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Upper ontology

About: Upper ontology is a research topic. Over the lifetime, 9767 publications have been published within this topic receiving 220721 citations. The topic is also known as: top-level ontology & foundation ontology.


Papers
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Proceedings ArticleDOI
27 Oct 2003
TL;DR: This paper relates domain analysis and ontologies, illustrates a step in the domain analysis method for identifying and categorizing concepts, and describes how this step, borrowed from library science, is incorporated into thedomain analysis method.
Abstract: An ontology can be defined as a conceptualization of a domain or subject area typically captured in an abstract model of how people think about things in the domain. Humans have been producing ontologies for millennia to understand and explain our rationale and environment. Only recently has the process of building ontologies become a research topic of interest. Today, ontologies are built very much ad-hoc. A terminology is first developed providing a controlled vocabulary for the subject area or domain of interest, then it is organized into a taxonomy where key concepts are identified, and finally these concepts are defined and related to create an ontology. This paper describes how a domain analysis method based on faceted classification can be used for building ontologies. It relates domain analysis and ontologies, illustrates a step in the domain analysis method for identifying and categorizing concepts, and describes how this step, borrowed from library science, is incorporated into the domain analysis method. The paper also gives an overview of the method and describes a tool for automating parts of the process.

74 citations

Book ChapterDOI
11 Oct 2015
TL;DR: Klink-2 is presented, a novel approach which improves on earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web.
Abstract: The amount of scholarly data available on the web is steadily increasing, enabling different types of analytics which can provide important insights into the research activity. In order to make sense of and explore this large-scale body of knowledge we need an accurate, comprehensive and up-to-date ontology of research topics. Unfortunately, human crafted classifications do not satisfy these criteria, as they evolve too slowly and tend to be too coarse-grained. Current automated methods for generating ontologies of research areas also present a number of limitations, such as: i they do not consider the rich amount of indirect statistical and semantic relationships, which can help to understand the relation between two topics --- e.g., the fact that two research areas are associated with a similar set of venues or technologies; ii they do not distinguish between different kinds of hierarchical relationships; and iii they are not able to handle effectively ambiguous topics characterized by a noisy set of relationships. In this paper we present Klink-2, a novel approach which improves on our earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web. In particular, Klink-2 analyses networks of research entities including papers, authors, venues, and technologies to infer three kinds of semantic relationships between topics. It also identifies ambiguous keywords e.g., "ontology" and separates them into the appropriate distinct topics --- e.g., "ontology/philosophy" vs. "ontology/semantic web". Our experimental evaluation shows that the ability of Klink-2 to integrate a high number of data sources and to generate topics with accurate contextual meaning yields significant improvements over other algorithms in terms of both precision and recall.

74 citations

Book ChapterDOI
28 Oct 2008
TL;DR: This demonstration presents ROO, a tool that facilitates domain experts' definition of ontologies in OWL by allowing them to author the ontology in a controlled natural language called Rabbit, and the quality of the resultant ontologies.
Abstract: This demonstration presents ROO, a tool that facilitates domain experts' definition of ontologies in OWL by allowing them to author the ontology in a controlled natural language called Rabbit. ROO guides users through the ontology construction process by following a methodology geared towards domain experts' involvement in ontology authoring, and exploiting intelligent user interfaces techniques. An experimental study with ROO was conducted to examine the usability and usefulness of the tool, and the quality of the resultant ontologies. The findings of the study will be presented in a full paper at the ISWC08 research track [2].

74 citations

Journal ArticleDOI
TL;DR: An overview of different methods for resolving ontology mismatches is presented and the Ontology Negotiation Protocol (ONP) is motivated as a method that addresses some problems with other approaches.
Abstract: This paper describes an approach to ontology negotiation between agents supporting intelligent information management. Ontologies are declarative (data-driven) expressions of an agent's “world”: the objects, operations, facts and rules that constitute the logical space within which an agent performs. Ontology negotiation enables agents to cooperate in performing a task, even if they are based on different ontologies.Our objective is to increase the opportunities for “strange agents” – that is, agents not necessarily developed within the same framework or with the same contextual operating assumptions – to communicate in solving tasks when they encounter each other on the web. In particular, we have focused on information search tasks.We have developed a protocol that allows agents to discover ontology conflicts and then, through incremental interpretation, clarification and explanation, establish a common basis for communicating with each other. We have implemented this protocol in a set of Java classes that can be added to a variety of agents, irrespective of their underlying ontological assumptions. We have demonstrated the use of the protocol, through this implementation, in a test-bed that includes two large scientific archives: NASA's Global Change Master Directory and NOAA's Wind and Sea Index.This paper presents an overview of different methods for resolving ontology mismatches and motivates the Ontology Negotiation Protocol (ONP) as a method that addresses some problems with other approaches. Much remains to be done. The protocol must be tested in larger and less familiar contexts (for example, numerous archives that have not been preselected) and it must be extended to accommodate additional forms of clarification and ontology evolution.

74 citations

Book ChapterDOI
30 Nov 2008
TL;DR: In this paper, the content of a message is described using an interaction model: the entities to which the terms refer are correlated with other entities in the interaction, and they may also have prior probabilities determined by earlier, similar interactions.
Abstract: Agents need to communicate in order to accomplish tasks that they are unable to perform alone. Communication requires agents to share a common ontology, a strong assumption in open environments where agents from different backgrounds meet briefly, making it impossible to map all the ontologies in advance. An agent, when it receives a message, needs to compare the foreign terms in the message with all the terms in its own local ontology, searching for the most similar one. However, the content of a message may be described using an interaction model: the entities to which the terms refer are correlated with other entities in the interaction, and they may also have prior probabilities determined by earlier, similar interactions. Within the context of an interaction it is possible to predict the set of possible entities a received message may contain, and it is possible to sacrifice recall for efficiency by comparing the foreign terms only with the most probable local ones. This allows a novel form of dynamic ontology matching.

74 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202343
2022155
20219
20205
20199
201838