NIF: An ontology-based and linked-data-aware NLP Interchange Format

We are currently observing a plethora of Natural Language Processing tools and services being made available. Each of the tools and services has its particular strengths and weaknesses, but exploiting the strengths and synergistically combining different tools is currently an extremely cumbersome and time consuming task. Also, once a particular set of tools is integrated this integration is not reusable by others. We argue that simplifying the interoperability of different NLP tools performing similar but also complementary tasks will facilitate the comparability of results and the creation of sophisticated NLP applications. In addition, the synergistic combination of tools might ultimately yield a boost in precision and recall for common NLP tasks. In this paper, we present the NLP Interchange Format (NIF). NIF is based on a Linked Data enabled URI scheme for identifying elements in (hyper-)texts and an ontology for describing common NLP terms and concepts. NIF aware applications will produce output (and possibly also consume input) adhering to the NIF ontology. Other than more centralized solutions such as UIMA and GATE, NIF enables the creation of heterogeneous, distributed and loosely coupled NLP applications, which use the Web as an integration platform. We evaluate the NIF approach by (1) benchmarking the stability of the NIF URI scheme and (2) providing results of a field study, where we integrated 6 diverse NLP tools using NIF wrappers.