mcp-proto-okn: Natural-language access to open scientific knowledge graphs through the Model Context Protocol

MCP Server Proto-OKN (mcp-proto-okn) is a Python-based Model Context Protocol server that enables AI assistants to discover, inspect, query and integrate scientific knowledge graphs through natural language. The server provides graph routing, schema inspection, SPARQL execution, ontology expansion, multi-graph querying, and transcript generation, lowering the barrier to cross-domain knowledge graph analysis for biomedical and scientific users. mcp-proto-okn is implemented in Python using the FastMCP framework and is available at https://github.com/sbl-sdsc/mcp-proto-okn. Documentation, client configuration instructions, and example analysis transcripts are provided in the GitHub repository.

Exposing ambiguities in a relation-extraction gold standard with crowdsourcing

Semantic relation extraction is one of the frontiers of biomedical natural language processing research. Gold standards are key tools for advancing this research. It is challenging to generate these standards because of the high cost of expert time and the difficulty in establishing agreement between annotators. We implemented and evaluated a microtask crowdsourcing approach that can produce a gold standard for extracting drug-disease relations. The aggregated crowd judgment agreed with expert annotations from a pre-existing corpus on 43 of 60 sentences tested. The levels of crowd agreement varied in a similar manner to the levels of agreement among the original expert annotators. This work rein-forces the power of crowdsourcing in the process of assembling gold standards for relation extraction. Further, it high-lights the importance of exposing the levels of agreement between human annotators, expert or crowd, in gold standard corpora as these are reproducible signals indicating ambiguities in the data or in the annotation guidelines.

OntoLoki: an automatic, instance-based method for the evaluation of biological ontologies on the Semantic Web

The delineation of logical definitions for each class in an ontology and the consistent application of these definitions to the assignment of instances to classes are important criteria for ontology evaluation. If ontologies are specified with property-based restrictions on class membership, then such consistency can be checked automatically. If no such logical restrictions are applied, as is the case with many biological ontologies, there are currently no automated methods for measuring the semantic consistency of instance assignment on an ontology-wide scale, nor for inferring the patterns of properties that might define a particular class. We constructed a program that takes as its input an OWL/RDF knowledge base containing an ontology, instances associated with each of the classes in the ontology, and properties of those instances. For each class, it outputs: 1) a rule for determining class membership based on the properties of the instances and 2) a quantitative score for the class that reflects the ability of the identified rule to correctly predict class membership for the instances in the knowledge base. We evaluated this program using both artificial knowledge bases of known quality and real, widely used ontologies. The results indicate that the suggested method can be used to conduct objective, automatic, data-driven evaluations of biological ontologies without formal class definitions in regards to the property-based consistency of instance-assignment. This inductive method complements existing, purely deductive approaches to automatic consistency checking, offering not just the potential to help in the ontology engineering process but also in the knowledge discovery process.