Computer-aided Ontology Development: an integrated environment

Lecture Notes in Computer Science, 2012

Ontologies are fundamental tools used with different purposes and with different modalities in different areas and communities. To guarantee the right level of quality, the most widely used ontologies are man-made. However, developing and maintaining them turns out to be extremely time-consuming. For this reason, there are approaches aiming at their automatic construction where ontologies are incrementally extended by extracting and integrating knowledge from existing sources. However, these approaches tend to reach an accuracy that, according to the application they need to serve, cannot be always considered satisfactory. Therefore, when a higher accuracy is necessary, manual or semi-automatic approaches are still preferable. In this paper we present a technique and a corresponding tool, that we called SAM (semi-automatic mapper), for the semi-automatic enrichment of an ontology through the mapping of an external source to the target ontology. As proved by our evaluation, the tool allows saving around 50% of the time required by purely manual approaches.

icsi.berkeley.edu

Information Extraction systems were designed to filter, to select and to classify the increasing amount of information available nowadays, mainly on the Web. Most of them were based on shallow natural language processing techniques, but semantics was not really used, due to the unavailability of generic ontologies. During last years, some generic Ontologies become available and a lot of research projects tried to take into account semantic aspects to obtain more precise results for IE systems. Meanwhile, important efforts concentrate on developing tools for semi-automatic building of domain-specific Ontologies, based on IE and text-mining techniques.

International Journal of Metadata, Semantics and Ontologies, 2011

Accessing the massive amount of information posted daily to the web is becoming a challenge. The need for tools to exploit information from such content in a robust and rapid manner is greater than ever. Ontology could be an effi cient alternative for searching and exploiting information on the web. The main advantage of ontology is that it provides a standard for the vocabulary. In fact, ontologies aim to present, in a generic and reusable manner, the semantics of a given domain. The automatic construction of ontologies, while still challenging, is gaining ground against the tedious, slow and expensive manual approach. We aim to provide tools for automatic extraction of both terms and relations. The process will automate ontology building. We demonstrate in this work the process of automatic extraction while trying to develop or adapt existing technology to support Arabic language.

IJMER

Abstract: Ontology Extraction play an important role in the Semantic Web as well as in knowledge management. The emergence of Semantic Web and the related technologies promise to make the Web a meaningful experience. Conversely, success of Semantic Web and its applications depends largely on utilization and interoperability of well-formulated ontology bases in an automated heterogeneous environment. Ontology is what exists in a domain and how they relate with each other. The advantage of an ontology is that it represents real world information in a manner that is machine understandable. This leads to a variety of interesting applications for the benefit of the target user groups. An ontology defines the terms used to describe and represent an area of knowledge. Ontologies are critical for applications that need to search across or merge information from diverse communities. In this paper, we present our approach to extract relevant ontology concepts and their relationships from a knowledge base of heterogeneous text documents.

2003

The Web is probably the largest and richest information repository available today. Search engines are the common access routes to this valuable source. However, the role of these search engines is often limited to the retrieval of lists of potentially relevant documents. The burden of analysing the returned documents and identifying the knowledge of interest is therefore left to the user.

Recent Advances in Artificial …, 2004

In this paper we present a methodology to build automatically an ontology, extracting information from the World Wide Web from an initial keyword. This ontology represents a taxonomy of classes and gives to the user a general view of the kind of concepts and the most significant sites that he can find on the Web for the specified keyword's domain. The system uses intensively a publicly available search engine, extracts concepts (based on its relation to the initial one and statistical data about appearance) and represents the result in a standard way.

2012

ABStRAct The collection of the specialized vocabulary of a particular domain (terminology) is an important initial step of creating formalized domain knowledge representations (ontologies). Terminology Extraction (TE) aims at automating this process by collecting the relevant domain vocabulary from existing lexical resources or collections of domain texts.

LDV Forum, 2005

After the vision of the Semantic Web was broadcasted at the turn of the millennium, ontology became a synonym for the solution to many problems concerning the fact that computers do not understand human language: if there were an ontology and every document were marked up with it and we had agents that would understand the markup, then computers would finally be able to process our queries in a really sophisticated way. Some years later, the success of Google shows us that the vision has not come true, being hampered by the incredible amount of extra work required for the intellectual encoding of semantic mark-up -as compared to simply uploading an HTML page. To alleviate this acquisition bottleneck, the field of ontology learning has since emerged as an important sub-field of ontology engineering.

Proceedings of the international …, 2001

Though the utility of domain Ontologies is now widely acknowledged in an increasing number of domains, several barriers must be overcome before Ontologies become practical and useful tools. A critical issue is the task of identifying, defining, and entering the concept definitions. In case of large and complex application domains this task can be lengthy, costly, and controversial (since different persons may have different points of view about the same concept). To reduce time, cost (and, sometimes, harsh discussions) it is highly advisable to refer, in constructing or updating an ontology, to the documents available in the field. In this paper we describe OntoLearn, a text-mining tool devised to improve human productivity during the process of ontology construction.

Informatica (Slovenia), 2021

The automatic ontology enrichment consists of automatic knowledge extraction from texts related to a domain of discourse in the aim to enrich automatically an initial ontology of the same domain. However, the passage, from a plain text to an enriched ontology requires a number of steps. In this paper, we present a three steps ontology enrichment approach. In the first step, we apply natural language processing techniques to obtain tagged sentences. The second step allows us to reduce each extracted sentence to an SVO (Subject, Verb, and Object) sentence, supposed to preserve main information carried by the original sentence(s) from which it is extracted. Finally, in the third step, we proceed to enrich an initial ontology built manually by adding extracted terms in the generated SVO as new concepts or instances of concepts and new relations. To validate our approach, we have used “Phytotherapy" domain because of the availability of related texts on the WWW and also because its ...