John P. McCrae

Linking lexical resources and ontologies on the semantic web with lemon

There are a large number of ontologies currently available on the Semantic Web. However, in order to exploit them within natural language processing applications, more linguistic information than can be represented in current Semantic Web standards is required. Further, there are a large number of lexical resources available representing a wealth of linguistic information, but this data exists in various formats and is difficult to link to ontologies and other resources. We present a model we call lemon (Lexicon Model for Ontologies) that supports the sharing of terminological and lexicon resources on the Semantic Web as well as their linking to the existing semantic representations provided by ontologies. We demonstrate that lemon can succinctly represent existing lexical resources and in combination with standard NLP tools we can easily generate new lexica for domain ontologies according to the lemon model. We demonstrate that by combining generated and existing lexica we can collaboratively develop rich lexical descriptions of ontology entities. We also show that the adoption of Semantic Web standards can provide added value for lexicon models by supporting a rich axiomatization of linguistic categories that can be used to constrain the usage of the model and to perform consistency checks.

Towards Open Data for Linguistics: Linguistic Linked Data

‘Open Data’ has become very important in a wide range of fields. However for linguistics, much data is still published in proprietary, closed formats and is not made available on the web. We propose the use of linked data principles to enable language resources to be published and interlinked openly on the web, and we describe the application of this paradigm to the modeling of two resources, WordNet and the MASC corpus. Here, WordNet and the MASC corpus serve as representative examples for two major classes of linguistic resources, lexical-semantic resources and annotated corpora, respectively.Furthermore, we argue that modeling and publishing language resources as linked data offers crucial advantages as compared to existing formalisms. In particular, it is explained how this can enhance the interoperability and the integration of linguistic resources. Further benefits of this approach include unambiguous identifiability of elements of linguistic description, the creation of dynamic, but unambiguous links between different resources, the possibility to query across distributed resources, and the availability of a mature technological infrastructure. Finally, recent community activities are described.

Integrating WordNet and Wiktionary with lemon

Nowadays, there is a significant quantity of linguistic data available on the Web. However, linguistic resources are often published using proprietary formats and, as such, it can be difficult to interface with one another and they end up confined in “data silos”. The creation of web standards for the publishing of data on the Web and projects to create Linked Data have lead to interest in the creation of resources that can be published using Web principles. One of the most important aspects of “Lexical Linked Data” is the sharing of lexica and machine readable dictionaries. It is for this reason, that the lemon format has been proposed, which we briefly describe. We then consider two resources that seem ideal candidates for the Linked Data cloud, namely WordNet 3.0 and Wiktionary, a large document based dictionary. We discuss the challenges of converting both resources to lemon , and in particular for Wiktionary, the challenge of processing the mark-up, and handling inconsistencies and underspecification in the source material. Finally, we turn to the task of creating links between the two resources and present a novel algorithm for linking lexica as lexical Linked Data.

Semantic Quran: A multilingual resource for natural-language processing

In this paper we describe the Semantic Quran dataset, a multilingual RDF representation of translations of the Quran. The dataset was created by integrating data from two different semi-structured sources and aligned to an ontology designed to represent multilingual data from sources with a hierarchical structure. The resulting RDF data encompasses 43 different languages which belong to the most under-represented languages in the Linked Data Cloud, including Arabic, Amharic and Amazigh. We designed the dataset to be easily usable in natural-language processing applications with the goal of facilitating the development of knowledge extraction tools for these languages. In particular, the Semantic Quran is compatible with the Natural-Language Interchange Format and contains explicit morpho-syntactic information on the utilized terms. We present the ontology devised for structuring the data. We also provide the transformation rules implemented in our extraction framework. Finally, we detail the link creation process as well as possible usage scenarios for the Semantic Quran dataset.

lemonUby – A large, interlinked, syntactically-rich lexical resource for ontologies

We introduce lemonUby, a new lexical resource integrated in the Semantic Web which is the result of converting data extracted from the existing large-scale linked lexical resource UBY to the lemon lexicon model. The following data from UBY were converted: WordNet, FrameNet, VerbNet, English and German Wiktionary, the English and German entries of Omega- Wiki, as well as links between pairs of these lexicons at the word sense level (links between VerbNet and FrameNet, VerbNet and WordNet, WordNet and FrameNet, WordNet and Wiktionary, WordNet and German OmegaWiki). We linked lemonUby to other lexical resources and linguistic terminology repositories in the Linguistic Linked Open Data cloud and outline possible applications of this new dataset.

Design patterns for engineering the ontology-lexicon interface

In this paper, we combine two ideas: one is the recently identified need to extend ontologies with a richer lexical layer, and the other is the use of ontology design patterns for ontology engineering. We combine both to develop the first set of design patterns for ontology-lexica, using the ontology-lexicon model, lemon. We show how these patterns can be used to model nouns, verbs and adjectives and what implications these patterns impose on both the lexicon and the ontology. We implemented these patterns by means of a domain-specific language that can generate the patterns from a short description, which can significantly reduce the effort in developing ontology-lexica. We exemplify this with the use case of constructing a lexicon for the DBpedia ontology.

On the Role of Senses in the Ontology-Lexicon

This chapter investigates the notion of ‘sense’ in the ontology-lexicon interface.As a realization of the ontology-lexicon interface, we are concerned with so called ‘ontology lexica’ which specify the meaning of lexical entries by reference to a given ontology.We propose that in the context of the ontology-lexicon interface a ‘sense’ can be understood as a three-faceted entity, i.e.as a (i) reification of the link between a lexical entry and the ontological reference, (ii) as subset of all the uses of the word that can be interpreted as referring to the same ontological reference, and (iii) as an implicitly defined subconcept.We also provide a new definition of the traditional notions of homonymy, synonymy, metonymy etc.in the ontology-lexicon interface.

LIME: The Metadata Module for OntoLex

The OntoLex W3C Community Group has been working for more than three years on a shared lexicon model for ontologies, called lemon. The lemon model consists of a core model that is complemented by a number of modules accounting for specific aspects in the modeling of lexical information within ontologies. In many usage scenarios, the discovery and exploitation of linguistically grounded ontologies may benefit from summarizing information about their linguistic expressivity and lexical coverage by means of metadata. That situation is compounded by the fact that lemon allows the independent publication of ontologies, lexica and lexicalizations linking them. While the VoID vocabulary already addresses the need for general metadata about interlinked datasets, it is unable by itself to represent the more specific metadata relevant to lemon. To solve this problem, we developed a module of lemon, named LIME Linguistic Metadata, which extends VoID with a vocabulary of metadata about the ontology-lexicon interface.

One Ontology to Bind Them All: The META-SHARE OWL Ontology for the Interoperability of Linguistic Datasets on the Web

META-SHARE is an infrastructure for sharing Language Resources LRs where significant effort has been made into providing carefully curated metadata about LRs. However, in the face of the flood of data that is used in computational linguistics, a manual approach cannot suffice. We present the development of the META-SHARE ontology, which transforms the metadata schema used by META-SHARE into ontology in the Web Ontology Language OWL that can better handle the diversity of metadata found in legacy and crowd-sourced resources. We show how this model can interface with other more general purpose vocabularies for online datasets and licensing, and apply this model to the CLARIN VLO, a large source of legacy metadata about LRs. Furthermore, we demonstrate the usefulness of this approach in two public metadata portals for information about language resources.

Using SPIN to Formalise XBRL Accounting Regulations on the Semantic Web

The eXtensible Business Reporting Language (XBRL) has standardised consolidated financial reporting and through its machine readable format facilitates access to and consumption of financial figures contained within the report. Formalising XBRL as RDF facilitates the leveraging of XBRL with Open Financial Data. Previous XBRL to Semantic Web transformations have however concentrated on making the semantics of its logical model explicit to the exclusion of accounting regulatory validation rules and constraints found within the XBRL calculation linkbases. Using off-the-shelf Semantic Web technologies this paper investigates the use of the SPARQL Inferencing Notation (SPIN) with RDF to formalise these accounting regulations found across XBRL jurisdictional taxonomies. Moving beyond previous RDF to XBRL transformations we investigate how SPIN enhanced formalisation enables financial instrument fact inferencing and sophisticated consistency checking. SPIN formalisations are further used to evaluate the correctness of reported financial data against the calculation requirements imposed by accounting regulation. Our approach illustrated through the use of use case demonstrators outlines that SPIN usage meets central requirements for financial constraint regulatory modelling.