fix typo

README.md

@@ -79,7 +79,7 @@ The initial focus is to describe the aims for a sequence of [demonstrators](demo
 
 A further line of work deals with the means to express and reason with imperfect knowledge, that is, everyday knowledge subject to uncertainty, imprecision, incompleteness and inconsistencies. See the draft specifcation for the [plausible knowledge notation](https://w3c.github.io/cogai/pkn.html) (PKN), and the [web-based PKN demonstrator](https://www.w3.org/Data/demos/chunks/reasoning/). This is based upon work on guidelines for effective argumentation by a long line of philosophers since the days of Ancient Greece. In place of logical proof, we have multiple lines of argument for and against the premise in question just like in courtrooms and everyday reasoning. 
 
-Both PKN and chunks & rules can be considered in relation to RDF. RDF is a framework for symbolic graphs based upon labelled directed graph edges (aka *triples*). Compared to RDF, PKN and chunks & rules are higher level with additional semantics, and designed for used in human-like AI applications. Furthermore, both notations are designed to be easy to read and author compared with RDF serialisations such as RDF/XML, Turtle and even JSON-LD. See also the [Notation3 (N3) Language](https://w3c.github.io/N3/spec/) which is an assertion and logic language defined as a superset of RDF.
+Both PKN and chunks & rules can be considered in relation to RDF. RDF is a framework for symbolic graphs based upon labelled directed graph edges (aka *triples*). Compared to RDF, PKN and chunks & rules are higher level with additional semantics, and designed for use in human-like AI applications. Furthermore, both notations are designed to be easy to read and author compared with RDF serialisations such as RDF/XML, Turtle and even JSON-LD. See also the [Notation3 (N3) Language](https://w3c.github.io/N3/spec/) which is an assertion and logic language defined as a superset of RDF.
 
 Future work is anticipated on vector-space representations of knowledge using artificial neural networks. Advances with generative AI have shown the huge potential of vector-space representations in combination with deep learning. However, there is a long way to go to better model many aspects of human cognition, e.g. continuous learning using a blend of type 1 and type 2 cognition, episodic memory, and the role of emotions and feelings in directing cognition. Symbolic models will continue to serve an important role for semantic interoperability.  Neurosymbolic systems combine the complementary strengths of vector space and symbolic approaches.