From a modeling standpoint, information retrieval is a deep field predicated on several disciplines, including statistics, math, linguistics, artificial intelligence and now data science. In practice, these models are applied against text within corpora to discover patterns in the data. Not only do the IR models overlap in their usage, they can "partner" with other models such as k-means or k-nearest neighbor models, then other models can be applied from the vantage point of computational linguistics such as LDA/LDI and topic modeling Then, the end game is some sort of information visualization of this discovery--after ranking, clustering and aggregating work. Information Retrieval may appear to be a cryptic discipline, but serious effort, which is greatly appreciated, is going into opening up the area for deeper understanding of each model, and the interaction between models. I cite "Synthesis Lectures on Information Concepts, Retrieval, and Services" Series as the best place to delve into a foundation for IR.
While I don't entirely separate IR and Information Extraction, perhaps a subset of IE, concept level extraction, does apply IR patterns along with AI-based inferencing rules to extract related ontologies. The graphical nature of these relations are being enhanced with ontology modeling in OWL and RDF, and with graph databases, which allow for a less strict or rigorous set of relationship modeling, and allow for more relationships to surface, rather than being controlled per se. The ability to grow information extraction dynamically keeps its "discipline" strongly interesting to researchers.
Both IR and IE play out in our own significant "entities of the moment"--some have called "dynamic ontologies"--some being Palantir-- we need the patterns, models, simulations and visualizations of those significant entities to do business in the face of morphing new sources of information and changing of existing information. The conceptual, relational, definitional, pattern and ontological modeling have to be flexible and their visualizations the same. The heavy lifting of AI engines such as Watson in the information extraction and inferencing fields has cast a spotlight on the IE and frankly IR fields. Also the ubiquity of natural language processing and machine learning are calling attention to IR and IE models and engines. The impact of IR models on search and SEO, and on semantic web modeling is one of those "watching a change agent change due to impacts back from what it impacted" events--somewhere in harmonics and relativity theory. IR and IE are expanding based on what they're impacting.