We demonstrate the usage of our FoodKG [3], a food knowledge graph designed to assist in food recommendation. This resource, which brings together recipes, nutrition, food taxonomies, and links into existing ontologies, is used to power a cognitive agent that performs knowledge-base question answering, primarily to help improve peoples' diets by guiding them towards better foods. The system demonstration involves three categories of questions: simple queries for nutritional information, comparisons of nutrients between dierent foods, and constraintbased queries to nd recipes matching certain criteria.
In our resource track paper titled ‘FoodKG: A Semantics-Driven Knowledge
Graph for Food Recommendation’ [
For the sake of brevity, we did not fully describe potential and current applications of our Food knowledge graph. In our demo, as described in this paper, we examine how a knowledge graph can be leveraged by a question-answering system to answer queries related to recipes and nutrition. In our demo, we will demonstrate the use of our FoodKG for answering natural language questions - a process known as knowledge base question answering (KBQA). The system takes natural language questions, such as What Indian dishes can I make with chicken and garlic?, and generates an answer based on information stored in the FoodKG. ? Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
We seek to answer three Questions Question Template Examples tcyopmepsaroifsoqnu,eastnidoncso:nsstirmapinlet., Simple Hioswinm{uicnhgr{enduietnrite}n?t} THhowe simmupcleh qusuesgtaironsi,s e.ign., Comparison {inwghreicdhiehnta1s}leosrs {{innugtrreidenietn}t?2}, cchreeaetseed,cbraeasemd, ofanttfhreeeU?,SaDreA Constraint maWkehwatit{hta{gin}gdreisdhieenstc_anlisIt}? data and require only one hop reasoning. The compar- Table 1. Examples of the types of questions handled ison question, e.g., Sesame by the Q&A system oil or peanut oil, which has less saturated fat?, can be regarded as a composition of two simple questions, followed by a quantitative comparison on some attribute. Last, we also create questions with constraints, e.g., What Laotian dishes can I make with sugar, water, oranges?. Table 1 summarizes the types of questions. 2.1
Our KBQA system consists of three components: the question type classier , the
topic entity predictor , and the KBQA model. Given a natural language question,
such as how much sugar is in Cheese, Blue?, the question type classier
determines the question type. In this case, the question is simple. Then, the topic
entity predictor is applied to detect the topic entity mentioned in the question
and link it to the entity ‘Cheese, Blue’ in the FoodKG. Finally, the KBQA model
is called to retrieve answers from the KG subgraph (which we assume contains
all the candidate answers) surrounding the topic entity. For our
implementation, we deploy our state-of-the-art neural network-based KBQA model called
BAMnet [
In order to provide personalized food recommendation to users, we are working to extend our KBQA system to be aware of user preferences. Our current strategy is to treat preferences as additional, implicit constraints. This is done by maintaining a personal KG for user preferences, such as liked/disliked ingredients or nutritional requirements.
Given a query, such as What are brewing recipes that consist of sugar?, we transform the query into one that incorporates the user’s preferences. For a user who is allergic to ‘mint leaves’, this could result in a query of What are brewing recipes that consist of sugar, and do not have mint leaves? A regular KBQA system that is not aware of user preferences will recommend ‘Kahlua Popsicles’, ‘Jasmine, Green Tea, Fresh Lemonade’, ‘Lemon Liqueur’ and ‘Tequila Lime Punch’. However, our personalized KBQA system will instead recommend ‘Kahlua Popsicles’, ‘Lemon Liqueur’ and ‘Tequila Lime Punch’, since ‘Jasmine, Green Tea, Fresh Lemonade’ contains ‘mint leaves’. In this way, our system is able to provide dierent users with tailored food suggestions that suit their
needs. Future work in this direction includes how to combine personal KG with FoodKG and how to eectively handle negation in a query. 3 3.1
The KBQA system provides answers to user questions; on top of this, we have built a web application to receive questions and present the results.
This is summarized in Figure 1. Bold- Fig. 1. The architecture of the food information system. face text refers to specic components within the gure.
We chose to build a web application in Node-RED3, a ow-based tool to link together logic and services. This made it straightforward to combine the aforementioned KBQA system with a simple frontend, along with the necessary services to perform entity recognition display results.
To identify the topic entity and question type of a query, we use IBM’s Watson Assistant4 service. This information, along with the original query, is passed to the KBQA service.
Since the answers to the user’s queries exist directly in the FoodKG, we chose to have the KBQA system respond not with a plain-text response (e.g., 250 mg sodium), but rather with a URI (e.g., http://idea.rpi.edu/heals/kb/usda#sodium-01004). This direct reference to the KG is then handed to another service, the Explainer. The Explainer service uses a SPARQL query to retrieve data from the FoodKG, and then uses the result of the query to produce humanreadable text. Each type of resource measurement, recipe, nutritional entry has its own rule. This separation of concerns makes it simpler to extend and modify the Q&A system. 3.2
To demonstrate the usefulness of the FoodKG, we plan to present the usage of the above system to answer three types of questions. Examples of such questions and their answers are shown in Figure 2.
As noted, we are presently working to incorporate personalization into the question-answering pipeline, leading to markedly dierent answers to the same
4 https://www.ibm.com/cloud/watson-assistant/ question for dierent users - depending on personal tness and preferences. These implicit additions to the question enrich the meaning of each query without requiring a fundamental shift in how the queries are carried out. (b) Comparison
(c) Constraint
There is a vast amount of disconnected food knowledge, which spans an entire spectrum of food topics, from nutrients to recipes. For an individual, numerous biological, psychological, social and environmental factors inuence food choices. Therefore, navigating this vast space on food requires meaningful linkages. Furthermore, the provenance of the food facts is equally important in order to trust the data, and eventually, the food that is consumed by an individual. Our unied knowledge graph on food, the FoodKG, has these characteristics, and in our demo, we will illustrate the utility of this resource via a Q&A system.