Post-Logical Verification of Ontology and Lexicons

Ontologies are utilized for a wide range of tasks, like information retrieval/extraction or text generation, and in a multitude of domains, such as biology, medicine or business and commerce. To be actually usable in such real-world scenarios, ontologies usually have to encompass a large number of factual statements. However, with increasing size, it becomes very difficult to ensure their complete correctness. This is particularly true in the case when an ontology is not hand-crafted but constructed (semi)automatically through text mining, for example. As a consequence, when inference mechanisms are applied on these ontologies, even minimal inconsistencies oftentimes lead to serious errors and are hard to trace back and find. This paper addresses this issue and describes a method to validate ontologies using an automatic theorem prover and MultiNet axioms. This logic-based approach allows to detect many inconsistencies, which are difficult or even impossible to identify through statistical methods or by manual investigation in reasonable time. To make this approach accessible for ontology developers, a graphical user interface is provided that highlights erroneous axioms directly in the ontology for quicker fixing.

Proceedings of LREC-02, Spain, June, 2002

In this paper we discuss ongoing activity within the approach to natural language processing known as ontological semantics, as defined in Nirenburg and Raskin (forthcoming). After a brief discussion of the principal tenets on which this approach is built, and a revision of extant implementations that have led toward its present form, we concentrate on some specific aspects that are key to the development of this approach, such as the acquisition of the semantics of lexical items and, intimately connected with this, the ontology, the central resource in this approach. Although we review the fundamentals of the approach, the focus is on practical aspects of implementation, such as the automation of static knowledge acquisition and the acquisition of scripts to enrich the ontology further. Applicable Theory Ontological Semantics Description Methodology Acquisition of static knowledge sources and procedures for dynamic knowledge manipulation Description Static and dynamic knowledge sources Application Methodology Application-specific adaptation of acquisition and procedures Phenomena Application Systems and Results

2010

ABSTRACT The relation between ontologies and language is currently at the forefront of natural language processing (NLP). Ontologies, as widely used models in semantic technologies, have much in common with the lexicon. A lexicon organizes words as a conventional inventory of concepts, while an ontology formalizes concepts and their logical relations. A shared lexicon is the prerequisite for knowledge-sharing through language, and a shared ontology is the prerequisite for knowledge-sharing through information technology. In building models of language, computational linguists must be able to accurately map the relations between words and the concepts that they can be linked to. This book focuses on the technology involved in enabling integration between lexical resources and semantic technologies. It will be of interest to researchers and graduate students in NLP, computational linguistics, and knowledge engineering, as well as in semantics, psycholinguistics, lexicology and morphology/syntax.

Towards sounder taxonomies in wordnets 9 (Pedersen) Using EuroWordNet for the translation of ontologies 16 (Declerck et al.) Linguistic Enrichment of Ontologies: a methodological framework 20 (Pazienza, Stellato) LingInfo: a Model for the Integration of Linguistic 28 Information in Ontologies Using various semantic relations in WSD 41 (Szarvas et al.) SmartIndexer – Amalgamating Ontologies and Lexical 45 resources for Document Indexing How Linguistic Resources May help to recommend TV programmes 51 Exploiting Linguistic Resources for building linguistically 57 motivated ontologies in the Semantic Web (Pazienza, Stellato) Metadata Cards for Describing Project Gutenberg Texts 63 (Reck) Open-class named entity classification in multiple domains 69 (Faulhaber et al.) Abstract Reasoning about natural language most prominently requires combining semantically rich lexical resources with world knowledge, provided by ontologies. Therefore, we are building bindings from FrameNet – a lexical r...

A Natural Language Processing Perspective, 2010

ABSTRACT The relation between ontologies and language is currently at the forefront of natural language processing (NLP). Ontologies, as widely used models in semantic technologies, have much in common with the lexicon. A lexicon organizes words as a conventional inventory of concepts, while an ontology formalizes concepts and their logical relations. A shared lexicon is the prerequisite for knowledge-sharing through language, and a shared ontology is the prerequisite for knowledge-sharing through information technology. In building models of language, computational linguists must be able to accurately map the relations between words and the concepts that they can be linked to. This book focuses on the technology involved in enabling integration between lexical resources and semantic technologies. It will be of interest to researchers and graduate students in NLP, computational linguistics, and knowledge engineering, as well as in semantics, psycholinguistics, lexicology and morphology/syntax.

BMC Bioinformatics, 2011

Background: Ontologies are widely used to represent knowledge in biomedicine. Systematic approaches for detecting errors and disagreements are needed for large ontologies with hundreds or thousands of terms and semantic relationships. A recent approach of defining terms using logical definitions is now increasingly being adopted as a method for quality control as well as for facilitating interoperability and data integration.

The aim of natural language ontology is to uncover the ontological categories and structures that are implicit in the use of natural language. This paper aims to clarify what exactly the subject matter of natural language ontology is, what sorts of linguistic data it should take into account, how natural language ontology relates to other sorts of projects in metaphysics, in what ways natural language ontology is important, and in what ways the ontology of natural language may be driven by the use of natural language itself.

The aim of natural language ontology is to uncover the ontological categories and structures that are implicit in the use of natural language, that is, that a speaker accepts when using a language. This talk aims to clarify how natural language ontology relates to other projects in metaphysics, what sorts of linguistic data it should and should not take into account and why natural language ontology is important.

2010

Nowadays, semantic information plays an important role in natural language processing, more specifically describing and representing "the meanings of the words" crucial for understanding the human language. In the last two decades, there have been efforts to create a large database that represents lexical knowledge, where the words and their meanings are represented along with connections held between them. However, in most of the cases, this resources are created manually. For instance Princeton WordNet is considered the standard model of a lexical ontology for the English language. Besides that, also for Portuguese there have been some attempts to create a broad-coverage ontology, also created manually and not publicly available. Still, they are not public available for download, and also all of them were manually created. Despite being less prone to errors, the problem is that the manual creation of these resources takes a lot of time consuming and requires a team, and researchers specialised in the area. Nevertheless, in the last years, some efforts have been made to develop computational tools to reduce the need of manual intervention, such as some authors that propose lexico-semantic patterns to find semantic relations between terms in text. This kind of approach should be considered as an alternative and subject of research, in order to avoid impractical human work in the construction of these resources. Having this in mind, the work expected in this project is the creation of a system capable of automatically acquire semantic knowledge from any kind of Portuguese text. The extraction method is based on lexico-syntactic patterns, that indicate a relation of interest, and also by a inference method to extract hypernymy relations from compound nouns.Also, different kinds of textual resources are used to test and improve our system. Furthermore, this work analyses the benefits from applying similarity distributional metrics based on the occurrence of words in documents to our system outputs. The quality and the utility of the knowledge extracted from the various textual resources, will be compared against another Portuguese knowledge-base. In the end of this research, important contributions for the computational processing of Portuguese language are provided, such as computational tools capable of extracting and inferring lexico-semantic information from text, methodologies to automatically validate these knowledge, and also compare knowledge-bases. Finally, the experiments outcomes and conclusions are published in important conferences for the area.

Ontology and the Lexicon, 2010

This book contributes to the study of the interface between ontologies and the lexicon, a major research topic both in artificial intelligence and in linguistics. Our focus will be on how this interface, when implemented, creates synergy between ontological and lexical knowledge. This focus is actually derived from two diagonal approaches that have become well-received in computational linguistics: To use large linguistic resources in discovery and modeling of ontologies, and to apply ontologies to resolution of semantic issues in natural language processing. This book introduces the foundational framework and infrastructures for research on the interface of ontologies and the lexicon. In addition, state of the art research papers representing these two approaches in computational linguistics are included. This first chapter introduces and establishes a multi-disciplinary perspective on the ontology-lexicon interface or ontolex interface. In particular, we situate our studies at the intersection of knowledge representation, lexical semantics, and computational lexicography by providing a set of working definitions and by surveying previous research that paved the road for this current volume.