Linguistic Linked Open Data

In natural language processing, linguistics and neighboring fields, Linguistic Linked Open Data (LLOD) describes a method and an interdisciplinary community concerned with creating, sharing and (re-)using language resources in accordance with Linked Data principles. The Linguistic Linked Open Data cloud was conceived and is maintained by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation, but has been a point of focal activity for several W3C community groups, research projects and infrastructure efforts since then.

Linguistic Linked Open Data

LLOD Cloud (2016-05-24)

Linguistic Linked Open Data is a movement about publishing data for linguistics and natural language processing using the following principles:^[1]

• Data should be openly license using licenses such as the Creative Commons licenses.
• The elements in a dataset should be uniquely identified by means of a URI.
• The URI should resolve, so users can access more information using web browsers.
• Resolving an LLOD resource should return results using web standards such as Resource Description Framework (RDF).
• Links to other resources should be included to help users discover new resources and provide semantics.

The primary benefits of LLOD have been identified as:^[2]

• Representation: Linked graphs are a more flexible representation format for linguistic data
• Interoperability: Common RDF models can easily be integrated
• Federation: Data from multiple sources can trivially be combined
• Ecosystem: Tools for RDF and linked data are widely available under open source licenses
• Expressivity: Existing vocabularies help express linguistic resources.
• Semantics: Common links express what you mean.
• Dynamicity: Web data can be continuously improved.

Uses of LLOD

Linguistic Linked Open Data was applied to address a number of scientific research problems:

• In all areas of empirical linguistics, computational philology and natural language processing, linguistic annotation and linguistic markup represent central elements of analysis, but progress in this field is being hampered by interoperability challenges, most notably differences in vocabularies and annotation schemes used for different resources and tools. Using Linked Data to connect language resources and ontologies/terminology repositories facilitates re-using shared vocabularies and interpreting them against a common basis.
• In corpus linguistics and computational philology, overlapping markup represents a notorious problem to conventional XML formats. Hence, graph-based data models have been suggested since the late 1990s.^[3] These are traditionally represented by means of multiple, interlinked XML files (standoff XML),^[4] which are poorly supported by off-the-shelf XML technology.^[5] Modeling such complex annotations as Linked Data represents a formalism semantically equivalent to standoff XML,^[6] but eliminates the need for special-purpose technology, and, instead, relies on the existing RDF ecosystem.
• Multilingual issues including the linking of lexical resources such as WordNet as performed in the Interlingual Index of the Global WordNet Association and interconnecting heterogeneous resources such as WordNet and Wikipedia, as was done in BabelNet.
• Providing forums for standardization of linguistic resource information

LLOD cloud development and community activities

The LLOD cloud diagram is maintained by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (since 2014 Open Knowledge), an open and interdisciplinary of experts in language resources.

The OWLG organizes community events and coordinates LLOD developments and facilitates interdisciplinary communication between and among LLOD contributors and users.

Several W3C Business and Community Groups focus on specialized aspects of LLOD:

• The W3C Ontology-Lexica Community Group develops and maintains specifications for machine-readable dictionaries in the LLOD cloud
• The W3C Best Practices for Multilingual Linked Open Data Community Group gathers information on best practises for producing multilingual linked open data.
• The W3C Linked Data for Language Technology Community Group assembles user cases and requirements for language technology applications that use Linked Data.

LLOD development is driven forward by and documented in series of international workshops, datathons, and associated publications. Among others, these include

• Linked Data in Linguistics (LDL), annual scientific workshop, since 2012
• Multilingual Linked Open Data for Enterprises (MLODE), bi-annual community meeting, since 2012
• Summer Datathon on Linguistic Linked Open Data (SD-LLOD), bi-annual datathon, since 2015

Uses and development of LLOD have been subject to several research projects, including

• LOD2 (11 EU countries + Korea, 2010–2014)
• MONNET (5 EU countries, 2010–2013)
• LIDER (5 EU countries, 2013–2015)
• QTLeap (6 EU countries, 2013–2016)
• FREME (6 EU countries, 2015-2017)

References