ENHANCING OWL ONTOLOGIES WITH RELATION SEMANTICS

AIERO: An algorithm for identifying engineering relationships in ontologies

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

In the context of Semantic Web Search Engines is becoming crucial to study relations between ontologies to improve the ontology selection task. In this paper, we describe DOOR - The Descriptive Ontology of Ontology Relations, to represent, manipulate and reason upon relations between ontologies in large ontology repositories. DOOR represents a first attempt in describing and formalizing ontology relations. In fact, it does not pretend to be a universal standard structure. Rather, It is intended to be a flexible, easily modifiable structure to model ontology relations in the context of ontology repositories. Here, we provide a detailed description of the methodology used to design the DOOR ontology, as well as an overview of its content. We also describe how DOOR is used in a complete framework (called KANNEL) for detecting and managing semantic relations between ontologies in large ontology repositories. Applied in the context of a large collection of automatically crawled ontologies, DOOR and KANNEL provide a starting point for analyzing the underlying structure of the network of ontologies that is the Semantic Web.

Geographic information integration and fusion from multi-sources geospatial data is still a challenge because of semantic heterogeneity of geospatial entity data. These will delay geospatial cooperation decision support and integrated assessment application for natural resources and environmental problem. Ontology as a conceptualization and knowledge representation of application domain have provided a potential and aided support for entity matching and information integration of multiple sources and heterogeneous data. However, many data sources may not provided ontology definition and formalization representation. This paper will provide a method of graph model based ontologies representation and ontologies learning from data sources schema. Entity matching and fusion with ontology support is also discussed. This paper has classified unary properties and binary properties in ontology representation. Binary semantic relations in ontologies are analyzed. Based on semantic relations, the Tree model and Network model of ontology representation are defined. Through analyzing entity data schema, semantic relations extraction and learning are also discussed in constitution of entity ontologies. The framework of entity fusion with ontology support has been designed. Finally entity matching and fusion methods are discussed.

This article presents a relations taxonomy constructed on the basis of a conceptual framework, as well as methods of identifying semantic relations that are used for automatically constructing ontologies of subject areas. A predicate lexicon that corresponds to relation types is established through automatically processing a collection of scientific and technical document texts. Patterns of linguistic constructions of semantic relations are constructed in a way that considers morphological features of words and morphemes. Statistics of types of relations and the use of patterns depending on the document type are studied.

This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the Semantic Modeling Framework, which represents environments using layered structures of places, objects, and their ontological relations.

Ontology is the basis of sharing and reusing knowledge on the Semantic Web, and ontology-based semantic retrieval is a hotspot of current research. Fuzzy ontology is an extension of domain ontology for solving the uncertainty problems. To represent fuzzy knowledge more effectively, this paper presents a new series of fuzzy ontology models that consists of fuzzy domain ontology and fuzzy linguistic variable ontologies, considering semantic relationships of concepts, including set relation, order relation, equivalence relation and semantic association relation etc. The process to construct linguistic variables ontology is discussed. Using ontology and RDFS, the knowledge model for product information is created. To achieve semantic retrieval, the semantic query expansion in SeRQL is constructed by semantic relations between fuzzy concepts. The application shows that these models can overcome the localization of other fuzzy ontology models, and this research facilitates the fuzzy knowledge sharing and semantic retrieval on the Semantic Web.

This chapter handles assessment of the innermost layer of the Semantic Web cake, i.e., ontology, through a metrics suite called OntoMod. We assess the syntactical composition of ontology, in addition to its behavioral aspect in a Semantic Web application. Firstly, novel cohesion and coupling metrics have been discussed, which are found to be superior in terms of accuracy and expressive power than existing cohesion and coupling metrics, on sample ontologies. Furthermore, by using continuous scale of measurement, and tackling indirect relationships in ontology, our cohesion and coupling metrics can account for subtle dissimilarities among different modularizations of the same ontology. Hence, a key role of OntoMod is to help the ontology engineer in choosing the most suitable modularization of ontology according to fitness for (re)use, regardless of its type and size. Secondly, improved metrics for structural complexity of an ontology have been formulated. Finally, a few crucial metrics for behavioral assessment of ontology have been adapted from the literature and incorporated in the OntoMod suite. The only precondition for assessing structural and behavioral quality of modular ontology through the OntoMod suite is the availability of partial or complete ontology visualization. This chapter validates each of the constituent OntoMod metrics, either practically on real-world ontologies or theoretically through benchmark validation frameworks.

Effective ranking and search techniques for Web resources considering semantic relationships

Matching large ontologies: A divide-and-conquer approach

This thesis investigates the question whether and how ontologies such as the ones currently evolving in the Semantic Web can serve as knowledge structures for the generation of query expansion terms in information retrieval systems. This issue is examined using a specific example domain, namely educational research. Initial results support the already well-researched finding that query expansion can increase recall. Subsequent experiments will focus on comparing the effectiveness of thesaurus and ontology-based query expansion, on identifying ontological relationships which are especially useful for the generation of query expansion terms in the domain of educational research, and on evaluating the usefulness of different ontological relationships as expansion terms for different types of queries, as well as for different query expansion modes.

Aiming at the problems of low resource utilisation rate, low model reliability and poor feasibility of current network resource sharing model, this paper proposes and constructs a sharing model for network digital teaching resource based on semantic web. The game model is used to analyse the behaviour evolution of teaching resource sharing model. Based on ontology relationship, a general framework of metadata for teaching resources is constructed to realise interoperability between metadata. Taking XML as the basic grammar, according to the details of the application of teaching resources, the related application based on XML is created, and the sharing of network digital teaching resources is realised by defining the pattern layer by layer. The experimental results show that the utilisation rate of teaching resources under this model is high, and the fitting degree between the model and the actual situation is high.

Aiming at the problems of low resource utilisation rate, low model reliability and poor feasibility of current network resource sharing model, this paper proposes and constructs a sharing model for network digital teaching resource based on semantic web. The game model is used to analyse the behaviour evolution of teaching resource sharing model. Based on ontology relationship, a general framework of metadata for teaching resources is constructed to realise interoperability between metadata. Taking XML as the basic grammar, according to the details of the application of teaching resources, the related application based on XML is created, and the sharing of network digital teaching resources is realised by defining the pattern layer by layer. The experimental results show that the utilisation rate of teaching resources under this model is high, and the fitting degree between the model and the actual situation is high.

This paper presents a common framework for the conceptual information of MPEG-7, interoperable with the semantic Web, and providing high performance search capabilities, using fuzzy set theory and ontology relations. Starting from the meta-data model of the MPEG-7, to which we add few layers while keeping the existent, so as to guarantee compatibility with the standard, we then link MPEG-7 descriptions to ontologies. These ontologies are used to define precisely each element used in the description scheme of MPEG-7. In order to allow similarities estimations and performing searches, the model is projected into a 5-dimensional space that permits to describe easily the content of multimedia documents.

Constitute is a web application developed for the analysis of the text of written constitutions. At the heart of the application is a set of constitutional excerpts that are encoded with tags derived from a conceptual inventory used in the Comparative Constitutions Project (CCP), a related dataset that records the characteristics of national constitutions since 1789. The data collection deposited with QDR is a complete set of excerpts, together with topical tags, from in-force constitutions, as well as a few draft and historical constitutions, appearing on the site as of December 26, 2017. Data Abstract The data include the constitutions of nearly all independent states, as identified by Ward and Gledistch, in force as of December 26, 2017. Constitute’s conceptual organization was informed by its intended use as a public-facing data product. Since Constitute is intended to be usable for members of the public as well as real-life constitution-writers and academics, the project’s texts, content tags, and conceptual inventory are all organized with an eye towards readability and parsimony. This guiding principle is most noticeable in the dataset’s conceptual inventory, which covers roughly half as many items as the original Comparative Constitutions Project (CCP) variable set. Constitute’s hierarchical variable organization — enabled by the Semantic Web technology underlying the project — is informed by similar concerns. To enhance usability (both visually on the project’s website and in downstream statistical applications), Constitute’s conceptual hierarchy is oriented in a shallow but wide fashion, with few hierarchical layers and many sub-categories at each layer. In compiling data for QDR, we included all information which is available on the Constitute website at the time of submission. These data consist of Semantic Web (.nt) files that include excerpts from nearly all in-force constitutions as of 2017, with accompanying content tags. The data follow a conceptual framework developed by the Principal Investigators in the Comparative Constitutions Project (CCP), a related dataset that records some 600 characteristics of national constitutions enacted since 1789. Documentation and data for that project, including data and texts for older constitutions, are available on the project website, at https://comparativeconstitutionsproject.org . Data Model The textual excerpts are derived from the CCP’s core dataset, which records a comprehensive set of characteristics of historical constitutions. The Constitute dataset consists of two main pieces: (1) a set of conceptual tags and a related conceptual ontology; and (2) a collection of cleaned and tagged constitutional texts. Constitute tracks some 300 unique conceptual tags at the paragraph level of each individual constitution, which are organized into a hierarchical, extensible ontology. Example tags might include “Free Expression,” “Head of State Powers,” or “National Motto.” The Constitute conceptual inventory is available in QDR as an OWL file, a graph-based Semantic Web format that allows for formal descriptions of hierarchical classification schemes. In accordance with the general Semantic Web framework, OWL files can be easily expanded to include new categories and relationships, allowing researchers to extend the Constitute coding scheme for their own purposes. Each constitutional text in Constitute is represented in a related hierarchical format. Currently, the QDR sample consists of nearly all in-force national constitutions as of 2017, as well as a few draft and historical texts. Files Description The data collection deposited with QDR is a complete set of excerpts -- together with topical tags – as they appeared on the site as of December 26, 2017. Metadata about each constitution is also provided (as an .nt file), along with the project’s conceptual inventory (as an .owl file) and copyright information (as a spreadsheet).