Harness the power of ontologies to build better information platforms
Ontologies provide a formal representation of a knowledge domain, describing its entities, events and processes and the relationships connecting these. One key advantage of ontologies is their ability to store general knowledge constructs as well as specific ones i.e. instances of the generalized knowledge constructs. As such ontologies support the development of modern information platforms that aim to store a representation of the status quo for subsequent interpretation. The closer the ontology resembles the status quo, the closer the inferred reality resembles the original situation for interpretation. Ontologies also enable the integration and combination of data from multiple heterogenous data sources, where data can be represented in different formats, stored using different infrastructures or different terms are being used to represent the same data. All these challenges can be addressed using an ontological approach to data modelling, which will be discussed in this presentation INTRODUCTION In a previous PhUSE paper [1] we pointed out that there is a clear paradigm shift in clinical development. Instead of a linear, rather siloed workflow where data is moving from one stage to the next, the industry needs an approach where data and the resulting information and knowledge is available across all functions throughout the whole value chain from discovery to market. This has resulted in the realisation that the clinical development process is data driven. Having said that, it is not the data per se that drives this process but rather the information and the knowledge we derive from this data. In order to achieve this goal, we need to be able to understand the data in context and interpret it accordingly. Because of the paradigm shift we eluded to earlier, we don't only have to be able to evaluate data at the point of collection but also at a later date, when additional information about how, when and in which context the data was collected is normally not readily available. In this sense modern databases are not so much archival systems but rather information platforms that in one way or another need to be representations of the data and its contextual constraints (i.e. meta data) at the time of collection so that we can understand and interpret the data at a later time. As a result, we need to create a factual representation for further interpretation so that if our view of the status quo changes our representation of the current situation can change as well. Figure 1 Information systems as a representation of the real world PhUSE US Connect 2019 2 ONTOLOGICAL FRAMEWORK In order to understand how ontologies can support the development of such a representational information model it is important to determine what an ontology actually is. The term ontology has its origin in philosophy, where it denotes the study of what entities exist and how they relate to each other (metaphysics) Unfortunately, the term ontology has been used to mean a variety of things within the realm of information technology, ranging from simple lists, vocabularies, terminologies, taxonomies to more complex representational frameworks. Combining these different viewpoints, we define an ontology as follows: To start with, an ontology is a representation of a knowledge domain. An ontology can be looked at as a photograph. In the same way a photograph is a faithful representation of reality an ontology should be as close as possible to reality or our understanding of reality. In addition, an ontology does not only cover entities or things in the way a taxonomy, a hierarchical classification of things, does. An ontology also includes their properties, qualities and attributes as well as events and processes. An ontology is capable of capturing change over time. Ontologies also need to be well structured. A consistent terminology needs to be available to define all the different entities, events and processes. The terminology tells you what terms are used to describe a particular entity while the ontology is concerned with the entity itself. Take the term patient for example. A terminology would define what the term patient means, whereas an ontology would look at a patient as part of what exists in reality. Since the terminology is part of the ontology, an ontology is self describing. The main purpose of any ontology is to provide a better understanding of the existing knowledge by putting all available entities in context. By putting entities in context, you connect them to each other, creating relationships between them. These relationships are rooted in first order (or predicate) logic exemplified by the simple statement "there exists an entity X such that X is a patient and X has Asthma" The relationships in this statement are denoted by "is a" and "has" Relationships like this are often referred to as primitive. Primitive relationships effectively do not carry any meaning as such other than denoting that two entities are connected. Other types of relationship might provide a specific meaning compare the two statements "Jane lives in New York" to "Jane works in New York" While both statements connect Jane and New York, the context in which these two entities are connected is clearly different. Another aspect about an ontology, relevant in the context of a representational information platform, is the ability to instantiate the entities, events and processes described in the ontology. As such the descriptions in an ontology are based on a generalisation, i.e. the "encoding of general features of things" [2] All these generalisations are based on individual instances of these things. As an example, what characterises a person in general is ultimately determined by the characteristics of an individual person. Therefore, it needs to be possible to use the generalisation of a person to describe an individual person. Looking at different ontologies there are several types of ontologies that can be distinguished. Upper or Formal Ontologies are domain independent ontologies. In many ways these Formal Ontologies describe other ontologies and how they should be organised. You could consider them ontologies of ontologies. Domain specific or Material Ontologies on the other hand describe specific knowledge domains using domain specific vocabulary. ONTOLOGY A structured representation of a knowledge domain, describing its entities including properties, qualities and attributes as well as events and processes and the relationships connecting these entities, events and processes, that can be instantiated. PhUSE US Connect 2019 3 Figure 2 A simple ontology PURPOSE OF AN ONTOLOGY We have already mentioned that one of the main purposes of an ontology is to provide a better understanding of a knowledge domain. By describing all the elements of a knowledge domain and its relationship, all the knowledge is made explicit and therefore usable by everybody or at least domain experts. The explicit nature of an ontology also makes it possible to evaluate the information at a later date, thus making it reusable. The terminological component of an ontology makes it also possible to integrate data from highly disparate data sources. Ontologies are also mentioned in the context of annotating data [3] mainly in the form of assignment of entries from controlled vocabularies to particular data points. In the clinical development fields this is currently the domain of metadata repositories, which are used to annotate data in existing relational database systems. REPRESENTATIONAL (ONTOLOGICAL) INFORMATION MODEL Relational databases have been the "swiss army knife of the industry for decades" [4] Relational databases have proven to be very effective as transactional systems but as analytical systems, they have many shortcomings. In particular the ever increasing complexity of data is making the development of appropriate data models increasingly difficult or rather impossible. People are becoming increasingly aware of the connected and linked nature of data requiring new approaches to store and evaluate complex, highly disparate data. With the advent of modern database technologies, in particular graph databases, it is becoming more and more common to represent data as knowledge graphs (not to be confused with Googles Knowledge Graph) or linked data. We described different graph database technologies as well as their benefits over conventional relational databases in previous PhUSE papers [4] [5] BENEFITS OF GRAPH DATABASES The conceptual data model translates directly into the graph database model. There is no need for transformations. Most data and how the data is connected can be described as a graph. In reality this is how our brain works making it rather easy to understand. There are no constraints on individual joins as with relational databases. Graph databases are flexible and easily expandable. New nodes and relationships can be added without disturbing the existing graph. As a consequence, the data model can evolve with our understanding of the respective domain. This means that data can already be exploited or evaluated before the final data model has emerged. Repurposing of the data is also much easier as the perspective and focus can be adjusted. Metadata can be stored directly as part of the data. Graph databases make it easy to store any data about the data as part of the model. This makes it much easier for users to understand the model and work with it. Data can be evaluated in different contexts. Data can be explored starting from any node within the network of data points. PhUSE US Connect 2019 4 These benefits can be greatly enhanced through the use of an ontology. After a knowledge domain has been adequately described in an ontology, this can be directly implemented as the conceptual data model for the respective database. While the ontology provides the framework and the terminology for the database, the database provides the means