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, lexicalsemantic 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. C. Chiarcos ( ) Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA e-mail: chiarcos@isi.edu J. McCrae P. Cimiano Semantic Computing Group, Cognitive Interaction Technology Center of Excellence (CITEC), University of Bielefeld, Bielefeld, Germany e-mail: jmccrae@cit-ec.uni-bielefeld.de; cimiano@cit-ec.uni-bielefeld.de C. Fellbaum Computer Science Department, Princeton University, Princeton, NJ, USA e-mail: fellbaum@princeton.edu A. Oltramari et al. (eds.), New Trends of Research in Ontologies and Lexical Resources, Theory and Applications of Natural Language Processing, DOI 10.1007/978-3-642-31782-8 2, © Springer-Verlag Berlin Heidelberg 2013 7 8 C. Chiarcos et al. 2.1 Motivation and Overview Language is arguably one of the most complex forms of human behaviour, and accordingly, its investigation involves a broad width of formalisms and resources used to analyze, to process and to generate natural language. An important challenge is to store, to connect and to exploit the wealth of language data assembled in half a century of computational linguistics research. The key issue is the interoperability of language resources, a problem that is at best partially solved [25]. Closely related to this is the challenge of information integration, i.e., how information from different sources can be retrieved and combined in an efficient way. As a principal solution, Tim Berners-Lee – the founder of the World Wide Web – proposed the so called linked data principles to publish open data on the Web. These principles represent rules of best practice that should be followed when publishing data on the Web [4]: 1. Use URIs as (unique) names for things. 2. Use HTTP URIs so that people can look up those names. 3. When someone looks up a URI, provide useful information, using Web standards such as RDF, and SPARQL. 4. Include links to other URIs, so that they can discover more things. We argue that applying the linked data principles to lexical and other linguistic resources has a number of advantages and represents an effective approach to publishing language resources as open data. The first principle means that we assign a unique identifier (URI) to every element of a resource, i.e., each entry in a lexicon, each document in a corpus, every token in a corpus as well as to each data category that we use for annotation purposes. The benefit is that this makes the above mentioned resources uniquely and globally identifiable in an unambiguous fashion. The second principle entails that any agent wishing to obtain information about the resource can contact the corresponding web server and retrieve this information using a well-established protocol (HTTP) that also supports different ‘views’ on the same resource. That is, computer agents might request a machine readable format, while web browsers might request a human-readable and browseable view of this information as HTML. The third principle requires the use of standardized, and thus, inter-operable data models for representing (RDF, [29]) and querying linked data (SPARQL, [35]). The fourth principle fosters the creation of a network of language resources where equivalent senses are linked across different lexicalsemantic resources, annotations are linked to their corresponding data categories in data category repositories, etc. In the definition of linked data, the Resource Description Framework (RDF) receives special attention. RDF was originally designed as a language to provide metadata about resources that are available both offline (e.g., books in a library) and online (e.g., eBooks in a store). RDF provides a data model that is based on labelled directed (multi-)graphs, which can be serialized in different formats, where 2 Towards Open Data for Linguistics: Linguistic Linked Data 9 Table 2.1 Selected relations from existing RDF vocabularies and possible fields of application Domain Example Reference Meta data creator Dublin core meta data categories General relations between resources sameAs Web ontology language (OWL) Concept hierarchies subClassOf RDF schema Relations between vocabularies broader Simple knowledge organization scheme Linguistic annotation lemma NLP interchange format the nodes identified by URIs are referred to as ‘resources’.1 On this basis, RDF represents information in terms of triples – a property (relation, in graph-theoretical terms a labelled edge) that connects a subject (a resource, in graph-theoretical terms a labelled node) with its object (another resource, or a literal, e.g., a string). Every RDF resource and every property is uniquely identified by a URI. They are thus globally unambiguous in the web of data. This allows resources hosted at different locations to refer to each other, and thereby to create a network of data collections. A number of RDF-based vocabularies are already available, and many of them can be directly applied to linguistic resources. A few examples are given in Table 2.1. In this way, the RDF specification provides only elementary data structures, whereas the actual vocabularies and domain-specific semantics need to be defined independently. For reasons of interoperability, existing vocabularies should be re-used whenever possible, but if a novel type of resource requires a new set of properties, RDF also provides the means to introduce new relations, etc. RDF has been applied for various purposes beyond its original field of application. In particular, it evolved into a generic format for data exchange on the Web. It was readily adapted by disciplines as diverse as biomedicine and bibliography, and eventually it became one of the building stones of the Semantic Web. Due to its application across discipline boundaries, RDF is maintained by a large and active community of users and developers, and it comes with a rich infrastructure of APIs, tools, databases, and query languages. Further, RDF vocabularies do not only define the labels that should be used to represent RDF data, but they also can introduce additional constraints to formalize specialized RDF sub-languages. For example, the Web Ontology Language (OWL) defines the data types necessary for the representation of ontologies as an extension of RDF, i.e., classes (concepts), instances (individuals) and properties (relations). In the remainder of this chapter, we explore the benefits of linked data, considering in particular the following advantages: Representation and modelling Lexical-semantic resources can be described as labelled directed graphs (feature structures, [27]), as can annotated corpora [3]. 1The term ‘resource’ is ambiguous here. As understood in this chapter, resources are structured collections of data which can be represented, for example, in RDF. Hence, we prefer the terms ‘node’ or ‘concept’ whenever RDF resources are meant. 10 C. Chiarcos et al. RDF is based on labelled directed graphs and thus particularly well-suited for modelling both types of language resources. Structural interoperability Using a common data model eases the integration of different resources. In particular, merging multiple RDF documents yields another valid RDF document, while this is not necessarily the case for other formats. Federation In contrast to traditional methods, where it may be difficult to query across even multiple parts of the same resource, linked data allows for federated querying across multiple, distributed databases maintained by different data providers. Ecosystem Linked data is supported by a community of developers in other fields beyond linguistics, and the ability to build on a broad range of existing tools and systems is clearly an advantage. Expressivity Semantic Web languages (OWL in particular) support the definition of axioms that allow to constrain the usage of the vocabulary, thus introducing formal data types and the possibility of checking a lexicon or an annotated corpus for consistency. Conceptual interoperability The linked data principles have the potential to make the interoperability problem less severe in that globally unique identifiers for concepts or categories can be used to define the vocabulary that we use and these URIs can be used by many parties who have the same interpretation of the concept. Furthermore, linking by OWL axioms allows us to define the exact relation between two different concepts beyond simple equivalence statements. Dynamic import URIs can be used to refer to external resources such that one can thus import other linguistic resources “dynamically”. By using URIs to point to external content, the URIs can be resolved when needed in order to integrate the most recent version of the dynamically imported resources. We elaborate further on these aspects in this chapter. It