Ontology computation for graph spaces focus on partial vertex pairs

Ontology learning is a technology. Ontology learning can be used to establish ontology automatically or semi-automatically by introducing the ontology engineering and machine learning technology and many other sciences and technologies. The ontology learning technology which is proposed in our paper is to reduce the time of building an entire ontology. Our paper presents an Ontology Learning model which will enhance the efficiency of extraction concept, and enhance the efficiency of ontology building. It includes several aspects, and area concept extraction is the main aspect of all. The model combines personalized recommendation with concept extraction and realizes a more accurate and stable domain concept extraction method. We describe these techniques and report the results of the experiment examining its effectiveness and efficiency.

Property Graph databases (PGs) are emerging as efficient graph stores with flexible schemata. This raises the need to have a unified view over heterogenous data produced from these stores. Ontology based Data Access (OBDA) has become the most dominant approach to integrate heterogeneous data sources by providing a unified conceptual view (ontology) over them. The corner stone of any OBDA system is to define mappings between the data source and the target (domain) ontology. However, manual mapping generation is time consuming and requires great efforts. This paper proposes ProGOMap ( Pro perty G raph to O ntology M apper) system that automatically generates mappings from property graphs to a domain ontology. ProGOMap starts by generating a putative ontology with direct axioms from PG. A novel ontology learning algorithm is proposed to enrich the putative ontology with subclass axioms inferred from PG. The putative ontology is then aligned to an existing domain ontology using string similarity metrics. Another algorithm is proposed to align object properties between the two ontologies considering different modelling criteria. Finally, mappings are generated from alignment results. Experiments were done on eight data sets with different scenarios to evaluate the effectiveness of the generated mappings. The experimental results achieved mapping accuracy up to 97% and 81% when addressing PG-to-ontology terminological and structural heterogeneities, respectively. Ontology learning by inferring subclass axioms from a property graph helps to address the heterogeneity between the PG and ontology models.

LDAP based search mechanisms in Grid environment are syntactic based and do not support semantic information extraction. With the quick development of Semantic Grid, it demands a more flexible and powerful search mechanism for Grid information discovery. We propose a new search mechanism which extends LDAP and can be used in the Semantic Grid environment for semantic searching of relevant information. The mechanism uses an ontology learning model which maps WordNet lexicon into OWL ontologies. Such ontology assistant mechanism can utilize the reasoning services from particular description logic systems and make use of more machine automation in the searching process.

Research on domain ontology in different granulations based on concept lattice

Ontology is the backbone of the Semantic Web, helping users search for relevant resources from the Web of linked data. The existing context-free mapping approach between tags and concepts fails to address the problems of social synonymy and social polysemy when ontologies are induced from folksonomies. The novel contributions of this paper are threefold. First, grounded in the cognitively motivated category utility measure, a novel basic-level concept mining algorithm is developed to construct semantically rich concept vectors to alleviate the problem of social synonymy. Second, contextual aspects of ontology learning are exploited via probabilistic topic modeling to address the problem of social polysemy. Third, a novel context-sensitive domain ontology learning algorithm that combines link- and content-based semantic analysis is developed to identify both taxonomic and associative relations among concepts. To the best of our knowledge, this is the first successful research that exploits a cognitively motivated method to learn context-sensitive domain ontologies from folksonomies. By using the Open Directory Project ontology as a benchmark, we examined the effectiveness of the proposed algorithms based on social annotations crawled from three different folksonomy sites. Our experimental results show that the proposed ontology learning system significantly outperforms the best baseline system by 13.83% in terms of taxonomic F-measure. The practical implication of our research is that high-quality ontologies are constructed with minimal human intervention to facilitate concept-driven retrieval of linked data and the knowledge-based interoperability among enterprises.

Ontology similarity calculation and ontology mapping are important research topics in information retrieval. By learning optimization similarity function, we propose the new algorithm for ontology similarity measure and ontology mapping. The stability of ontology algorithms is studied by adopting a strategy which adjusts the sample set by deleting one or two element from it. Relationship between uniform loss stability and uniform score stability is investigated. A sufficient condition for uniform score stability is given. The result of our work shows that if for any (v,v') ∈ V × V, a kernel function K((v,v'), (v,v')) has a limited upper bound, then the ontology algorithm which minimizes the regularization empirical l-error will have good uniform score stability. Also, two experiments results show that the proposed algorithm has high accuracy and efficiency for similarity calculation and ontology mapping. Index Terms—Ontology, similarity calculation, ontology mapping.

Ontology similarity calculation and ontology mapping are crucial research topics in information retrieval. Ontology, as a concept structure model, is also widely used in biology, physics, geography and social sciences. In this paper, we propose new algorithms for ontology similarity measurement and ontology mapping using supervised multidimensional scaling and multi-dividing technologies. Two experimental results show that the proposed new algorithm has high accuracy and efficiency on ontology similarity calculation and ontology mapping in certain applications.

The conceptual structure of the ontology is usually represented by a graph, and the related information of this concept is encapsulated by a vector with uniform dimension. The essence of the similarity calculation of the ontology concept is the calculation of the distance of the vector corresponding to the vertex in the high-dimensional space. This paper continues to consider the ontology learning algorithm of the multi-dividing setting, and proposes a MB based learning strategy under this framework. The experimental data verifies the effectiveness of the given new algorithm.

This paper presents an automated ontology framework for service robots. The framework is designed to automatically create an ontology and an instance of concept in dynamic environment. Ontology learning from text is applied to build a concept hierarchy using WordNet which provides a rich semantic processing for physical objects. The Automated Ontology is composed of four modules: Concept Creation, Property Creation, Relationship Creation and Instance of Concept Creation. The automated ontology algorithm was implemented in order to create the concept hierarchy in the Robot Ontology. The Semantic Knowledge Acquisition represents knowledge of physical objects in dynamic environments. In simulation experiments, the list of object names and property names was identified. The result shows the concept hierarchy which represents explicit terms and the semantic knowledge of physical objects for performing everyday manipulation tasks.

In the information retrieval field, ontology plays a crucial role in searching for information that is highly semantic similar to the query concept in origin. It also plays a major roles in returning the results to the user. The main purpose of ontology algorithm lies in obtaining the function for calculating the similarity between concepts. One learning technology in applying ontology is to obtain a ontology function f:V Æ R In this structure, it maps each vertex to a real number and the similarity between two concepts is then determined by the difference of their real numbers. In this article, we report a sparse vector learning algorithm for ontology similarity measure and ontology mapping in terms of gradient descent and iterative computation. The ontology penalty term in optimization model is reformulated by virtue of dual norm, and then the smooth approximation to this penalty term is obtained. The main procedure of our algorithm is iterative computation based on gradient descent tricks. Results achieved in the simulation experiments show that the new proposed algorithm is highly efficient and accuracy in ontology similarity measure and ontology mapping in multiple disciplines.

The exponential growth of heterogeneous enterprise data has exposed the limitations of traditional Big Data architectures, which prioritize storage scalability over semantic understanding. This paper presents a Semantic Data Lake (SDL) framework that integrates Big Data technologies with knowledge graphs and ontology-driven reasoning to enhance enterprise decision support. The SDL architecture introduces a multi-layered system encompassing data ingestion, semantic annotation, ontology alignment, and graph-based reasoning for contextual query processing. Implemented using Hadoop, Spark, and GraphDB, the framework demonstrates superior performance in query efficiency, scalability, and semantic accuracy compared to conventional data lakes. Experimental evaluations show up to a 40% reduction in query latency and a 19% improvement in semantic precision, achieved through ontology mapping and reasoning-based query optimization. The results validate that semantic enrichment transforms data lakes into intelligent ecosystems capable of delivering explainable, context-aware analytics. The paper concludes by outlining future research directions, including AI-driven ontology learning, federated semantic integration, and hybrid reasoning for real-time knowledge discovery.

Ontology similarity calculation and ontology mapping are important research topics in information retrieval. One method for ontology similarity measure is using multi-dividing approach. Assume that the notation and terminology used but undefined in this paper can be found in [4] and [5]. We show that the assumption of strict ordering of xi*can be relaxed to allow some ties in the likelihood ratio. The same proof remains true if we consider equivalence classes defined by the likelihood ratio and relabel xi* as its equivalence class denoted by [xi*].

In recent years, as a semantic analysis and computational tool, ontology has been widely applied in many engineering applications. Many cases suggests that it’s confronted with countless big data source with the complex data structures. In order to relieve the dilemma, the sparse learning algorithms are introduced into the ontology similarity measuring and ontology mapping. In this setting, it should be a high dimensional expression of each ontology vertex, and the ontology algorithm should extract key component information effectively. Under such background, we consider the ontology sparse vector learning algorithm and application in different engineering applications. In this article, by means of coordinate descent minimization tricks, we present the ontology sparse vector optimization strategy and discuss the different transformation in different settings. At last, the new ontology sparse vector learning proceeding is applied to four engineering applications respectively to get its efficiency verified.

Historically, libraries have been responsible for storing, preserving, cataloguing and making available to the public large collections of information resources. In order to classify and organize these collections, the library community has developed several standards for the production, storage and communication of data describing different aspects of library knowledge assets. However, as we will argue in this thesis, most of the current practices and standards available are limited in their ability to integrate library data within the largest information network ever created: the World Wide Web (WWW). This thesis aims at providing theoretical foundations and technical solutions to tackle some of the challenges in bridging the gap between these two areas: library science and technologies, and the Web of Data. The investigation of these aspects has been tackled with a combination of theoretical, technological and empirical approaches. Moreover, the research presented in this thesis has been largely applied and deployed to sustain a large online data service of the National Library of Spain: datos.bne.es. Specifically, this thesis proposes and evaluates several constructs, languages, models and methods with the objective of transforming and publishing library catalogue data using semantic technologies and ontologies. In this thesis, we introduce marimba-framework, an ontologybased library data framework, that encompasses these constructs, languages, models and methods. The specific contributions of this work include: • marimba-datamodel, a nested relational model to represent library data sources, which can be operated using a recursive relational algebra. • A query language, marimba-sql, which can be used to query and operate with library data in the marimba-datamodel. • The syntax and operational semantics of a mapping language, marimbarml, for mapping and transforming library data sources into RDF. • Methodological guidelines for library ontology development and mapping library data sources into RDF, as well as the design, implementation and publication of a large library ontology, the BNE ontology. • A method for extracting latent topics from ontologies and understanding thematical relationships between ontologies, as well as several topicbased ontology similarity measures. • An in-depth study of the impact of semantic technologies in online library applications with regards to efficiency, usability and user satisfaction measures by comparing two large-scale library systems.

As an important data structure model, ontology has become one of the core contents in information science. Multi-dividing ontology algorithm combines the advantages of graph structure and learning algorithms proved to have high efficiency. In this paper, in terms of multi-dividing proper loss functions, we propose new multi-dividing ontology learning algorithms for similarity measure and ontology mapping construction. Several theoretical statistical characteristics supporting the new learning model are given. Finally, four experiments on different scientific fields verify that our multi-dividing ontology algorithm has high accuracy and efficiency in application implements.