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.

Methodologies for ontology development

Abstract: It is now widely recognised that constructing a domain model, or ontology, is an important step in the development of knowledge based systems. What is lacking, however, is a clear understanding of how to build ontologies. In this paper we survey the work which has been done so far in beginning to provide a methodology for building ontologies. This work is still formative, and relies heavily on particular experiences. We also provide some discussion of this work, and identify the key issues that must be addressed if we are to move on from ontology construction being an art and to make it an understood engineering process.

Using Domain Ontology as Domain Knowledge for Requirements Elicitation

Abstract: Domain knowledge is one of crucial factors to get a great success in requirements elicitation of high quality, and only domain experts, not requirements analysts, have it. We propose a new requirements elicitation method ORE (Ontology based Requirements Elicitation), where a domain ontology can be used as domain knowledge. In our method, a domain ontology plays a role on semantic domain which gives meanings to requirements statements by using a semantic function. By using inference rules on the ontology and a quality metrics on the semantic function, an analyst can be navigated which requirements should be added for improving completeness of the current version of the requirements and/or which requirements should be deleted from the current version for keeping consistency. We define this process as a method and evaluate it by an experimental case study of software music players.

Conservative extensions in expressive description logics

Abstract: The notion of a conservative extension plays a central role in ontology design and integration: it can be used to formalize ontology refinements, safe mergings of two ontologies, and independent modules inside an ontology. Regarding reasoning support, the most basic task is to decide whether one ontology is a conservative extension of another. It has recently been proved that this problem is decidable and 2ExpTime-complete if ontologies are formulated in the basic description logic ALC. We consider more expressive description logics and begin to map out the boundary between logics for which conservativity is decidable and those for which it is not. We prove that conservative extensions are 2ExpTime-complete in ALCQI, but undecidable in ALCQIO. We also show that if conservative extensions are defined model-theoretically rather than in terms of the consequence relation, they are undecidable already in ALC.

Learning ontologies from natural language texts

Abstract: Research on ontology is becoming increasingly widespread in the computer science community. The major problems in building ontologies are the bottleneck of knowledge acquisition and time-consuming construction of various ontologies for various domains/ applications. Meanwhile moving toward automation of ontology construction is a solution.We proposed an automatic ontology building approach. In this approach, the system starts from a small ontology kernel and constructs the ontology through text understanding automatically. The kernel contains the primitive concepts, relations and operators to build an ontology. The features of our proposed model are being domain/application independent, building ontologies upon a small primary kernel, learning words, concepts, taxonomic and non-taxonomic relations and axioms and applying a symbolic, hybrid ontology learning approach consisting of logical, linguistic based, template driven and semantic analysis methods.Hasti is an ongoing project to implement and test the automatic ontology building approach. It extracts lexical and ontological knowledge from Persian (Farsi) texts.In this paper, at first, we will describe some ontology engineering problems, which motivated our approach. In the next sections, after a brief description of Hasti, its features and its architecture, we will discuss its components in detail. In each part, the learning algorithms will be described. Then some experimental results will be discussed and at last, we will have an overview of related works and will introduce a general framework to compare ontology learning systems and will compare Hasti with related works according to the framework.

Ontology translation on the semantic web

Abstract: Ontologies are a crucial tool for formally specifying the vocabulary and relationship of concepts used on the Semantic Web. In order to share information, agents that use different vocabularies must be able to translate data from one ontological framework to another. Ontology translation is required when translating datasets, generating ontology extensions, and querying through different ontologies. OntoMerge, an online system for ontology merging and automated reasoning, can implement ontology translation with inputs and outputs in OWL or other web languages. Ontology translation can be thought of in terms of formal inference in a merged ontology. The merge of two related ontologies is obtained by taking the union of the concepts and the axioms defining them, and then adding bridging axioms that relate their concepts. The resulting merged ontology then serves as an inferential medium within which translation can occur. Our internal representation, Web-PDDL, is a strong typed first-order logic language for web application. Using a uniform notation for all problems allows us to factor out syntactic and semantic translation problems, and focus on the latter. Syntactic translation is done by an automatic translator between Web-PDDL and OWL or other web languages. Semantic translation is implemented using an inference engine (OntoEngine) which processes assertions and queries in Web-PDDL syntax, running in either a data-driven (forward chaining) or demand-driven (backward chaining) way.