Harvesting Domain Specific Ontologies from Text

The goal of Ontology Learning from Text is to learn ontologies that represent domains or applications that change often. Manually learning and updating such ontologies is too expensive. This is the reason for the Ontology Learning discipline's emergence. The leading approach to Ontology Learning from Text is the Ontology Learning Layer Cake. This approach splits the task into four or five sequential tasks. Each of the tasks may use diverse methods, ranging from uses of Linguistic knowledge to Machine Learning. The authors review the shortcomings of the Ontology Learning Layer Cake approach and conclude that the approach is not viable for Ontology Learning from Text. They suggest alternative approaches that may help learning ontologies in an efficient, effective way.

Expert Systems With Applications, 2008

Most of existing ontologies construction tools support construction of ontological relations (e.g., taxonomy, equivalence, etc.) but they do not support construction of domain relations, non-taxonomic conceptual relationships (e.g., causes, caused by, treat, treated by, has-member, contain, material-of, operated-by, controls, etc.). Domain relations are found mainly in text sources. TextOntoEx constructs ontology from natural domain text using semantic pattern-based approach. TextOntoEx is a chain between linguistic analysis and ontology engineering. TextOntoEx analyses natural domain text to extract candidate relations and then maps them into meaning representation to facilitate constructing ontology. The paper explains this approach in more details and discusses some experiments on deriving ontology from natural text.

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 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Ontology learning is a critical task in industry, which deals with identifying and extracting concepts reported in text such that these concepts can be used in different tasks, e.g. information retrieval. The problem of ontology learning is non-trivial due to several reasons with a limited amount of prior research work that automatically learns a domain specific ontology from data. In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text. In our model, the first-stage classifier classifies candidate concepts into relevant and irrelevant concepts and then the second-stage classifier assigns specific classes to the relevant concepts. The proposed system is deployed as a prototype in General Motors and its performance is validated by using complaint and repair verbatim data collected from different data sources. On average, our system shows the F1-score of 0.75, even when data distributions are vastly different.

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.

Ontologies are a key component for several applications. Ontologies are often built by hand, but automatizing the process of ontology building has been and is even more recognized as very important for scaling and speeding up this process. However, several difficulties have been identified, some of them are quite fundamental. In this paper, we present our work for overcoming some of the fundamental difficulties. Our work resulted in improvements of an existing ontology building tool (Text2Onto). The contribution of our work consists in the creation of a flexible language (DTPL-Dependency Tree Patterns Language) for expressing patterns as syntactic dependency trees to extract semantic relations, and making an existing ontology building tool (Text2Onto) able to use them. DTPL allows to exploit deep linguistic information (related to coreference resolutions, conjunctions, appositions, passive verbal phrases, etc.) provided by deep syntactic analysis of the text, and also (in order to improve the accuracy of patterns) to express the exclusion of some dependency bindings in patterns.

Web Semantics: Science, Services and Agents on the World Wide Web, 2012

The Semantic Web aims to extend the World Wide Web with a layer of semantic information, so that it is understandable not only by humans, but also by computers. At its core, the Semantic Web consists of ontologies that describe the meaning of concepts in a certain domain or across domains. The domain ontologies are mostly created and maintained by domain experts using manual, time-intensive processes. In this paper, we propose a rule-based method for learning ontology instances from text that helps domain experts with the ontology population process. In this method we define a lexico-semantic pattern language that, in addition to the lexical and syntactical information present in lexico-syntactic rules, also makes use of semantic information. We show that the lexico-semantic patterns are superior to lexicosyntactic patterns with respect to efficiency and effectivity. When applied to event relation recognition in text-based news items in the domains of finance and politics using Hermes, an ontology-driven news personalization service, our approach has a precision and recall of approximately 80% and 70%, respectively.

International Journal of Human-Computer …, 2004

Ontologies are the basic block of modern knowledge-based systems; however the effort and expertise required to develop them are often preventing their widespread adoption. In this chapter we present a tool for the automatic discovery of basic ontologies -we call them seed ontologies-starting from a corpus of documents related to a specific domain of knowledge. These seed ontologies are not meant for direct use, but they can be used to bootstrap the knowledge acquisition process by providing a selection of relevant terms and fundamental relationships. The tool is modular and it allows the integration of different methods/strategies in the indexing of the corpus, selection of relevant terms, discovery of hierarchies and other relationships among terms. Like any induction process, also ontology learning from text is prone to errors, so we do not expect from our tool a 100% correct ontology; according to our evaluation the result is more close to 80%, but this should be enough for a domain expert to complete the work with limited effort and in a short time.

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 ...