**Author details**

*Linked Open Data - Applications, Trends and Future Developments*

time is time-consuming and/or burdensome task.

ontologies, and

instances.

**Acknowledgements**

their previous references or mentions. To this effect, we note that ontology-based systems, in general, poses two main challenges that are directed towards [31]:

• identification of the concepts (e.g., process instances or entities) within the

• automatic population of the ontologies with newly (inferred or classified)

with the process instances or concepts assertions; the ultimate function of the resultant (OBIE) systems would simply be to manipulate the process elements, for example, by uncovering the relationships that exist amongst the process instances and revealing those to the users or search initiators based on the query modules [6, 9, 16, 31, 44, 54]. Moreover, for rule-based systems like OBIE, such procedures are relatively unswerving. But for learning-based IE systems, it appears to be more problematic due to the fact that training data are most often required to train the models, and collecting the necessary training data is, on the other hand, likely to be cumbersome/bottleneck [31]. Although to resolve such issues, new training datasets may need to be created either manually or semi-automatically; which are a lot of the

Perhaps, it is also important to mention that when the ontologies are populated

However, new and emerging systems/methods are being developed with the aim to help address such *metadata creation* problems for knowledge management or data analysis to support the IE and LOD at large [1, 11–15, 23, 33, 55, 56]. Moreover, unlike the traditional IE systems where the extracted facts (or information) are only classified as belonging to pre-defined types, an ontology-based (semantic) IE system (such as the SBLODF) seeks to identify, analyze and represent information at the conceptual (abstraction) levels by establishing a link (references) between the entities residing in the underlying systems' knowledge-bases and their mentions within the contextual domain. Henceforth, semantically-based LOD systems should not only support the formal representation of the different domains. But should also, on the other hand, provide information about the several known entities and their properties descriptions. Thus, ontology-based LOD systems such as the SBLODF introduced in this chapter must integrate well-defined entities with their semantic descriptions for an efficient explicit and implicit information extraction and/or analysis, i.e., machine-readable and machine-understandable system.

The author would like to acknowledge the technical support of Writing Lab,

TecLabs, Tecnologico de Monterrey, in the publication of this work.

**54**

Kingsley Okoye Writing Lab, TecLabs, Office of the Vice President for Research and Technology Transfer, Tecnologico de Monterrey, Monterrey, CP 64849, Nuevo Leon, Mexico

\*Address all correspondence to: kingsley.okoye@tec.mx

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
