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STRUCTURAL ANALYSIS OF SEMANTIC RELATIONS REGARDING INTEGRATION AND ASSOCIATION OF SEMANTIC NETWORK IN VOCBENCH AS AN AGRICULTURAL ONTOLOGY
Abstract
The purpose of this article is to analyze semantic relations based on graph-independent structural analysis in VocBench. The mix-method of deductive and inductive approach is adapted in operating the research methodology, especially for data collection. The research data are structural domains of semantic relations in ontologies. The data resource is the authoritative agricultural ontology, VocBench, that has been originated by Food and Agricultural organization (FAO), United Nation. VocBench includes around 40000 concepts. The sample size is around 1500 concepts. Sampling technique used is the stratified random sampling. The data analysis results are employed in the SPSS and Excel software using descriptive and proportional analysis. The research results reveal that the taxonomic relations cover a wide area in VocBench. Moreover, the overloading was not seen in the usage of non-taxonomic relations. The high frequency in the usage of the semantic relations' output might be implied the possibility of the width (i.e., exhaustivity) in semantic network in VocBench.
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Background
Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies’ semantic content remains a challenge. ResultsWe developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies’ semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph. Conclusions
Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.
Visualizations of ontologies, in particular graph visualizations in the form of node-link diagrams, are often used to support ontology development, exploration, verification, and sensemaking. With growing size and complexity of ontology graph visualizations, their represented information tend to become hard to comprehend due to visual clutter and information overload. We present a new approach of semantic zooming for ontology graph visualizations that abstracts and simplifies the underlying graph structure. It separates the comprised information into three layers with discrete levels of detail. The approach is applied to a force-directed graph layout using the VOWL notation. The mental map is preserved by using smart expanding and ordering of elements in the layout. Navigation and sensemaking are supported by local and global exploration methods, halo visualization, and smooth zooming. The results of a user study confirm an increase in readability, visual clarity, and information clarity of ontology graph visualizations enhanced with our semantic zooming approach.
A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.
VocBench is an open source web application for editing of SKOS and SKOS-XL thesauri, with a strong focus on collaboration, supported by workflow management for content validation and publication. Dedicated user roles provide a clean separation of competences, addressing different specificities ranging from management aspects to vertical competences on content editing, such as conceptualization versus terminology editing. Extensive support for scheme management allows editors to fully exploit the possibilities of the SKOS model, as well as to fulfill its integrity constraints. We describe here the main features of VocBench, which will be shown along the demo held at the ESWC15 conference.
Objective: This part II article about the ‘words of prevention’ presents in a terminological way the content of ten current concepts used in the prevention domain which are closely linked to quaternary prevention: (1) overinformation, (2) overdiagnosis, (3) medically unexplained symptoms, (4) overmedicalisation, (5) incidentaloma, (6) overscreening, (7) overtreatment, (8) shared decision making, (9) deprescribing, and (10) disease mongering. Methods: with the support of the laboratory team of the University of Rouen, France, which is dedicated to medical terminology and semantic relationships, it was possible to utilize a graphic user interface (called DBGUI) allowing the construction of links for each of chosen terms, and making automatic links to MeSH, if any. Those concepts are analyzed in their environment in current literature, as well as in their MeSH counterparts, if any, and related semantic online terminologies. Results and Discussion: The rules in terminological development aspire to cover the whole field of a concept and in the meantime, it helps to avoid the noise due to proxy and not exactly related issues. This refers to exhaustivity and specificity in information retrieval. Our finds show that referring to MeSH only in information retrieval in General Practice/Family medicine can induce much noise and poor adequacy to the subject investigated. Conclusion: Gathering concepts in specially prepared terminologies for further development of ontologies is a necessity to enter in the semantic web area and the era of distributed data.
Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database. However, functional labels with only a few (<10) annotated genes, which constitute about half of the GO terms in yeast, mouse and human, pose a unique challenge in that any prediction algorithm that independently considers each label faces a paucity of information and thus is prone to capture non-generalizable patterns in the data, resulting in poor predictive performance. There exist a variety of algorithms for function prediction, but none properly address this 'overfitting' issue of sparsely annotated functions, or do so in a manner scalable to tens of thousands of functions in the human catalog.
We propose a novel function prediction algorithm, clusDCA, which transfers information between similar functional labels to alleviate the overfitting problem for sparsely annotated functions. Our method is scalable to datasets with a large number of annotations. In a cross-validation experiment in yeast, mouse and human, our method greatly outperformed previous state-of-the-art function prediction algorithms in predicting sparsely annotated functions, without sacrificing the performance on labels with sufficient information. Furthermore, we show that our method can accurately predict genes that will be assigned a functional label that has no known annotations, based only on the ontology graph structure and genes associated with other labels, which further suggests that our method effectively utilizes the similarity between gene functions.
https://github.com/wangshenguiuc/clusDCA.
jianpeng@illinois.eduSupplementary information: Supplementary data are available at Bioinformatics online.
© The Author 2015. Published by Oxford University Press.
Building ontology is a fundamental but also hard work. Collaborative ontology editing tools can make ontology development more efficiently. In this paper, the important features of collaborative ontology development were analyzed, and several tools such as AGROVOC Concept Server Workbench (ACSW), Collaborative Protégé and WebProtégé were studied. Besides, some comparisons among them from several aspects were made and some prospects for the further improvement of these tools were given. Finally, we show it is a good way to build agricultural ontology with these tools collaboratively and simultaneously.
Computers do not have the equivalent of a human cognitive system and therefore store data simply as the numbers and words that are entered into the computer. For a computer to interpret data it requires an information structure that provides at least some level of context. This can be accomplished utilizing an ontology of objects with characteristics, semantic behavior, and a rich set of relationships to create a virtual version of real world situations and provide the context within which intelligent logic (e.g., agents) can automatically operate. This paper discusses the process of developing ontologies that serve to provide context for agents to interpret and reason about data changes in decision-support software tools, services and systems.
In content-based filtering systems, content of items is used to recommend newi tems to the users. It is usually represented
by words in natural language where meanings of words are often ambiguous. We studied clustering of words based on their semantic
similarity. Then we used word clusters to represent items for recommending new items by content-based filtering. In the paper
we present our empirical results.
The need for evaluation-methodologies emerged very early in the field of ontology development and reuse and it has grown steadily. Yet, no comprehensive and global approach to this problem has been proposed to date. This situation may become a serious obstacle for the success of ontology-based Knowledge Technology, especially in the industrial and commercial sectors. In this paper we look at existing ontology-evaluation methods from the perspective of their integration in one single framework. Based on a catalogue of qualitative and quantitative measures for ontologies, we set up a formal model for ontology. The proposed formal model consists of a meta-ontology - O 2 - that characterizes ontologies as semiotic objects. The meta-ontology is complemented with an ontology of ontology evaluation and validation - oQual. Based on O 2 and oQual, we identify three main types of measures for ontology evaluation: structural measures, that are typical of ontologies represented as graphs; functional measures, that are related to the intended use of an ontology and of its components, i.e. their function; usability-related measures, that depend on the level of annotation of the considered ontology.
We present a comprehensive approach to ontology evaluation and validation, which have become a crucial problem for the development of semantic technologies. Existing evaluation methods are integrated into one sigle framework by means of a formal model. This model consists, firstly, of a meta-ontology called O
2, that characterises ontologies as semiotic objects. Based on O
2 and an analysis of existing methodologies, we identify three main types of measures for evaluation: structural measures, that are typical of ontologies represented as graphs; functional measures, that are related to the intended use of an ontology and of its components; and usability-profiling measures, that depend on the level of annotation of the considered ontology. The meta-ontology is then complemented with an ontology of ontology validation called oQual, which provides the means to devise the best set of criteria for choosing an ontology over others in the context of a given project. Finally, we provide a small example of how to apply oQual-derived criteria to a validation case.
The objective of the Semiotic-based Ontology Evaluation Tool (S-OntoEval) is to evaluate and propose improvements to a given on- tological model. The evaluation aims at assessing the quality of the ontology by drawing upon semiotic theory (Stamper et al., 2000), taking several metrics into consideration for assessing the syntactic, sem antic, and pragmatic aspects of ontology quality. We consider an ontology to be a semiotic object and we identify three main types of semiotic ontology evaluation levels: the structural level, assessing the ontology syntax and formal semantics; the functional level, assessing th e ontology cognitive semantics and; the usability-related level, assessing the ontology pragmatics. The Ontology Evaluation Tool implements metrics for each semiotic ontology level: on the structural level by making use of reasoner such as the RACER System (Haarselv and M¨ oller, 2001) and Pellet (Parsia and Sirin, 2004) to check the logical consistency of our ontological model (TBoxes and ABoxes) and graph-theory measures such as Depth; on the functional level by making use of a task-based evaluation approach which measures the quality of the ontology based on the adequacy of the ontological model for a specific task; and on the usability-profiling level by applying a quantitative analysis of the amount of annotation. Other metrics can be easily integrated and added to the respective evaluation leve l. In this work, the Ontology Evaluation Tool is used to test and evaluate the SWIntO Ontology of the SmartWeb project.
Existing classification schemes and thesauri are lacking in well-defined semantics and structural consistency.
Empowering end users in searching collections of ever increasing magnitudes with performance far exceeding
plain free-text searching (as used in many Web search engines), and developing systems that not only find but
also process information for action, requires far more powerful and complex knowledge organization systems
(KOSs). The paper presents a conceptual structure and transition procedure to support the shift from a traditional
KOS towards a full-fledged and semantically rich KOS. The proposed structure also complies with other
interoperability approaches like RDFS and XML in the Web environment. AGROVOC, a traditional thesaurus
developed and maintained by the Food and Agriculture Organization (FAO) of the United Nations, serves as a
case study for exploring the reengineering of a traditional thesaurus into a fully-fledged ontology. We start the
process of developing an inventory of specific relationship types with well-defined semantics for the agricultural
domain and explore the rules-as-you-go approach to streamlining the reengineering process.
Semantic distance and semantic similarity are two important information retrieval measures used in word sense disambiguation as well as for the assessment of how relevant concepts are with respect to the documents in which they are found. A variety of calculation methods have been proposed in the literature, whereby methods taking into account the information content of an individual concept outperform those that do not. In this paper, we present a novel recursive approach to calculate a concept's information content based on the information content of the concepts to which it relates. The method is applicable to extremely large ontologies containing several million concepts and relationships amongst them. It is shown that a concept's information content as calculated by this method provides additional information with respect to an ontology that cannot be approximated by hierarchical edge-counting or human insight. In addition, it is suggested that the method can be used for quality control within large ontologies and that it can give you an impression on the structure and the quality of the ontology.
In view of the need to provide tools to facilitate the reuse of existing knowledge structures such as ontologies, we present in this paper a system, AKTiveRank, for the ranking of ontologies. AKTiveRank uses as input the search terms provided by a knowledge engineer and, using the output of an ontology search engine, ranks the ontologies. We apply a number of classical metrics in an attempt to investigate their appropriateness for ranking ontologies, and compare the results with a questionnaire-based human study. Our results show that AKTiveRank will have great utility although there is potential for improvement.
The quantitative evaluation of thesauri has been carried out much further since 1976. This type of evaluation is based on counting of special factors in thesaurus structure, some of which arecounting preferred terms, non preferred terms, cross reference terms and so on. Therefore, variousstatistical tests have been proposed and applied for evaluation of thesauri. In this article, we try to explain some ratios in the field of unity quantitative evaluation in a thesaurus term network. Theoretical base of the ratios' indicators and indices construction, and epistemological thought in this type of quantitative evaluation, are discussed in this article. The theoretical base of quantitative evaluation is the epistemological thought of Immanuel Kant's Critique of pure reason.The cognition states of transcendental understanding are divided into three steps, the first is perception, the second combination and the third, relation making. Terms relation domains and conceptual relation domains can be analyzed with ratios. The use of quantitative evaluations in current research in the field of thesaurus construction prepares a basis for a restoration period. In modern thesaurus construction, traditional term relations are analyzed in detail in the form of new conceptual relations. Hence, the new domains of hierarchical and associative relations are constructed in the form of relations between concepts. The newly formed conceptual domains can be a suitable basis for quantitative evaluation analysis in conceptual relations.
Conceptual Structures (CSs) are a powerful and efficient knowledge representation technique which has many built-in features, such as subsumption of concepts, type hierarchies, and formal validation techniques. The Conceptual Structures community has put much effort into improving and expanding the definition of unification. None of the recently proposed methods, however, are able to use simple unification methods, and still guarantee that a unified graph with constrained referents represents reasonable knowledge in the domain. We define a method for the unification of CSs which contain real-value constraints in the referent of a concept. The significance of this work is that a simple unification operation, using join and type subsumption, is defined which can be used to validate the constraints over an entire unified graph.
This work describes the new method of ontology cognitive ergonomics evaluation and its automation and implementation.
Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account representation of individual ontologies, performance and accuracy on tasks for which the ontology is designed and used, degree of alignment with other ontologies and their compatibility with automated reasoning. A sound and systematic approach to ontology evaluation is required to transform ontology engineering into a true scientific and engineering discipline. This chapter discusses issues and problems in ontology evaluation, describes some current strategies, and suggests some approaches that might be useful in the future.
Conceptual graphs are a knowledge representation language designed as a synthesis of several different traditions. First are the semantic networks, which have been used in machine translation and computational linguistics for over thirty years. Second are the logic-based techniques of unification, lambda calculus, and Peirce's existential graphs. Third is the linguistic research based on Tesnière's dependency graphs and various forms of case grammar and thematic relations. Fourth are the dataflow diagrams and Petri nets, which provide a computational mechanism for relating conceptual graphs to external procedures and databases. The result is a highly expressive system of logic with a direct mapping to and from natural languages. The lambda calculus supports the definitions for a taxonomic system and provides a general mechanism for restructuring knowledge bases. With the definitional mechanisms, conceptual graphs can be used an intermediate stage between natural languages and the rules and frames of expert systems—an important feature for knowledge acquisition and for help and explanations. During the past five years, conceptual graphs have been applied to almost every aspect of AI, ranging from expert systems and natural language to computer vision and neural networks. This paper surveys conceptual graphs, their development from each of these traditions, and the applications based on them.
The Conceptual Structures community has recently shown increased interest in methods and formalisms for the use of constraints
in Conceptual Graphs (CGs), especially the definition of unification over constraints. None of the recent proposed constraint
methods, however, are able to use simple unification methods, and still guarantee that a graph which is structurally valid
under the canonical formation rules is also semantically valid in the knowledge domain. Our approach defines a method (and
concept type) for constraining real values in the referent of a concept. The significance of our work is that a simple unification
operation, using join and type subsumption, is defined which can be used to validate the constraints over an entire unified
graph. A useful side-effect is that this constraint method can also be used to define real numbers in a referent.
Immanuel Kant's Critique of Pure Reason is both one of the most rewarding of all philosophical works, and one of the most difficult. Norman Kemp Smith's translation is immensely valuable, not simply because he rendered Kant's language into readable English, but also because his own extensive understanding of the Critique made him acutely aware of the pitfalls of translation. The text followed is that of the second edition of 1787, and a translation is also given of all first-edition passages which in the second edition have been either altered or omitted. For this reissue of Norman Kemp Smith's classic translation, renowned Kant scholar Gary Banham has contributed a unique, extensive bibliography of secondary resources. Combined with Howard Caygill's context-setting introduction and guide to further reading, this reissue provides the definitive edition of this cornerstone of Western intellectual history.
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