While writing posts on the relation between content mapping and semantic web-related topics, I’m also working out the technical background for a specific content mapping solution.
Organic network of content
Content mapping is a collective effort to organize content into an organic, “living” network. As opposed to technologies that attempt to understand content by semantic analysis, content mapping facilitates understanding by having humans classify the connections in between. It is built on the presumption that conceptualization in the human brain follows the same pattern, where comprehension manifests through the connections between otherwise meaningless words, sounds, and mental images gathered by experience. Content mapping therefore is not restricted to textual content as NLP is. It’s applicable to images, audio, and video as well.
The purpose of content mapping is to guide from one piece of content to its most relevant peers. Searching in a content map equals to looking for the ‘strongest’ paths between a node and its network.
Approach & architecture
The technical essence of content mapping is the way we store and manage connections. From a design perspective, I see three approaches.
- Graph: Querying neighbors in arbitrary depth while aggregating connection properties along paths. Limited in the sense that it works only on networks with no more than a fixed number of edges on a path, e.g. question-answer pairs.
- Recursive: Crawling all paths in a node’s network while calculating and sorting weights. Resource hungry due to recursion. Aggregated weights have to be stored until result is returned, and cached until an affected connection is changed.
- Indexing: Tracking paths as implicit connections on the fly. All implicit connections have to be stored separately to make sure they’re quickly retrievable.
When deciding on an architecture upon which to implement the solution, three choices come to mind.
- Relational: Traditional RDBMS, mature and familiar. The richness of SQL and data integrity is highly valuable for most web applications, but such advantages often come at the price of costly joins, tedious optimizations and poor scalability.
- Graph: Fits applications dealing with networks. Despite the structural resemblance with content maps, this genre of databases – being relatively young – lacks certain features necessary for a content mapping solution, such as aggregation along paths.
- Distributed: Scalability and performance are given highest priority. Consequently, access to resources, and features common in relational databases such as references, joins, or transactions are limited or completely missing.
The following table summarizes the key characteristics of each of the nine approach-architecture combinations.
|Relational||Costly self-joins in fixed depth||Complex, caching required||Writing is not scalable|
|Graph||No aggregation along paths||Graph architecture not exploitable||Implicit connection as separate edge type|
|Distributed||Lacks joins, same as recursive||Limited access to resources||Needs concurrency management|
The table above shows that most options have at least one showstopper: either complexity, lack of features and scalability, costly operations or unfitting architecture.
Only two of them seem to satisfy the purpose of content mapping as described in the first section: the graph and distributed implementations of the indexing approach.
- Even though it’s not the graph approach we’re talking about, this is a combination that exploits the advantages of the graph database to its full extent. By storing implicit connections as separate edges, there’s no need to query paths deeper than one neighbor.
- In a distributed database there are no constraints or triggers, demanding more attention in regard to concurrency management. Graph structure is not supported on a native level, but scalability and performance make up for it.