[u' @inproceedings{zulaika_zurimendi_enhancing_2018, address = {Punta Cana, Republica Dominicana}, title = {Enhancing {Profile} and {Context} {Aware} {Relevant} {Food} {Search} through {Knowledge} {Graphs}}, volume = {2}, isbn = {2504-3900}, doi = {10.3390/proceedings2191228}, abstract = {Foodbar is a Cloud-based gastroevaluation solution, leveraging IBM Watson cognitive services. It brings together machine and human intelligence to enable cognitive gastroevaluation of \u201ctapas\u201d or \u201cpintxos\u201d , i.e., small miniature bites or dishes. Foodbar matchmakes users\u2019 profiles, preferences and context against an elaborated knowledge graph based model of user and machine generated information about food items. This paper reasons about the suitability of this novel way of modelling heterogeneous, with diverse degree of veracity, information to offer more stakeholder satisfying knowledge exploitation solutions, i.e., those offering more relevant and elaborated, directly usable, information to those that want to take decisions regarding food in miniature. An evaluation of the information modelling power of such approach is performed highlighting why such model can offer better more relevant and enriched answers to natural language questions posed by users.}, booktitle = {Proceedings}, publisher = {MDPI}, author = {Zulaika Zurimendi, Unai and Lopez-de-Ipina, Diego and Gutierrez, Asier}, month = dec, year = {2018}, keywords = {Artificial Intelligence, Knowledge Representation and Management, data models, foodbar2, knowledge graphs, machine learning, recommendation systems, smart cities, software architectures}, pages = {1228}, } ']