It is beneficial to annotate sensor data with distinct sensor ontologies in order to facilitate interoperability among different sensor systems. However, for this interoperability to be possible, comparable sensor ontologies are required since it is essential to make meaningful links between relevant sensor data. Swarm Intelligent Algorithms (SIAs), namely the Beetle Swarm Optimisation Algorithm (BSO), present a possible answer to ontology matching problems. This research focuses on a method for optimizing ontology alignment that employs BSO. A novel method for effectively controlling memory use and striking a balance between algorithm exploration and exploitation is proposed: the Simulated Annealing‐based Beetle Swarm Optimisation Algorithm (SA‐BSO). Utilizing Gray code for solution encoding, two compact operators for exploitation and exploration, and Probability Vectors (PVs) for swarming choosing exploitation and exploration, SA‐BSO combines simulated annealing with the beetle search process. Through inter‐swarm communication in every generation, SA‐BSO improves search efficiency in addressing sensor ontology matching. Three pairs of real sensor ontologies and the Conference track were used in the study to assess SA‐BSO's efficacy. Statistics show that SA‐BSO‐based ontology matching successfully aligns sensor ontologies and other general ontologies, particularly in conference planning scenarios.

An optimized system for sensor ontology meta‐matching using swarm intelligent algorithm

Linking data by finding matching instances in different datasets requires considering many characteristics, such as structural heterogeneity, implicit knowledge, and URI (Uniform Resource Identifier)-oriented identification. The authors propose a context-independent approach to align Linked data through an alignment process based on the ontological model’s components and considering data’s multidimensionality. The researchers experimented with the proposed approach against two methods for aligning linked data in two datasets and evaluated precision, recall, and f-measure metrics. The authors also conducted a case study in a real scenario considering a Brazilian publication dataset on computers and education. This study’s results indicate that the proposed approach overcomes the other methods (regarding the precision, recall, and f-measure metrics), requiring less work when changing the dataset domain. This work’s main contributions include enabling real datasets to be semi-automatically linked, presenting an approach capable of calculating resource similarity.

/pdf/a-context-independent-ontological-linked-data-alignment-2icgyjk8.pdf

A Context-Independent Ontological Linked Data Alignment Approach to Instance Matching

Ontologies have emerged to establish a well-defined meaning for information, solving problems of heterogeneity in data semantics and facilitating the process of information exchange. However, ontologies have generated a new semantic problem, since using more than one ontology can generate ambiguity in the meaning of a given data. The problem of ontology matching is to search for relationships between entities of distinct ontologies, solving the problem of semantic heterogeneity of the data. The problem is a relevant issue in the area of knowledge representation and several approaches have been proposed to solve it. This work presents a systematic mapping of the main works published in the area with an emphasis on metaheuristics-based meta-matching approaches.

Metaheuristics-based ontology meta-matching approaches

To address the Knowledge Graph (KG) heterogeneity issue, we need to determine a set of entity correspondences, which requires aggregating several complementary similarity measures to improve the confidence of the results. How to determine the suitable aggregating weights for the similarity measures to improve the KG alignment’s quality is called the KG meta-matching problem, whose challenge of scalability remains significant in the Semantic Web (SW) domain. To face this challenge, this work proposes a Compact Niching Evolutionary Algorithm (CNEA) based matching technique. We first propose an approximate evaluation metric on the alignment’s quality, and on this basis, a multi-modal optimization model is constructed to formally define the KG meta-matching problem. Then, a niching strategy is combined with EA’s evolutionary paradigm to address it, which is able to effectively locate and maintain multiple global optimal solutions. Moreover, a serial matching framework and the compact evolutionary mechanism are presented to improve CNEA’s efficiency. In particular, the former reduces the algorithm’s search space of the instance matching phase with schema-level alignments, and the latter uses the probability representation on the population to reduce the algorithm’s memory consumption and run time. The experiment utilizes Ontology Alignment Evaluation Initiative (OAEI)’s KG track to test our proposal’s performance, and experimental results show that CNEA-based KG matching technique is both effective and efficient. 

Matching Knowledge Graphs with Compact Niching Evolutionary Algorithm

Ontology meta-matching techniques have been consolidated as one of the best approaches to face the problem of discovering semantic relationships between knowledge models that belong to the same domain but have been developed independently. After more than a decade of research, the community has reached a stage of maturity characterized by increasingly better results and aspects such as the robustness and scalability of solutions have been solved. However, the resulting models remain practically intelligible to a human operator. In this work, we present a novel approach based on Mamdani fuzzy inference exploiting a model very close to natural language. This fact has a double objective: to achieve results with high degrees of accuracy but at the same time to guarantee the interpretability of the resulting models. After validating our proposal with several ontological models popular in the biomedical field, we can conclude that the results obtained are promising.

Interpretable ontology meta-matching in the biomedical domain using Mamdani fuzzy inference

An Analysis of Links in Wikidata

Ontology matching has become a key issue to solve problems of semantic heterogeneity. Several researchers propose diverse techniques that can be used in distinct scenarios. Ontology meta-matching approaches are a specialization of ontology matching and have achieved good results in pairs of ontologies with different types of heterogeneities. However, developing a new ontology meta-matcher can be a costly process and a lot of experiments are often carried out to analyze the behavior of the matcher. This article presents a modularized framework that covers the main stages of the ontology meta-matching evaluation process. This framework aims to aid researchers to develop and analyze algorithms for ontology meta-matching, mainly metaheuristic-based supervised and unsupervised approaches. As the main contribution of the research, the framework proposed will facilitate the evaluation of ontology meta-matching approaches and, as the secondary contribution, a data provenance model that captures the main information generated and consumed throughout experiments is presented in the framework.

A framework for evaluating ontology meta-matching approaches

Ontology matching has become one of the main research topics to address problems related to semantic interoperability on the web. The main goal is to find ways to make different ontologies interoperable. Due to the high heterogeneity in the knowledge representation of each ontology, several matchers are proposed in the literature, each seeking to capture a specific aspect of the ontology. Generally, different matchers are complementary and none stand out in all test cases. In this paper, we present a meta-matching approach employing the prey–predator meta-heuristic in order to define a set of weights to find the best possible result from a set of matchers. The approach was evaluated on the Ontology Alignment Evaluation Initiative benchmark and results showed that the prey–predator algorithm is competitive with other popular algorithms as it achieves an average f-measure of 0.91.

A Prey–Predator Approach for Ontology Meta-matching

/pdf/an-experimental-analysis-on-evolutionary-ontology-meta-5axjrgq3wk.pdf

An experimental analysis on evolutionary ontology meta-matching

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