Ontology Instances Research Articles

Ontology-based executable design decision template representation and reuse

AbstractIn decision-based design, the principal role of a designer is to make decisions. Decision support is crucial to augment this role. In this paper, we present an ontology that provides decision support from both the “construct” and the “information” perspectives that address the gap that existing research focus on these two perspectives separately and cannot provide effective decision support. The decision support construct in the ontology is the compromise decision support problem (cDSP) that is used to make multiobjective design decisions. The information for decision making is archived as cDSP templates and represented using frame-based ontology for facilitating reuse, consistency maintaining, and rapid execution. In order to facilitate designers’ effective reuse of the populated cDSP templates ontology instances, we identified three types of modification that can be made when design consideration evolves. In our earlier work, part of the utilization (consistency checking) of the ontology has been demonstrated through a thin-walled pressure vessel redesign example. In this paper, we comprehensively present the ontology utilization including consistency checking, trade-off analysis, and design space visualization based on the pressure vessel example.

$$\uptau $$ τ OWL: A Systematic Approach to Temporal Versioning of Semantic Web Ontologies

The W3C OWL 2 recommendation is an ontology language for the Semantic Web. It allows defining both schema (i.e., entities, axioms, and expressions) and instances (i.e., individuals) of ontologies. However, OWL 2 lacks explicit support for time-varying schema or for time-varying instances. Hence, knowledge engineers or maintainers of semantics-based Web resources have to use ad hoc techniques to specify OWL 2 time-varying ontologies. In this paper, for a disciplined and systematic approach to the temporal management of Semantic Web ontologies, we propose the adoption of a framework called temporal OWL 2 (\(\uptau \)OWL), which is inspired by the \(\uptau \)XSchema framework defined for XML data. In a way similar to what happens in \(\uptau \)XSchema, \(\uptau \)OWL allows creating a temporal OWL 2 ontology from a conventional (i.e., non-temporal) OWL 2 ontology and a set of logical and physical annotations. Logical annotations identify which elements of the ontology can vary over time; physical annotations specify how the time-varying aspects are represented in the OWL 2 document. Using annotations to integrate temporal aspects in the traditional Semantic Web, our framework (1) guarantees logical and physical data independence for temporal schemas and (2) provides a low-impact solution, since it requires neither modifications of existing Semantic Web ontologies, nor extensions to the OWL 2 recommendation and Semantic Web standards. Moreover, since the conventional schema and annotation documents could evolve over time to respond to new applications’ requirements, \(\uptau \)OWL supports temporal schema versioning by allowing changing these components and by keeping track of their evolution through the conventional schema versions and annotation document versions, respectively. Two complete sets of operations are proposed for changing the conventional schema and annotation documents; to complete the figure, a set of operations is also introduced for updating temporal schema which must be changed consequently each time one of the mentioned components evolves over time. To show the feasibility of our approach, a prototype tool, named \(\uptau \)OWL-Manager, is presented.

Ontology Instance Linking: Towards Interlinked Knowledge Graphs

Due to the decentralized nature of the Semantic Web, the same real-world entity may be described in various data sources with different ontologies and assigned syntactically distinct identifiers. In order to facilitate data utilization and consumption in the Semantic Web, without compromising the freedom of people to publish their data, one critical problem is to appropriately interlink such heterogeneous data. This interlinking process is sometimes referred to as Entity Coreference, i.e., finding which identifiers refer to the same real-world entity. In this paper, we first summarize state-of-the-art algorithms in detecting such coreference relationships between ontology instances. We then discuss various techniques in scaling entity coreference to large-scale datasets. Finally, we present well-adopted evaluation datasets and metrics, and compare the performance of the state-of-the-art algorithms on such datasets.

A Method for Mapping Sensor Data to SSN Ontology

Along with the continuous development of the sensor network technology, sensors from all over the world are constantly producing sensor data. However, the sensor data from different source is hard to work together for lack of semantic. Fortunately, SSN ontology provide a way to represent sensor data semantically, but how to transform sensor data into the instance of SSN ontology conveniently is still an issue to be considered. This paper proposed a solution to map sensor data to SSN ontology automatically based on a predefined XML-based document. We design a mapping language SASML (Sensors Annotation and Semantic Mapping Language) which provide a schema to annotate sensors and sources so as to generate a XML document for mapping. Then, an algorithm (namely SDRM) is designed to automatically transform sensor data, which described by SASML, to RDF conforming to SSN ontology, according to the mapping document and the element correspondences between the SASML and SSN ontology. Further, a case study about sensor data from greenhouse is presented to illustrate our method, and a prototype is also developed to demonstrate the feasibility and effectiveness.

An automatic method for the enrichment of DICOM metadata using biomedical ontologies.

This work is a novel contribution for enriching medical images using semantic annotations with a strategy for unifying different ontologies and instances of DICOM medical files. We present the L-MOM library (Library for Mapping of Ontological Metadata) as a tool for making an automatic mapping between instances of DICOM medical files and different medical ontologies (e.g., FMA, RadLex, MeSH). The main contributions are: i) the domain independent L-MOM library which is able to integrate DICOM metadata with ontologies from different domains; ii) a strategy to automatically annotate DICOM data with universally accepted medical ontologies, and provide values of similarity between ontologies and DICOM metadata; and iii) a framework to traverse ontological concepts that characterized clinical studies of patients registered in the framework catalog.

MOnCa2: 지능형 스마트폰 어플리케이션을 위한 사용자 이동 행위 인지와 경로 예측 기반의 고수준 콘텍스트 추론 프레임워크

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user's physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user's physical context, infer basic context regarding the user's travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user's travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

Addressing semantic heterogeneity through multiple knowledge base assisted merging of domain-specific ontologies

With the development of the Semantic Web (SW), the creation of ontologies to formally conceptualize our understanding of various domains has widely increased in number. However, the conceptual and terminological differences (a.k.a semantic heterogeneity problem) between ontologies form a major limiting factor towards their use/reuse and full adoption in practical settings. A key solution to addressing this problem can be through identifying semantic correspondences between the entities (including concepts, relations, and instances) of heterogeneous ontologies, and consequently achieving interoperability between them. This process is also known as ontology alignment. The output of this process can be further exploited to merge ontologies into a single coherent ontology. Indeed, this is widely regarded as a crucial, yet difficult task, specifically when dealing with heavyweight ontologies that consist of hundreds of thousands of concepts. To address this issue, various ontology merging approaches have been proposed. These approaches can be classified into three categories: single-strategy-based approaches, multiple-strategy-based approaches, and approaches based on exploiting external semantic resources. In this paper, we first discuss the strengths and limitations of each of these approaches, and then present our framework for addressing the semantic heterogeneity problem through merging domain-specific ontologies based on multiple external semantic resources. The novelty of the proposed approach is mainly based on employing knowledge represented by multiple external resources (knowledge bases in our work) to make aggregated decisions on the semantic correspondences between the entities of heterogeneous ontologies. Other important issues that we attempt to tackle in the proposed framework are: (i) Identifying and handling inconsistency of semantic relations between the ontology concepts and, (ii) Handling the issue of missing background knowledge (such as concepts and instances) in the exploited knowledge bases by utilizing an integrated statistical and semantic technique. Additionally, the proposed solution soundly enriches the knowledge bases with missing background knowledge, and thus enables the reuse of the newly obtained knowledge in future ontology merging tasks. To validate our proposal, we tested the framework using the OAEI 2009 benchmark and compared the produced results with state-of-the-art syntactic and semantic based systems. In addition, we utilized the proposed techniques to merge three heavyweight ontologies from the environmental domain.

Multi-Hierarchy Relevance Model of Cloud Manufacturing Resource Based on OWL

Aiming at the diversity and heterogeneity of manufacturing resources in cloud manufacturing, the multi-hierarchy relevance model (MHRM) based on category and environmental level was proposed. Moreover, the OWL was used to describe the cloud manufacturing resources. Finally, cloud manufacturing resource ontology instance was built by Protégé.

Construction of XBRL Semantic Metamodel and Knowledge Base Based on Ontology

In order to improve the interoperability of XBRL format financial reporting on the semantic level, a novel XBRL financial reporting metamodel and a fact data semantic metamodel are proposed, which uses the Semantic Web technologies and Ontology theory. Then, a XBRL knowledge base is constructed based on this metamodel .Using the metamodel-based translation mechanism from XBRL to OWL / RDF, all the semantic information in XBRL taxonomy and instance documents is translated into OWL ontology and RDF instance. Finally, a knowledge base covering the semantic information of financial reporting domain is constructed.

Case-based context ontology construction using fuzzy set theory for personalized service in a smart home environment

To provide context-based personalized services utilizing smart appliances in a smart home environment, we propose a framework for PersonAlized Service disCovery Using FuZZY-based CBR and Context Ontology (PASCUZZY). Basically, the PASCUZZY framework is implemented on case-based context ontology. To generate and manage the case instances on the case-based context ontology, we adopt the fuzzy set theory to transpose numerical-type context data sensed from the surrounding environment. The context is transposed to linguistic-type context instances on the context ontology. In addition, to formalize and manage the context and services as multi-attributed data, the context ontology was developed reflecting the structure of cases borrowed from case-based reasoning. Furthermore, we propose adaptation methods to adjust the generic fuzzy membership functions depending on the inhabitants' context. It is performed by modifying the values of the membership number and/or modifying the numbers of the linguistic terms that are based on the inhabitants' context to affect the membership numbers. The adapted membership functions return the personalized degree of memberships depending on the specialized context of a specific fuzzy variable. Inevitably, the number of cases on the case-based context ontology will be increased from time to time. We apply Ward's method not only to reduce the search effort via a hierarchical clustering on the case-based context ontology but also to find the most similar service as a solution to the new context. To verify the superiority of the PASCUZZY framework, we perform two kinds of evaluations. First, we evaluate the effectiveness of the adaptation of the fuzzy membership functions. Second, we verify the effectiveness of the application of a clustering method to the case instances of the case-based context ontology to identify the most similar service. Results of the experiment verified the effectiveness and superiority of the PASCUZZY framework.

An Efficient and Scalable Approach for Ontology Instance Matching

Ontology instance matching is a key interoperability enabler across heterogeneous data resources in the Semantic Web for integrating data semantically. Although most of the research has been emphasized on schema level matching so far, research on ontology matching is shifting from ontology schema or concept level to instance level to fulfill the vision of “Web of Data”. Ontology instances define data semantically and are kept in knowledge base. Since, heterogeneous sources of massive ontology instances grow sharply day-by-day, scalability has become a major research concern in ontology instance matching of semantic knowledge bases. In this study, we propose a method by filtering instances of knowledge base into two stages to address the scalability issue. First stage groups the instances based on the relation of concepts and next stage further filters the instances based on the properties associated to instances. Then, our instance matcher works by comparing an instance within a classification group of one knowledge base against the instances of same sub-group of other knowledge base to achieve interoperability. We experiment our proposed method with several benchmark data sets namely OAEI-2009, OAEI-2010 and OAEI-2011. On comparison with other baseline methods, our proposed method shows satisfactory result.

An Improved Method for Ontology Instances Similarity Computation

An Improved Method for Ontology Instances Similarity Computation

Datatype evolution on RDF ontologies

Semantic web ontologies are not static but evolve as the understanding of the domain or the domain itself grows or evolves. This evolution is usually independent of the ontological instance descriptions which are stored in knowledge bases KBs. Therefore, the effects of an ontology update on instance descriptions which are migrated to a new ontology version should be studied. In this paper, we are interested in the effects of the migration of literal-valued instance triples of an RDF KB to a new ontology version. Note that the datatype of the property of such an instance description may have evolved in the new ontology version, e.g., from integer to decimal or from float to double. We manage the quality of these migrated instance triples and design flexible interactive methods through which the curator of the KB can intervene to correct the 'inaccuracies' of the literal values that are incurred by the migration process. In addition, specialised algorithms are presented, which are highly efficient, since their time and space complexity is linear with respect to the size of the literal-valued instance triples of the KB. Our work is particularly important to e-science applications that are commonly curated and have many datatype properties.

A domain-independent process for automatic ontology population from text

Ontology Population looks for instantiating the constituent elements of an ontology, like properties and non-taxonomic relationships. Manual population by domain experts and knowledge engineers is an expensive and time consuming task. Fast ontology population is critical for the success of knowledge-based applications. Thus, automatic or semi-automatic approaches are needed. This work proposes a generic process approaching the Automatic Ontology Population problem by specifying its phases and the techniques used to perform the activities on each phase. The main contribution of the work here described is a domain-independent process for the automatic population of ontologies from text that applies natural language processing and information extraction techniques to acquire and classify ontology instances. This is a new approach for automatic ontology population that uses an ontology to automatically generate rules to extract instances from text and classify them in ontology classes. These rules can be generated from ontologies of any domain, making the proposed process domain-independent and therefore, allowing the instantiation of ontologies quickly and at a low cost. Four experiments using a legal and a tourism corpora were conducted in order to evaluate the proposed process. Results indicate that this approach can extract and classify instances with high effectiveness with the additional advantage of domain independence. Some techniques representing the state of the art of this field are also described along with the solutions they adopt for each phase of the Automatic Ontology Population process with their advantages and limitations.

Implementation of Ontology Learning and Population System from Structured Data Sources: Standard-based Approach

This paper proposes an ontology learning and population model from structured data sources. Recently various attempts have been made to harmonize Web 2.0 and the Semantic Web, named as Web 3.0 or Web 4.0. One of the most important issues for realization of the next Web platform is about how to make Web ontology rich as well as to address semantic interoperability between ontologies. To resolve those issues, Web ontology schemas should be precisely defined in semantic aspect and we should also develop a method for learning and population of ontology instances from diverse resources. This paper proposes a model which enables the ontology learning and population from structured data sources. The proposed model in this paper is a standard-based approach. In other words, our proposal is based on ISO/IEC 11179 - Metadata Registry, which is one of the international standards. This standard has been developed to support the interoperability between data by managing standardized common concepts. Therefore, our proposed model enhances the semantic interoperability between ontologies and enables the ontology enrichment from data sources.

An exploration of the digital library evaluation literature based on an ontological representation

Evaluation is a vital research area in the digital library domain, demonstrating a growing literature in conference and journal articles. We explore the directions and the evolution of evaluation research for the period 2001–2011 by studying the evaluation initiatives presented at 2 main conferences of the digital library domain, namely the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers (ACM/IEEE) Joint Conference on Digital Libraries (JCDL), and the European Conference on Digital Libraries (ECDL; since 2011 renamed to the International Conference on Theory and Practice of Digital Libraries [TPDL]). The literature is annotated using a domain ontology, named DiLEO, which defines explicitly the main concepts of the digital library evaluation domain and their correlations. The ontology instances constitute a semantic network that enables the uniform and formal representation of the critical evaluation constructs in both conferences, untangles their associations, and supports the study of their evolution. We discuss interesting patterns in the evaluation practices as well as in the research foci of the 2 venues, and outline current research trends and areas for further research.

Curating the Specificity of Ontological Descriptions under Ontology Evolution

Semantic Web Ontologies are not static, but evolve, however, this evolution usually happens independently of the ontological instance descriptions (usually referred as “metadata”) which are stored in the various Metadata Repositories or Knowledge Bases. Nevertheless, it is a common practice for a MR/KB to periodically update its ontologies to their latest versions by “migrating” the available instance descriptions to the latest ontology versions. Such migrations incur gaps regarding the specificity of the migrated metadata, i.e. inability to distinguish those descriptions that should be reexamined (for possible specialization as consequence of the migration) from those for which no reexamination is justified. Consequently, there is a need for principles, techniques, and tools for managing the uncertainty incurred by such migrations, specifically techniques for (a) identifying automatically the descriptions that are candidates for specialization, (b) computing, ranking and recommending possible specializations, and (c) flexible interactive techniques for updating the available descriptions (and their candidate specializations), after the user (curator of the repository) accepts/rejects such recommendations. This problem is especially important for curated knowledge bases which have increased quality requirements (as in E-science). In this paper, we formalize the problem and propose principles, rules, and algorithms for realizing the aforementioned scenario, for the RDF/S framework. Finally, we report experimental results demonstrating the feasibility of the approach.

Large scale instance matching via multiple indexes and candidate selection

Instance matching aims to discover the linkage between different descriptions of real objects across heterogeneous data sources. With the rapid development of Semantic Web, especially of the linked data, automatically instance matching has been become the fundamental issue for ontological data sharing and integration. Instances in the ontologies are often in large scale, which contains millions of, or even hundreds of millions objects. Directly applying previous schema level ontology matching methods is infeasible. In this paper, we systematically investigate the characteristics of instance matching, and then propose a scalable and efficient instance matching approach named VMI. VMI generates multiple vectors for different kinds of intained in the ontology instances, and uses a set of inverted indexes based rules to get the primary matching candidates. Then it employs user customized property values to further eliminate the incorrect matchings. Finally the similarities of matching candidates are computed as the integrated vector distances and the matching results are extracted. Experiments on instance track from OAEI 2009 and OAEI 2010 show that the proposed method achieves better effectiveness and efficiency (a speedup of more than 100 times and a bit better performance (+3.0% to 5.0% in terms of F1-score) than top performer RiMOM on most of the datasets). Experiments on Linked MDB and DBpedia show that VMI can obtain comparable results with the SILK system (about 26,000 results with good quality).

Domain-Independent Entity Coreference for Linking Ontology Instances

The objective of entity coreference is to determine if different mentions (e.g., person names, place names, database records, ontology instances, etc.) refer to the same real word object. Entity coreference algorithms can be used to detect duplicate database records and to determine if two Semantic Web instances represent the same underlying real word entity. The key issues in developing an entity coreference algorithm include how to locate context information and how to utilize the context appropriately. In this article, we present a novel entity coreference algorithm for ontology instances. For scalability reasons, we select a neighborhood of each instance from an RDF graph. To determine the similarity between two instances, our algorithm computes the similarity between comparable property values in the neighborhood graphs. The similarity of distinct URIs and blank nodes is computed by comparing their outgoing links. In an attempt to reduce the impact of distant nodes on the final similarity measure, we explore a distance-based discounting approach. To provide the best possible domain-independent matches, we propose an approach to compute the discriminability of triples in order to assign weights to the context information. We evaluated our algorithm using different instance categories from five datasets. Our experiments show that the best results are achieved by including both our discounting and triple discrimination approaches.

Ontology-Based Information Extraction of Crop Diseases on Chinese Web Pages

This paper proposes a method for extracting information of crop diseases on Chinese web pages. First, we define some special labels of the DOM tree[1] to partition the web page into some content blocks. Then the noise content in the web pages is eliminated according to the location and the word number of a content block. We employ an ontology-based way to implement information extraction from the content blocks. A top-down method is adopted to construct the ontology of crop diseases. In the extraction process, the concepts, relations and instances of ontology is used to extract the entities. The event is extracted by an optimal classification of paragraph groups in a content block. Experiments demonstrate the performance of the proposed method is satisfactory.