Knowledge graphs (KGs) are seeing widespread application across diverse domains, including querying, search, machine learning (ML), and stream data processing. In all these areas, ensuring data consistency and quality is of critical importance. For instance, erroneous attributes in industrial KGs, such as those describing machinery, can lead to unexpected machine downtime or unreliable analytical predictions. Several frameworks for specifying integrity constraints have been proposed, such as SHACL and ShEX for RDF, and PG-schema for property graphs. However, the challenge of defining comprehensive constraints remains only partially addressed, particularly with respect to combining open- and closed-world assumptions and handling numerical constraints. To this end, we propose a graph constraint language, GraphCo, composed of three key components: first, it supports integrity constraints under both open and closed reasoning when the KG is defined over an underlying ontology; second, it offers a set of constraints that are both expressive and tractable, addressing the requirements of diverse industrial applications, for example, in verifying the structural properties of industrial machines or ML data pipelines; third, it supports data-intensive validation tasks involving numerical transformations such as aggregation and comparison. We implemented GraphCo using Answer Set Programming and Apache Spark to evaluate its scalability, and the results are promising.