Semantic Log Processing Overview

• Semantic log processing is the discipline of converting unstructured logs into structured data by annotating events with explicit semantic roles.
• It employs advanced techniques such as BERT-based semantic tagging and logistic regression for accurate instance-level and attribute-level classification.
• The approach enables refined process mining, object-centric analysis, and improved operational insights through detailed semantic enrichment.

Semantic log processing is the discipline concerned with transforming unstructured or semi-structured event logs into structured, context-rich data representations that encode the semantics of process or system events. The objective is to automate the extraction and annotation of actionable process information—such as entity roles, operational context, object states, and actor resources—from free-form or ad hoc textual log attributes. By enabling fine-grained, explicit semantic annotations, this approach supports advanced process mining, organizational analytics, and artifact-centric analyses in domains ranging from business process management to complex IT service operations.

1. Semantic Role Labeling in Process Logs

A foundational capability of semantic log processing is the automatic assignment of semantic roles to textual fragments within event log attributes. Each event $e \in \mathcal{E}$ in a log $L$ comprises a set of attributes $D$. For instance-level labeling, semantic log processing applies a pipeline:

1. Tokenization: For any attribute value $e.D$, tokens are extracted as $tok(e.D) = \langle t_1, t_2, \ldots, t_n \rangle$.
2. Semantic Tagging: A tagger maps contiguous token groups (“chunks”) $c_i$ to semantic roles $r_i \in \mathcal{R} \cup \{\mathrm{other}\}$ via

$$tag(\langle t_1, \ldots, t_n \rangle) \mapsto \langle c_1 \backslash r_1, \ldots, c_m \backslash r_m \rangle$$

For example, “create purchase order” yields

$$\langle\mathrm{``create''} \backslash \mathrm{action},\ \mathrm{``purchase\ order''} \backslash \mathrm{obj}\rangle$$

State-of-the-art approaches, such as the one described in (Rebmann et al., 2021), implement this tagging via fine-tuned BERT models, capturing semantic nuance and context within variable event descriptions. Training utilizes extensive manually annotated corpora (e.g., 13k+ event values), optimizing the model to map tokens/chunks to one of eight semantic roles with high precision and recall.

2. Attribute-Level Semantic Classification

While instance-level tagging excels for free-form textual attributes, many logs also include structured, less expressive attributes (e.g., identifiers, status codes, Boolean flags). To semantically enrich these attributes, a secondary classification is performed:

• Attribute names are vectorized (e.g., via GloVe embeddings) and classified by a logistic regression model into semantic roles $r_D \in \mathcal{R} \cup \{\mathrm{other}\}$, yielding both a role and a confidence score.
• For noun-only or context-deficient attributes, the method inserts their values into richer, syntactically varied attribute contexts (extracted from multi-role textual attributes). Each context is then retagged by the BERT model, where the most frequent role assignment is used.

This dual strategy enables appropriate labeling even for attributes that are otherwise context-free or ambiguous, ensuring comprehensive semantic coverage.

3. Ontology of Semantic Roles

The semantic framework is structured around eight primary roles, partitioned as follows:

Category	Role	Description	Example
Business Object	obj	Main business object	“invoice”
	obj_status	State/status of the business object	“approved”
Action	action	Kind of action performed	“create”
	action_status	Status of the action	“started”
Actor	actor	Type of active resource	“system”
	actor_instance	Specific actor instance/ID	“emp42”
Passive Resource	passive	Type of passive resource	“recipient”
	passive_instance	Passive resource instance	“role-Z”

Explicitly recognizing these roles enables downstream process mining tasks to trace object life cycles, characterize agent behaviors, and unravel the structure of collaborative business actions.

4. Quantitative Evaluation and Empirical Results

Extensive leave-one-out cross-validation experiments were conducted on 14 diverse real-world event logs:

• Instance-level semantic role labeling: Achieved an overall F₁-score of ~0.91. Notably, “action” roles reached 0.94–0.95 (precision/recall), “obj” roles achieved around 0.89.
• Attribute-level classification: Across challenging non-textual attributes, reported F₁ ≈ 0.83, precision ≈ 0.87, and recall ≈ 0.79.
• Comparative parsing: When benchmarked against state-of-the-art label parsers on activity labels, the method significantly improved macro F₁-score (0.75 vs. 0.47).

The method retains high efficacy even with heterogeneous labels, confirming robustness to noise and real-world label ambiguity.

5. Real-World Application and Case Study Insights

Applied to the BPI20 Permit Log, semantic enrichment provided key analytical advantages:

• Event Class Reduction: By stripping resource information and aggregating by key semantic roles (e.g., action, obj), 51 classes condensed to 21—significantly simplifying process models.
• Object-Centric Analysis: For the “declaration” object, semantic roles allowed reconstruction of its lifecycle, facilitating analysis of patterns such as approval/rejection cycles that would otherwise remain opaque.
• Resource and Collaboration Analysis: By separating “actor” and “passive” roles, collaboration structures and responsibilities could be inferred directly from log data.

This demonstrates that semantic log annotation enhances process intelligibility, supports complex querying, and enables object-centric or resource-centric analytics.

6. Implications for Process Mining and Research

The integration of semantic role labeling and attribute-level classification redefines the scope of process mining beyond raw activity/event sequences:

• Scalability and Automation: The use of BERT-based models and robust classification reduces the reliance on manual configuration, increases scalability to heterogeneous event logs, and promotes automation in model deployment.
• Advanced Analytic Capabilities: Explicit role annotations underpin advanced mining paradigms, such as organizational/resource mining and artifact-centric modeling, since object states, agent behaviors, and passive resource involvement become directly accessible.
• Directions for Extension: Future work may expand the scope of semantic roles (e.g., distinguishing human vs. system actors), integrate knowledge graphs for domain adaptation, or incorporate cross-domain ontologies for transfer learning.

A plausible implication is that continuous development of semantic log processing techniques will generalize process mining to new domains where traditional approaches—limited to raw activity sequences—are inadequate.

7. Limitations and Future Research

While the current method substantially advances semantic enrichment of event logs, limitations include:

• Dependence on High-Quality Annotated Data: Effective model training (especially of deep LLMs) relies on substantial, accurately labeled corpora covering the full diversity of potential event values.
• Ambiguous or Low-Context Attributes: For attributes with extremely sparse or ambiguous labeling, contextual insertion may still yield noisy role assignments; further model adaptation or unsupervised refinement may be needed.
• Integration with Domain Knowledge: The framework provides a foundation but does not yet exploit external ontologies or knowledge graphs—potentially limiting precise alignment in highly specialized or regulated domains.

Broader research opportunities include automated semantic role expansion, domain-specific adaptation, and direct integration of ontological or knowledge graph resources to further enhance the semantic interpretability and process mining applicability of event logs.

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In summary, semantic log processing as articulated in (Rebmann et al., 2021) offers a rigorous, automated, and scalable blueprint for enriching event logs with granular semantic roles. By uniting state-of-the-art contextual LLMs and novel attribute classification with a formal role ontology, it robustly transforms raw event data into process-aware, analytics-ready resources, thereby enabling next-generation process discovery, monitoring, and analysis.