Moreover, the proposed alignment formats are not sufficient to cover the representation needs of the multi-viewpoints ontology alignments. The key insight into this work is that multi-viewpoints alignment is a set of semantic correspondences between multi-viewpoints ontology elements. Thus, this work focuses on the proposition of a new format allowing the presentation of semantic mapping between multi-viewpoints ontology elements. The extended multi-viewpoints alignment format has the possibility to take into account the different viewpoints' aspects in formal way.

Exploring methods to improve access to Music resources by aligning library Data with Linked Data: A report of methodologies and preliminary findings

As a part of a research project aiming to connect library data to the unfamiliar data sets available in the Linked Data (LD) community's CKAN Data Hub (thedatahub.org), this project collected, analyzed, and mapped properties used in describing and accessing music recordings, scores, and music‐related information used by selected music LD data sets, library catalogs, and various digital collections created by libraries and other cultural institutions. This article reviews current efforts to connect music data through the Semantic Web, with an emphasis on the Music Ontology (MO) and ontology alignment approaches; it also presents a framework for understanding the life cycle of a musical work, focusing on the central activities of composition, performance, and use. The project studied metadata structures and properties of 11 music‐related LD data sets and mapped them to the descriptions commonly used in the library cataloging records for sound recordings and musical scores (including MARC records and their extended schema.org markup), and records from 20 collections of digitized music recordings and scores (featuring a variety of metadata structures). The analysis resulted in a set of crosswalks and a unified crosswalk that aligns these properties. The paper reports on detailed methodologies used and discusses research findings and issues. Topics of particular concern include (a) the challenges of mapping between the overgeneralized descriptions found in library data and the specialized, music‐oriented properties present in the LD data sets; (b) the hidden information and access points in library data; and (c) the potential benefits of enriching library data through the mapping of properties found in library catalogs to similar properties used by LD data sets.

Assumption/Commitment Algorithmic Approach for Ontology Alignment

Ontology is the way to provide interoperability of different classes over semantic web. The better the ontology is constructed, the more are the chances for tight integration between different processes, no matter they are within the organization or are indirectly connected to organization, including external stakeholders i.e. Customers, Suppliers and Outsource services. For a properly design Ontology, it is always a mandatory step to apply different methods for Ontology Alignment. This propagates the reusability approach of a single object, thus at different instances, same property or attribute can be declared, rather going for heterogeneous environment. We have different traditional methods to align the ontology, however we are going to propose new method, adopted from the other domain, to better align the ontology. We will advance the concept by introducing algorithm which can help implementing the solution.

Homolonto: generating homology relationships by pairwise alignment of ontologies and application to vertebrate anatomy

Motivation: The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we need to establish relations between ontologies describing different species.Results: We present a new algorithm, and its implementation in the software Homolonto, to create new relationships between anatomical ontologies, based on the homology concept. Homolonto uses a supervised ontology alignment approach. Several alignments can be merged, forming homology groups. We also present an algorithm to generate relationships between these homology groups. This has been used to build a multi-species ontology, for the database of gene expression evolution Bgee.Availability: download section of the Bgee website http://bgee.unil.ch/Contact: marc.robinson-rechavi@unil.chSupplementary information: Supplementary data are available at Bioinformatics online.

A Neural-Networks-Based Approach for Ontology Alignment

Ontologies are key elements in the Semantic Web for providing formal definitions of concepts and relationships. Such definitions are needed to have data that could be understood and reasoned upon by machines as well as humans. However, because of the possibility of having many Ontologies in the web, alignment – which aims providing mappings across them – is a necessary operation. Many metrics have been defined for ontology alignment. The so-called simple metrics use linguistic or structural features of Ontological concepts to create mappings. Compound metrics, on the other hand, combine some of the simple metrics to have a better results. This paper reports our new method for compound metric creation. It is based on a supervised learning approach in data mining where a training set is used to create a neural network model, performs sensitivity analysis on it to select appropriate metrics among a set of existing ones, and finally constructs a neural network model to combine the result metrics into a compound one. Empirical results of applying it on a set of Ontologies is also shown in this paper.