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InBiodiv-O: An Ontology for Indian Biodiversity Knowledge Management

arXiv.org Artificial Intelligence

To present the biodiversity information, a semantic model is required that connects all kinds of data about living creatures and their habitats. The model must be able to encode human knowledge for machines to be understood. Ontology offers the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in the biodiversity domain. Various ontologies are developed for the biodiversity domain however a review of the current landscape shows that these ontologies are not capable to define the Indian biodiversity information though India is one of the megadiverse countries. To semantically analyze the Indian biodiversity information, it is crucial to build an ontology that describes all the essential terms of this domain from the unstructured format of the data available on the web. Since, the curation of the ontologies heavily depends on the domain where these are implemented hence there is no ideal methodology is defined yet to be ready for universal use. The aim of this article is to develop an ontology that semantically encodes all the terms of Indian biodiversity information in all its dimensions based on the proposed methodology. The comprehensive evaluation of the proposed ontology depicts that ontology is well built in the specified domain.


SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators

arXiv.org Artificial Intelligence

The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modeling the interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN's Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on \emph{Spatial Data on the Web}. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC's Observations and Measurements (O\&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modeling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at \url{https://www.w3.org/TR/vocab-ssn/}.


Best Practices for Implementing FAIR Vocabularies and Ontologies on the Web

arXiv.org Artificial Intelligence

With the adoption of Semantic Web technologies, an increasing number of vocabularies and ontologies have been developed in different domains, ranging from Biology to Agronomy or Geosciences. However, many of these ontologies are still difficult to find, access and understand by researchers due to a lack of documentation, URI resolving issues, versioning problems, etc. In this chapter we describe guidelines and best practices for creating accessible, understandable and reusable ontologies on the Web, using standard practices and pointing to existing tools and frameworks developed by the Semantic Web community. We illustrate our guidelines with concrete examples, in order to help researchers implement these practices in their future vocabularies.


LB2CO: A Semantic Ontology Framework for B2C eCommerce Transaction on the Internet

arXiv.org Artificial Intelligence

Business ontology can enhance the successful development of complex enterprise system; this is being achieved through knowledge sharing and the ease of communication between every entity in the domain. Through human semantic interaction with the web resources, machines to interpret the data published in a machine interpretable form under web. However, the theoretical practice of business ontology in eCommerce domain is quite a few especially in the section of electronic transaction, and the various techniques used to obtain efficient communication across spheres are error prone and are not always guaranteed to be efficient in obtaining desired result due to poor semantic integration between entities. To overcome the poor semantic integration this research focuses on proposed ontology called LB2CO, which combines the framework of IDEF5 & SNAP as an analysis tool, for automated recommendation of product and services and create effective ontological framework for B2C transaction & communication across different business domains that facilitates the interoperability & integration of B2C transactions over the web.


Ontologies in CLARIAH: Towards Interoperability in History, Language and Media

arXiv.org Artificial Intelligence

One of the most important goals of digital humanities is to provide researchers with data and tools for new research questions, either by increasing the scale of scholarly studies, linking existing databases, or improving the accessibility of data. Here, the FAIR principles provide a useful framework as these state that data needs to be: Findable, as they are often scattered among various sources; Accessible, since some might be offline or behind paywalls; Interoperable, thus using standard knowledge representation formats and shared vocabularies; and Reusable, through adequate licensing and permissions. Integrating data from diverse humanities domains is not trivial, research questions such as "was economic wealth equally distributed in the 18th century?", or "what are narratives constructed around disruptive media events?") and preparation phases (e.g. data collection, knowledge organisation, cleaning) of scholars need to be taken into account. In this chapter, we describe the ontologies and tools developed and integrated in the Dutch national project CLARIAH to address these issues across datasets from three fundamental domains or "pillars" of the humanities (linguistics, social and economic history, and media studies) that have paradigmatic data representations (textual corpora, structured data, and multimedia). We summarise the lessons learnt from using such ontologies and tools in these domains from a generalisation and reusability perspective.