A goal of Knowledge Management (KM) is to capture and share knowledge wherever it resides in the organization. Leveraging the corporate collective know-how will improve decision making and innovation where it is needed. Organizations do not make decisions just based on one factor; the total picture is what should drive decisions. The emerging challenge for organizations is to derive meaningful insights from available data and re-apply it intelligently. Information Architecture (IA) will enable analyzing and synthesizing data to derive meaningful knowledge.

IA provides the mechanism for organizing, structuring and extracting insights from data and information. Providing the methods and tools for organizing, labeling, building relationships (through associations), and describing (through metadata) your unstructured content adding this source to your overall data analysis. In addition, information architecture enables Artificial Intelligence (AI), in particular machine learning (ML) to rapidly explore and analyze any combination of structured and unstructured sources. ML requires information architecture to exploit relationships and synergies between information, aligning unstructured and structured data. This infrastructure enables organizations to make decisions utilizing the full spectrum of your big data sources.

IA and Machine Learning (ML)

IA effects ML/Data Analytics through Content (unstructured data) Curation. Content Curation is an essential step to enabling analytics to be effective. Curating the content, adding the necessary metadata (descriptive, structural, administrative), and categorization elements is enabled by applying IA. Content curation provides the methodological and technological data management support to address data quality issues, while maximizing the usability of the data. Besides playing a major role in curating data to be used in machine learning, IA is used as the basis for developing knowledge graphs. Knowledge graphs have been successfully implemented as another AI tool that can extract insights (knowledge) from your data.

IA and Knowledge Graphs

An essential artifact produced during the development of your information architecture is the Content Model. The Content Model provides the framework for organizing your content so that it can be delivered and reused in a variety of innovative ways. Once you have created the Content Model you will be able to label information in ways that will enhance search and retrieval, making it possible for users to find the information resources they need quickly and easily. The Content Model is created using classes, relations, axioms and instances at the content level of abstraction. The Content Model also serves as the basis for an ontology (knowledge model) that is used in the design of a knowledge graph.

Sample Content Model

Knowledge Graph

A Knowledge Graph (K-Graph) as it turns out is described in many ways. It has its roots as imbedded intelligence within a Google Search. This intelligence exposes the “thing” you searched for and their relationship between other similar or related “things”.  A succinct description of Knowledge Graphs comes from the Ontotext Company, which states “A knowledge graph is a collection of interlinked descriptions of concepts, entities, relationships and events. Knowledge graphs put data in context via linking and semantic metadata and this way provide a framework for data integration, unification, analytics and sharing”.

A Knowledge Graph is essentially a data structure that allows you to contextualize entities and organize those correlations between entities or multiple type of entities. A sample process of constructing a knowledge graph consists of Collecting and analyzing your data, Data Extraction & Integration, Data Linking & Enrichment, Storage, Querying & Inferencing, Search and Visualization.

Some of the applications of a Knowledge Graphs include: semantic search, automated fraud detection, intelligent chatbots, advanced drug discovery, dynamic risk analysis, content-based recommendation engines and knowledge management systems. The Knowledge Graph is focused on data (structured, semi-structured and unstructured) and this is the same for the content model, which makes understanding how to develop content models to be a very powerful tool for knowledge management and AI.

You could say that IA connects KM to AI!

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