# WP44 Ontology of Computer-Science Research (v1.0, 2026-11-03)

Agent D (Taxonomy Architect). This document describes the extensible hierarchical ontology used to classify every work of the WP44 corpus into the 40 leaf categories of CLAUDE.md section 4 (multi-label). The mapping itself is data: it lives in taxonomy/src/*.py (one module per area), is validated and serialised by taxonomy/build_ontology.py into taxonomy/ontology.yaml (the machine-readable ontology) and taxonomy/topic_map.csv (the OpenAlex-topic audit table). Everything between the two markers below is generated by the script and must not be edited by hand; everything outside the markers is written by hand.

# 1. Design

Three levels. Eight top-level areas group the 40 leaves (every leaf belongs to exactly one area); a small set of parent_of / child_of edges records where a leaf is a specialisation of another leaf (llm and multimodal_ai under foundation_models; compilers under programming_languages; cloud_computing and edge_computing under distributed_computing; federated_learning under privacy_preserving_computation; ai_accelerators under computer_architecture); related edges record the dense is-related-to graph between leaves. The areas are an organisational device for the manuscript (Parts V–VII) and for reporting; classification operates on leaves only.

Five signal types, one rule. Each leaf lists weighted signals of five kinds: OpenAlex topics (from any field, not only field 17), OpenAlex subfields (coarse fallback), arXiv categories, DBLP venue streams, and keyword regexes over title and/or abstract; optional exclusion regexes veto the leaf. The assignment rule is deliberately simple so that another agent can audit it: for each signal type the weights of matched signals are summed and capped at 1.0; the capped values are summed across types; a work belongs to a leaf if the score is at least 1.0, and the primary leaf is the argmax (ties: child before parent, then lower category number). A weight of 1.0 therefore means "this signal alone suffices" (e.g. cs.CV for computer_vision, the regex large language models? for llm); a weight of 0.3–0.6 means "corroborate with another signal type".

Keyword-sufficient categories. Six AI categories that OpenAlex topics cannot separate (LLM, foundation models, multimodal, generative AI, agents, AI safety) and seven small categories (federated learning, sustainable computing, autonomous scientific discovery, privacy-preserving computation, blockchain, quantum computing, edge computing) have at least one keyword pattern of weight 1.0; the build fails otherwise. Patterns use word boundaries, require a computing qualifier on ambiguous tokens (<qualifier> agent, <qualifier> accelerator, <qualifier> architecture) and never rely on single ambiguous words such as "agent", "model", "cloud", "edge" or "kernel". Where a polysemous term is unavoidable, an anchored exclusion of the form ^(?!.*<computing term>).*<non-computing sense> vetoes the leaf only when no computing term appears anywhere in the text.

Provenance. Every leaf carries a provenance note explaining how its mapping was built (which OpenAlex keyword lists were inspected, why a venue is partial, which senses are vetoed). Topic weights were set by reading the OpenAlex keyword lists of all 302 field-17 topics and of about 120 CS-adjacent topics from Mathematics, Engineering, Decision Sciences, Physics, Biochemistry, Social Sciences and Medicine; stream weights by venue purity (a venue that publishes only the category gets 1.0; shared venues 0.2–0.8); arXiv weights by the category's correspondence to the arXiv taxonomy.

# 2. Known ambiguities (decisions taken)

• LLM vs foundation model vs generative AI vs NLP. foundation_models is the parent concept (any modality, adaptation-oriented); llm is its language child and is the home of ChatGPT/GPT/LLaMA-era work; generative_ai is about the generative model and its outputs (diffusion, GANs, text-to-X, deepfakes), not about text generation by LLMs as such; nlp keeps task-framed language work. Multi-label assignment is expected: an instruction-tuning paper scores in llm and nlp, a diffusion-LLM paper in llm and generative_ai. The OpenAlex topic T10028 ("Topic Modeling") is in fact the host of BERT/pre-trained-LM/RAG work and is weighted 1.0 for llm.
• AI agents vs reinforcement learning vs robotics. Software autonomy (LLM agents, MAS, planning) is ai_agents; learning from reward is reinforcement_learning; physical embodiment is robotics. "Agent" alone never counts; chemical/medical/commercial agents are vetoed.
• AI safety vs cybersecurity vs ML theory (XAI). Adversarial examples, backdoors and prompt injection score in both ai_safety (0.8) and cybersecurity (0.8); fairness and governance belong to ai_safety; XAI as a statistical method is ml_theory (0.5) and as a safety practice ai_safety (interpretability of LLMs, 1.0).
• Cloud vs distributed vs edge. distributed_computing is the parent (algorithms, HPC systems, consensus); cloud_computing is the service model (virtualisation, serverless, data-centre resource management); edge_computing is placement near users/devices (offloading, MEC, on-device inference). IoT topics are shared between edge_computing and computer_networks.
• Cryptography vs security vs privacy. Primitives and protocols are applied_cryptography; attacks, defences and systems hardening are cybersecurity; using cryptography or statistics to protect data is privacy_preserving_computation, whose child federated_learning has no OpenAlex topic or venue of its own and is keyword-driven. T10764 ("Privacy-Preserving Technologies in Data") hosts both the parent (1.0) and the child (0.7).
• Computer architecture vs AI accelerators vs operating systems. Accelerators are a child of architecture and include efficient-inference systems (vLLM, FlashAttention, quantisation for hardware); OS has no OpenAlex topic and is carried by OSDI/SOSP/EuroSys/ATC/FAST streams, cs.OS and kernel/file-system keywords.
• Quantum computing vs quantum physics. Only computing/information-processing framings count; Physics topics on qubit platforms are weighted 0.2–0.3 and a veto removes quantum-physics texts without any computing term.
• Scientific computing vs computational biology vs autonomous discovery. Numerical method → scientific computing; biological object → computational biology; AI driving the scientific process itself → autonomous discovery (keyword-driven, with a veto when no AI/automation term is present).
• Algorithms vs complexity. STOC/FOCS/ICALP are shared; ECCC/ToCT and cs.CC are complexity-only; metaheuristics and evolutionary computation are counted as algorithms (0.5–0.6) because the corpus will contain many of them.
• Education, media and other field-17 residues. OpenAlex places 67 education/media/physics/generic clusters in field 17; 63 of them are deliberately unmapped (listed with reasons below), 4 were mapped at low weight.

# 3. Limitations

The weights are expert priors, not fitted parameters; Phase 2 must validate them on the manually reviewed sample (agreement statistics) and may re-weight. Venue weights are per DBLP stream, so workshop volumes sharing a stream key with a flagship conference inherit its weight. OpenAlex keyword lists were read in full for the field-17 topics and for the CS-adjacent topics named in the modules, but not for the remaining ~4,000 topics; a few relevant topics from Medicine or Materials Science may therefore be missing and should be added when the corpus reveals them.

Generated by taxonomy/build_ontology.py on 2026-11-03 from openalex_topics_2026-10-14.csv (4,516 topics) and dblp_streams_2018_2026.csv (3,569 streams). Do not edit between the markers.

# Areas and leaves

# The 40 categories

1. Large Language Models (llm, area ai_ml)
Research whose object is a transformer-based language model trained at scale: pre-training, instruction tuning, prompting and in-context learning, reasoning, retrieval augmentation, efficient inference, evaluation and benchmarks of LLMs. Excludes classical NLP tasks solved with small task-specific models (nlp) and non-language foundation models (foundation_models).
child of foundation_models; related to foundation_models, nlp, generative_ai, ai_agents, ai_safety.

2. Foundation Models (foundation_models, area ai_ml)
Large pre-trained models intended for adaptation to many downstream tasks in any modality: scaling laws, pre-training objectives, self-supervised representation learning at scale, transfer, fine-tuning and adapter methods, model releases and their evaluation. Language-specific work is llm (a child); vision-language work is multimodal_ai (a child).
parent of llm, multimodal_ai; related to llm, multimodal_ai, generative_ai, deep_learning_architectures, ml_theory.

3. Multimodal AI (multimodal_ai, area ai_ml)
Models and systems that jointly process two or more modalities (vision-language, audio-visual, video-text, 3D-language, sensors with language): vision-language models and multimodal LLMs, captioning, visual question answering, cross-modal retrieval and grounding. Unimodal vision is computer_vision; pure speech is speech_audio.
child of foundation_models; related to computer_vision, nlp, llm, speech_audio, generative_ai.

4. Generative AI (generative_ai, area ai_ml)
Models that synthesise new content (images, video, audio, 3D, text, code, molecules) and their training, sampling, evaluation, detection and misuse: GANs, VAEs, diffusion and flow models, autoregressive generators, text-to-X synthesis, deepfakes and AI-generated-content detection. LLM text generation as such is llm unless the contribution concerns the generative model or its outputs' authenticity.
related to llm, multimodal_ai, computer_vision, computer_graphics, foundation_models, speech_audio.

5. AI Agents and Autonomous Systems (ai_agents, area ai_ml)
Software agents that perceive, decide and act with some autonomy: LLM-based agents and tool use, multi-agent systems, planning and automated reasoning for acting, agent architectures, negotiation, agentic workflows and benchmarks, embodied agents in simulated environments. Physical robots are robotics; RL algorithms as such are reinforcement_learning.
related to llm, reinforcement_learning, robotics, ai_safety, autonomous_scientific_discovery.

6. Reinforcement Learning (reinforcement_learning, area ai_ml)
Learning to act from reward signals: value- and policy-based methods, model-based RL, offline and inverse RL, multi-agent RL, bandits, exploration, reward design, RL from human feedback, and RL theory. Applications to robot control are shared with robotics.
related to ai_agents, robotics, ml_theory, ai_safety, llm.

7. Machine Learning Theory (ml_theory, area ai_ml)
Statistical and computational learning theory and principled ML methodology: generalisation and sample complexity, optimisation theory for learning, kernel and Bayesian methods, probabilistic models, causal inference, online learning, PAC learning, interpretability and fairness as formal problems. Architecture engineering is deep_learning_architectures.
related to deep_learning_architectures, reinforcement_learning, algorithms, computational_complexity, ai_safety, foundation_models.

8. Deep Learning Architectures (deep_learning_architectures, area ai_ml)
Design, training and compression of neural network architectures as such: CNNs, RNNs, transformers and attention, graph neural networks, state-space models, normalisation and optimisers in practice, neural architecture search, pruning, quantisation and distillation, neuromorphic and spiking networks as models. Task-specific results go to the application category; hardware goes to ai_accelerators.
related to ml_theory, computer_vision, nlp, ai_accelerators, foundation_models, llm.

9. Computer Vision (computer_vision, area perception_interaction)
Extracting information from images, video and 3D data: recognition, detection, segmentation, tracking, pose, depth and 3D reconstruction, image restoration and enhancement, medical and remote-sensing image analysis, visual representation learning. Image synthesis is generative_ai; vision-language is multimodal_ai; rendering is computer_graphics.
related to multimodal_ai, computer_graphics, robotics, deep_learning_architectures, generative_ai, computational_biology.

10. Natural Language Processing (nlp, area perception_interaction)
Computational processing of human language: machine translation, parsing and tagging, information extraction, sentiment and text classification, summarisation, question answering, dialogue, text mining, computational linguistics, low-resource and multilingual NLP, NLP evaluation and resources. LLM-centred work is llm (also classified here when the task framing dominates).
related to llm, speech_audio, information_retrieval, multimodal_ai, computational_biology.

11. Speech and Audio Computing (speech_audio, area perception_interaction)
Processing and generation of speech, music and general audio: speech recognition and synthesis, speaker and language recognition, enhancement and separation, spoken language understanding, music information retrieval and generation, audio event detection, neural audio codecs. Text-only dialogue is nlp.
related to nlp, multimodal_ai, generative_ai, llm.

12. Robotics (robotics, area perception_interaction)
Machines that sense and act in the physical world: manipulation, locomotion, motion and path planning, SLAM and localisation, robot learning, human-robot interaction, autonomous vehicles and drones as robotic systems, soft and swarm robotics, teleoperation. RL algorithms are reinforcement_learning; robotic process automation and surgical-outcome studies are excluded.
related to reinforcement_learning, computer_vision, ai_agents, hci, edge_computing.

13. Human-Computer Interaction (hci, area perception_interaction)
Design, evaluation and understanding of interactive systems and their users: user studies and usability, interaction techniques, VR/AR/XR, accessibility and assistive technology, CSCW and social computing, human-AI interaction, ubiquitous and wearable computing, affective computing. Rendering is computer_graphics; physical robots are robotics.
related to computer_graphics, robotics, ai_safety, multimodal_ai, software_engineering, information_retrieval.

14. Software Engineering (software_engineering, area software_pl)
How software is specified, built, tested, maintained and studied: requirements, architecture, testing and debugging, program repair, mining software repositories, empirical studies of developers, DevOps, model-driven engineering, open-source ecosystems, and AI-assisted software development. Language design is programming_languages; proofs of correctness are formal_verification.
related to programming_languages, formal_verification, llm, hci, ai_agents, operating_systems.

15. Programming Languages (programming_languages, area software_pl)
Design, semantics, type systems and implementation principles of programming languages: type theory, operational and denotational semantics, functional and logic programming, effects, concurrency models, memory safety, domain-specific languages, program analysis as a language problem. Code generation and optimisation pipelines are compilers (a child); machine-checked correctness is formal_verification.
parent of compilers; related to compilers, formal_verification, software_engineering, computational_complexity.

16. Compilers (compilers, area software_pl)
Translation and optimisation of programs for execution: compiler construction, intermediate representations, optimisation passes, code generation, JIT compilation, auto-vectorisation and parallelisation, polyhedral methods, high-level synthesis, and compilers for machine-learning workloads and accelerators. A child of programming_languages.
child of programming_languages; related to programming_languages, ai_accelerators, computer_architecture, scientific_computing, distributed_computing.

17. Formal Verification (formal_verification, area software_pl)
Mathematically rigorous establishment of system properties: model checking, theorem proving and proof assistants, SAT/SMT solving and automated reasoning, deductive and runtime verification, verified software and hardware, formal semantics as a verification tool, verification of neural networks, smart contracts and protocols, automata and temporal logics. Language semantics as design is programming_languages.
related to programming_languages, software_engineering, computational_complexity, applied_cryptography, ai_safety, computer_architecture.

18. Algorithms and Data Structures (algorithms, area theory)
Design and analysis of algorithms and data structures: graph, string, geometric, approximation, randomized, online, streaming, parallel and parameterized algorithms, combinatorial optimisation and metaheuristics, algorithmic game theory, algorithm engineering, and the combinatorics that underpins them. Lower bounds and hardness as such are computational_complexity; numerical algorithms are scientific_computing.
related to computational_complexity, ml_theory, computer_graphics, database_systems, distributed_computing, scientific_computing.

19. Computational Complexity (computational_complexity, area theory)
The intrinsic difficulty of computational problems: complexity classes and their separations, hardness of approximation, circuit, proof, communication and query complexity, fine-grained and parameterized complexity, pseudorandomness and derandomization, computability and automata theory, algebraic complexity, and quantum complexity. Algorithm design is algorithms; cryptographic hardness assumptions are applied_cryptography.
related to algorithms, formal_verification, quantum_computing, applied_cryptography, ml_theory.

20. Database Systems (database_systems, area data_distributed)
Storage, querying and management of structured and semi-structured data: query processing and optimisation, transactions and concurrency control, indexing and storage engines, data integration, cleaning and provenance, big-data processing frameworks, data mining as a data-management problem, learned database components, vector and graph databases. Ranking of unstructured documents is information_retrieval.
related to information_retrieval, distributed_computing, cloud_computing, algorithms, ml_theory.

21. Information Retrieval (information_retrieval, area data_distributed)
Finding and ranking relevant information for a user need: search engines and ranking models, dense and sparse retrieval, recommender systems, query understanding, evaluation and test collections, web search and mining, scholarly and digital-library retrieval, retrieval components of RAG systems. Structured query processing is database_systems; text understanding tasks are nlp.
related to nlp, database_systems, llm, hci, ml_theory.

22. Distributed Computing (distributed_computing, area data_distributed)
Computation across multiple processors or machines: distributed algorithms and consensus, fault tolerance and replication, parallel programming and HPC systems software, cluster and grid resource management, distributed storage and middleware, distributed and parallel ML training systems. Cloud service models are cloud_computing and edge placement is edge_computing (both children).
parent of cloud_computing, edge_computing; related to cloud_computing, edge_computing, computer_networks, database_systems, operating_systems, scientific_computing, blockchain, federated_learning.

23. Cloud Computing (cloud_computing, area data_distributed)
Computing delivered as elastic, multi-tenant, on-demand services: IaaS/PaaS/SaaS and serverless, virtualisation and containers, resource provisioning and auto-scaling, SLAs and pricing, cloud-native architectures, data-centre resource management, cloud security and migration. A child of distributed_computing; placement at the network edge is edge_computing.
child of distributed_computing; related to distributed_computing, edge_computing, operating_systems, computer_networks, sustainable_computing, cybersecurity.

24. Edge Computing (edge_computing, area data_distributed)
Computation placed near data sources and users: edge/fog/MEC architectures, computation offloading, on-device and TinyML inference, edge-cloud collaboration and split computing, IoT platforms as computing substrates, latency-critical edge services, edge caching and orchestration. Radio access and protocols are computer_networks; model compression as such is deep_learning_architectures or ai_accelerators.
child of distributed_computing; related to cloud_computing, computer_networks, federated_learning, ai_accelerators, sustainable_computing, robotics, distributed_computing.

25. Computer Networks (computer_networks, area data_distributed)
Design, protocols, measurement and performance of communication networks: Internet architecture and transport, routing, wireless and mobile networks, 5G/6G and software-defined networking, network management, sensor and vehicular networks, content delivery and caching, network coding and information theory applied to networking. Physical-layer signal processing is included only when framed as networking.
related to distributed_computing, edge_computing, cybersecurity, cloud_computing, applied_cryptography.

26. Cybersecurity (cybersecurity, area security_privacy)
Protecting computing systems, networks, software and users from attack: vulnerability discovery and exploitation, malware and intrusion detection, authentication and access control, system and hardware security, web and mobile security, digital forensics, security of AI systems, security operations and human factors of security. Cryptographic primitives and protocols are applied_cryptography; data-protection techniques are privacy_preserving_computation.
related to applied_cryptography, privacy_preserving_computation, computer_networks, ai_safety, blockchain, formal_verification, operating_systems, software_engineering.

27. Applied Cryptography (applied_cryptography, area security_privacy)
Design, analysis, implementation and deployment of cryptographic primitives and protocols: symmetric and public-key schemes, hash functions, signatures, key exchange, post-quantum cryptography, zero-knowledge proofs, secure multi-party computation and homomorphic encryption as primitives, cryptanalysis, side-channel-resistant implementations, cryptographic hardware, provable security, quantum key distribution. Deployment of these primitives for data protection is privacy_preserving_computation; ledgers are blockchain.
related to cybersecurity, privacy_preserving_computation, blockchain, quantum_computing, computational_complexity, computer_architecture.

28. Blockchain Systems (blockchain, area security_privacy)
Distributed ledgers and the systems built on them: consensus for permissioned and permissionless chains, smart contracts and their security, scalability (sharding, layer-2, rollups), cryptocurrencies and DeFi mechanisms, blockchain applications and governance, measurement of blockchain ecosystems. Generic BFT consensus theory is shared with distributed_computing; primitives are applied_cryptography.
related to applied_cryptography, distributed_computing, cybersecurity, privacy_preserving_computation, computer_networks.

29. Computer Architecture (computer_architecture, area hardware_emerging)
Organisation and design of processors, memory systems and digital hardware: microarchitecture, caches and memory hierarchies, interconnects and networks-on-chip, FPGAs and reconfigurable computing, embedded and real-time systems, hardware/software co-design, electronic design automation, VLSI and low-power design, processing-in-memory. Accelerators specialised for ML are ai_accelerators (a child); kernels are operating_systems.
parent of ai_accelerators; related to ai_accelerators, operating_systems, compilers, sustainable_computing, quantum_computing, distributed_computing, cybersecurity.

30. Accelerators and AI Hardware (ai_accelerators, area hardware_emerging)
Hardware and hardware-aware software for efficient machine-learning computation: GPU/TPU/NPU design and programming, ASIC and FPGA DNN accelerators, neuromorphic and spiking hardware, in-memory and analog/photonic computing for AI, quantisation and sparsity exploited by hardware, efficient LLM inference and serving systems, ML compilers' hardware mapping, benchmarks such as MLPerf. A child of computer_architecture.
child of computer_architecture; related to computer_architecture, deep_learning_architectures, compilers, edge_computing, sustainable_computing, distributed_computing, llm.

31. Operating Systems (operating_systems, area hardware_emerging)
System software that manages hardware and provides execution abstractions: kernels and drivers, scheduling, memory management, file and storage systems, virtualisation and containers, OS security and isolation, I/O and networking stacks, real-time and embedded OS, systems support for new hardware (persistent memory, CXL, SmartNICs). Distributed coordination is distributed_computing; hardware design is computer_architecture.
related to computer_architecture, distributed_computing, cloud_computing, cybersecurity, programming_languages, edge_computing.

32. Quantum Computing (quantum_computing, area hardware_emerging)
Computation and information processing exploiting quantum mechanics: quantum algorithms and complexity, quantum error correction and fault tolerance, qubit hardware platforms and control, quantum software, compilers and simulators, variational and NISQ algorithms, quantum machine learning, quantum communication and key distribution, quantum networking. Quantum physics without a computational or information-processing framing is out of scope; post-quantum cryptography is applied_cryptography.
related to applied_cryptography, computational_complexity, computer_architecture, scientific_computing, compilers.

33. Scientific Computing (scientific_computing, area computational_science)
Numerical and computational methods and software for mathematical models of science and engineering: numerical linear algebra, discretisation of differential equations, solvers and preconditioners, simulation frameworks, uncertainty quantification, reduced-order and surrogate models, scientific machine learning (PINNs, neural operators), high-performance scientific software and performance portability, mathematical software, computational physics/chemistry/engineering tooling. Domain biology is computational_biology; parallel systems software is distributed_computing.
related to distributed_computing, computational_biology, algorithms, computer_graphics, ai_accelerators, autonomous_scientific_discovery, compilers, ml_theory.

34. Computational Biology (computational_biology, area computational_science)
Computational methods for biological and biomedical data: bioinformatics and genomics, protein structure and design, molecular modelling and computational drug discovery, single-cell and multi-omics analysis, systems and synthetic biology, biomedical text mining, computational neuroscience, biomedical and health informatics methods, DNA/molecular computing. Medical image analysis is shared with computer_vision; numerical methods as such are scientific_computing.
related to scientific_computing, nlp, computer_vision, ml_theory, autonomous_scientific_discovery, privacy_preserving_computation, generative_ai.

35. Computer Graphics (computer_graphics, area perception_interaction)
Synthesis and manipulation of visual content from models: rendering, geometry processing and modelling, animation and simulation for graphics, neural rendering and 3D representations, visualisation, procedural content, game and VR engines. Image understanding is computer_vision; pixel-space generative models are generative_ai; user studies of VR are hci.
related to computer_vision, hci, generative_ai, scientific_computing, algorithms.

36. Privacy-Preserving Computation (privacy_preserving_computation, area security_privacy)
Computing on, releasing or sharing data while limiting what is learned about individuals: differential privacy, homomorphic encryption, secure multi-party computation and TEEs applied to data analysis and ML, anonymisation and synthetic data, private information retrieval, privacy attacks on models and their defences, machine unlearning, privacy engineering and regulation (GDPR). Federated learning is a child; cryptographic constructions as such are applied_cryptography.
parent of federated_learning; related to applied_cryptography, cybersecurity, ai_safety, database_systems, blockchain, federated_learning, ml_theory.

37. Federated Learning (federated_learning, area security_privacy)
Training models across multiple clients or institutions without centralising their raw data: federated averaging and optimisation under heterogeneity, communication efficiency, client selection, personalisation, secure aggregation and privacy of federated systems, robustness to malicious clients, vertical and cross-silo federated learning, federated fine-tuning of foundation models, decentralised/gossip learning. A child of privacy_preserving_computation; also linked to distributed_computing and edge_computing.
child of privacy_preserving_computation; related to privacy_preserving_computation, distributed_computing, edge_computing, ml_theory, ai_safety, foundation_models.

38. AI Safety and Alignment (ai_safety, area ai_ml)
Making AI systems safe, aligned, robust, fair, interpretable and governable: alignment and preference learning framed as a safety problem, red-teaming and jailbreaks, adversarial robustness and backdoors, hallucination and truthfulness, bias and fairness, mechanistic interpretability, evaluation of dangerous capabilities, AI governance and regulation. Generic system security is cybersecurity; XAI as a statistical method only is ml_theory.
related to llm, ml_theory, cybersecurity, privacy_preserving_computation, ai_agents, hci, reinforcement_learning.

39. Sustainable Computing (sustainable_computing, area hardware_emerging)
The energy, carbon, water and material footprint of computing and how to reduce it: energy-efficient and carbon-aware systems, data-centre energy and cooling, Green AI and the cost of training and inference, life-cycle and embodied carbon of hardware, green software engineering and networking, energy harvesting and intermittent computing, computing for sustainability when the object studied is the computing system. Generic low-power circuit design is computer_architecture; energy-efficient communication is computer_networks.
related to cloud_computing, computer_architecture, ai_accelerators, edge_computing, distributed_computing, computer_networks, ai_safety.

40. Autonomous Scientific Discovery (autonomous_scientific_discovery, area computational_science)
AI and automation systems that perform or substantially drive the scientific process: AI scientist and research-agent systems, automated hypothesis generation and literature synthesis, self-driving laboratories and closed-loop experimentation, AI-driven discovery of materials, molecules, equations, algorithms or theorems, autonomous experimental design, benchmarks for research agents, and the epistemology and evaluation of machine-generated science. Using ML inside a scientific pipeline without autonomy is scientific_computing or computational_biology; LLM agents in general are ai_agents.
related to ai_agents, llm, scientific_computing, computational_biology, formal_verification, algorithms, information_retrieval, ai_safety.

# Hierarchy (parent → child edges)

• foundation_models → llm
• foundation_models → multimodal_ai
• programming_languages → compilers
• distributed_computing → cloud_computing
• distributed_computing → edge_computing
• computer_architecture → ai_accelerators
• privacy_preserving_computation → federated_learning

# Signal counts per category

Totals: 646 topic→category weights over 410 distinct topics; 1320 stream→category weights over 771 distinct streams; 273 keyword patterns; 24 exclusion patterns.

# OpenAlex coverage

• Field 17 (Computer Science): 240 of 302 topics mapped (79.5 %); 62 left unmapped with an explicit reason below.
• Topics mapped from other fields: 170 — 11 Agricultural and Biological Sciences: 1; 12 Arts and Humanities: 5; 13 Biochemistry, Genetics and Molecular Biology: 18; 14 Business, Management and Accounting: 1; 18 Decision Sciences: 8; 19 Earth and Planetary Sciences: 2; 20 Economics, Econometrics and Finance: 1; 21 Energy: 1; 22 Engineering: 71; 23 Environmental Science: 3; 25 Materials Science: 1; 26 Mathematics: 20; 27 Medicine: 6; 28 Neuroscience: 4; 31 Physics and Astronomy: 15; 32 Psychology: 2; 33 Social Sciences: 10; 36 Health Professions: 1.
• Topics mapped to more than one category: 146 of 410.

# Mapped CS topics per subfield

# Unmapped CS topics (field 17) with reason

# DBLP coverage

• Streams mapped: 771 (any rank); records covered (2018–2026, excluding CoRR): 2,017,910 of 3,319,884 (60.8 %).
• Of the 599 largest streams (CoRR excluded), 496 are mapped (82.8 %). The 103 unmapped large streams are listed below; they are either out of scope (medicine, geoscience, education, management, electronics) or too generic to be a category signal (IEEE Access, Sensors, Symmetry).

# Topics mapped to several categories (audit list)