Semantic Web Based Execution-time Merging Of Processes
Bibliographic record
Abstract
A process is a series of actions executed in a particular environment in order to achieve a goal. It is often the case that several concurrent processes coexist in an environment in order to achieve several goals simultaneously. However, executing multiple processes is not always a possibility in an environment due to the following reasons: (1) All processes might be needed to be executed by a single agent that is not capable of executing more than one process at a time; (2) Multiple processes may have interactions between them that hamper their concurrent executions by multiple agents. As an example, there might be conflicting actions between several processes that their concurrent execution will stop those processes from achieving their goals. The existing solution to address the abovementioned complications is to merge several processes into a unified conflict-free and improved process before execution. This unified merged process is then executed by a single agent in order to achieve goals of all processes. However, we believe this is not the optimal solution because (a) in some environments, it is unrealistic to assume execution of all processes merged into one single process can be delegated to a single agent; (b) since merging is performed before actual execution of the unified process, some of the assumptions made regarding execution flow in individual processes may not be true during actual execution which will render the merged process irrelevant. In this paper, we propose a semantic web based solution to merge multiple processes during their concurrent execution in several agents in order to address the above-mentioned limitations. Our semantic web Process Merging Framework features a Web Ontology Language (OWL) based ontology called Process Merging Ontology (PMO) capable of representing a wide range of workflow and institutional Process Merging Constraints, mutual exclusivity relations between those constraints and their conditions. Process Merging Constraints should be respected during concurrent execution of processes in several agents in order to achieve execution-time process merging. We use OWL axioms and Semantic Web Rule Language (SWRL) rules in the PMO to define the formal semantics of the merging constraints. A Process Merging Engine has also been developed to coordinate several agents, each executing a process pertaining to a goal, to perform process merging during execution. This engine runs the Process Merging Algorithm that utilizes Process Merging Execution Semantics and an OWL reasoner to infer the necessary modifications in actions of each of the processes so that Process Merging Constraints are respected. In order to evaluate our framework we have merged several clinical workflows each pertaining to a disease represented as processes so that they can be used for decision support for comorbid patients. Technical evaluations show efficiency of our framework and evaluations with the help of domain expert shows expressivity of PMO in representation of merging constraints and capability of Process Merging Engine in successful interpretation of the merging constraints. We plan to extend our work to solve problems in business process model merging and AI plan merging research areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".