The large-scale analysis of scholarly artifact usage is constrained primarily by current practices in usage data archiving, privacy issues concerned with the dissemination of usage data, and the lack of a practical ontology for modeling the usage domain. As a remedy to the third constraint, this article presents a scholarly ontology that was engineered to represent those classes for which large-scale bibliographic and usage data exists, supports usage research, and whose instantiation is scalable to the order of 50 million articles along with their associated artifacts (e.g. authors and journals) and an accompanying 1 billion usage events. The real world instantiation of the presented abstract ontology is a semantic network model of the scholarly community which lends the scholarly process to statistical analysis and computational support. We present the ontology, discuss its instantiation, and provide some example inference rules for calculating various scholarly artifact metrics.
The ABC ontology @cite_25 was primarily engineered as a common conceptual model for the interoperability of a variety of metadata ontologies from different domains. Although the ABC ontology is able to represent bibliographic and usage concepts by means of constructs such as artifact (e.g. article), agent (e.g. author), and action (e.g. use), it is designed at a level of generality that does not directly support the granularity required by the MESUR project.
@cite_25
1552027408 1598795547 1911282740 2137079713
This paper describes the latest version of the ABC metadata model. This model has been developed within the Harmony international digital library project to provide a common conceptual model to facilitate interoperability between metadata vocabularies from different domains. This updated ABC model is the result of collaboration with the CIMI consortium whereby earlier versions of the ABC model were applied to metadata descriptions of complex objects provided by CIMI museums and libraries. The result is a metadata model with more logically grounded time and entity semantics. Based on this model we have been able to build a metadata repository of RDF descriptions and a search interface which is capable of more sophisticated queries than less-expressive, object-centric metadata models will allow. Geographic information is based on observations or measurements. The Open Geospatial Consortium (OGC) has developed an implementation specification for observations and measurements (O&M). It specifies precisely how to encode information. Yet, the O&M conceptual model does not specify precisely which real-world entities are denoted by the specified information objects. We provide formal semantics for the central O&M terms by aligning them to the foundational ontology DOLCE. The alignment to a foundational ontology restricts the possible interpretations of the central elements in the O&M model and establishes explicit relations between categories of real world entities and classes of information objects. These relations are essential for assessing semantic interoperability between geospatial information sources. There has been an explosion in the types, availability and volume of data accessible in an information system, thanks to the World Wide Web (the Web) and related inter-networking technologies. In this environment, there is a critical need to replace or complement earlier database integration approaches and current browsing and keyword-based techniques with concept-based approaches. Ontologies are increasingly becoming accepted as an important part of any concept or semantics based solution, and there is increasing realization that any viable solution will need to support multiple ontologies that may be independently developed and managed. In particular, we consider the use of concepts from pre-existing real world domain ontologies for describing the content of the underlying data repositories. The most challenging issue in this approach is that of vocabulary sharing, which involves dealing with the use of different terms or concepts to describe similar information. In this paper, we describe the architecture, design and implementation of the OBSERVER system. Brokering across the domain ontologies is enabled by representing and utilizing interontology relationships such as (but not limited to) synonyms, hyponyms and hypernyms across terms in different ontologies. User queries are rewritten by using these relationships to obtain translations across ontologies. Well established metrics like precision and recall based on the extensions underlying the concepts are used to estimate the loss of information, if any. Abstract To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.