Construct your knowledge graph (static, temporal, or temporal-dynamic) and detect weak signals

This repository brings together three complementary lines of work from Yassir Lairgi's PhD:

1. iText2KG — Incremental Knowledge Graphs Construction Using Large Language Models (WISE 2024)
2. ATOM — AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs (Findings of EACL 2026)
3. C-Unseen — Weak Signal Detection in Dynamic Temporal Knowledge Graphs via LLM Reasoning (WISE 2026)

Legacy iText2KG documentation remains in README_itext2kg.md.

       

🔥 News

• [04/09/2026] C-Unseen — Weak Signal Detection in DTKGs:

  • Paper released: C-Unseen: Weak Signal Detection in Dynamic Temporal Knowledge Graphs via LLM Reasoning (arXiv:2608.26870).
  • First definition of weak signals in DTKGs: a rare, semantically coherent subgraph that proliferates across consecutive TKG snapshots.
  • Two-module pipeline: Rare Subgraphs Extractor (LLM CoT identifies subgraphs in tension with the dominant snapshot narrative) + Weak Signal Alerter (tracks persistence / corroboration across time).
  • Self-interpretable: rare and weak-signal labels are written back onto the DTKG for inspection.
  • Outperforms keyword-, topic-, and graph-based weak-signal baselines.

• [04/09/2026] ATOM — End-to-end scripts & optional domains:

  • End-to-end builders: ready-to-run scripts under useful_scripts/ — build_atom_kg_from_json.py (temporal / dynamic KG from dated texts) and build_atom_kg_from_pdf.py (static KG from a PDF).
  • Optional domains on atomic facts: extract_atomic_facts / build_graph accept a domains list for noise filtering (facts can be labeled and filtered by domain). Default remains empty — behavior is unchanged vs. classic ATOM when domains are not provided.

• [20/10/2025] ATOM - Major Enhancements:

  • Complete Architectural Redesign: ATOM now employs a three-module parallel pipeline for DTKG construction and updates.
  • Atomic Fact Decomposition: A new first module splits text into minimal "atomic facts," addressing the "forgetting effect" where LLMs omit facts in longer contexts.
  • Enhanced Exhaustivity and Stability: The new architecture achieves significant gains: ~31% in factual exhaustivity, ~18% in temporal exhaustivity, and ~17% in stability.
  • Dual-Time Modeling: Implemented dual-time modeling (t_obs vs. t_start/t_end) to prevent temporal misattribution in dynamic KGs.
  • Parallel 5-Tuple Extraction: Module-2 now directly extracts 5-tuples (subject, predicate, object, t_start, t_end) in parallel from atomic facts.
  • Parallel Atomic Merge Architecture: Module-3 uses an efficient, parallel pairwise merge algorithm, achieving 93.8% latency reduction vs. Graphiti and 95.3% vs. iText2KG.
  • LLM-Independent Resolution: Replaced slow LLM-based resolution with distance metrics (cosine similarity) for scalable, parallel merging.

• [29/07/2025] iText2KG - New Features and Enhanced Capabilities:

  • iText2KG_Star: Introduced a simpler version that directly extracts relationships, eliminating the separate entity extraction step and reducing token consumption.
  • Facts-Based KG Construction: Enhanced the framework with facts-based KG construction using a Document Distiller.
  • Dynamic Knowledge Graphs: Added support for building dynamic KGs that evolve over time. See example: Dynamic KG Construction. NB: Temporal/logical conflicts resolution is not handled in this version.

• [19/07/2025] iText2KG - Major Performance and Reliability Updates:

  • Asynchronous Architecture: Migrated core methods to async/await for non-blocking I/O with LLM APIs.
  • Logging System: Implemented comprehensive logging to replace print statements.
  • Enhanced Batch Processing: Improved efficiency for handling multiple documents and LLM calls.
  • Better Error Handling: Added enhanced error handling and retry mechanisms.

• [07/10/2024] iText2KG - Latest features:

  • Refactored code with data models for Entity, Relation, and KnowledgeGraph.
  • Entities are embedded using both name (0.6 weight) and label (0.4 weight) to differentiate concepts (e.g., Python:Language vs. Python:Snake).
  • Added max_tries parameters to build_graph to handle LLM hallucinations.

• [17/09/2024] iText2KG - Latest features:

  • Compatibility with all LangChain chat and embedding models.
  • The build_graph function can now expand existing graphs.
  • Compatible with Python 3.9+.

• [16/07/2024] iText2KG - Addressed two major LLM hallucination issues:

  • Handled invented entities by replacing them with the most similar entity from the provided list.
  • Handled the "forgetting effect" (failing to assign relations) by re-prompting the LLM for missing entities.

---

ATOM

ATOM is a few-shot and scalable approach for building and continuously updating Temporal Knowledge Graphs (TKGs) from unstructured texts. It can also build static KGs (fix a single observation time) and dynamic temporal KGs (multiple observation times with dual-time modeling).

Overview

Traditional static KG construction often overlooks the dynamic and time-sensitive nature of real-world data, limiting adaptability to continuous changes. Moreover, recent zero- or few-shot approaches that avoid domain-specific fine-tuning or reliance on prebuilt ontologies often suffer from instability across multiple runs, as well as incomplete coverage of key facts.

ATOM splits input documents into minimal, self-contained “atomic” facts, improving extraction exhaustivity and stability. From these atomic facts, atomic KGs are derived and then merged in parallel.

In a nutshell, ATOM addresses these limitations by:

• ✅ Improving exhaustivity: Capturing comprehensive fact coverage from longer texts (~31% gain on factual exhaustivity, ~18% improvement in temporal exhaustivity)
• ✅ Ensuring stability: Producing consistent TKGs across multiple runs (~17% improvement)
• ✅ Enabling scalability: Supporting large-scale dynamic temporal updates through parallel architecture.

Architecture

ATOM employs a three-module parallel pipeline that constructs and continuously updates DTKGs from unstructured text.

Module-1 (Atomic Fact Decomposition) splits input documents D_t observed at time t into temporal atomic facts {f_{t,1}, ..., f_{t,m_t}} using LLM-based prompting with an optimal chunk size of <400 tokens, where each temporal atomic fact is a short, self-contained snippet that conveys exactly one piece of information.

Module-2 (Atomic TKGs Construction) extracts 5-tuples (quintuples) in parallel from each atomic fact f_{t,i} to construct atomic temporal KGs G^t_i, while embedding nodes and relations and addressing temporal resolution during extraction by transforming end validity facts into affirmative counterparts while modifying only the t_end time (e.g., "John Doe is no longer CEO of X on 01-01-2026" → (John_Doe, is_ceo, X, [.], [01-01-2026])).

Module-3 (Parallel Atomic Merge) employs a binary merge algorithm to merge pairs of atomic TKGs through iterative pairwise merging in parallel until convergence, with three resolution phases: (1) entity resolution using exact match or cosine similarity threshold θ_E = 0.8, (2) relation resolution merging relation names regardless of endpoints and timestamps using threshold θ_R = 0.7, and (3) temporal resolution that merges observation and validity time sets for relations with similar (e_s, r_p, e_o).

The resulting TKG snapshot G^t_s is then merged with the previous DTKG G^{t-1} to yield the updated DTKG: G^t.

---

Example of the ATOM Workflow

On observation date 09-01-2007, ATOM processes the fact "Steve Jobs was the CEO of Apple Inc. on January 9, 2007" to create the 5-tuple (Steve Jobs, is_ceo, Apple Inc., [09-01-2007], [.]) where t_start = [09-01-2007] and t_end = [.] (empty/unknown).

Later, on observation date 05-10-2011, ATOM processes the update "Steve Jobs is no longer the CEO of Apple Inc. on 05-10-2011". As described in Module-2, this end validity fact is transformed into its affirmative counterpart by modifying only the t_end time, producing (Steve Jobs, is_ceo, Apple Inc., [.], [05-10-2011]).

During Module-3's temporal resolution phase, ATOM detects that both 5-tuples share the same (e_s, r_p, e_o) triple and merges their time lists to produce the final 5-tuple: (Steve Jobs, is_ceo, Apple Inc., [09-01-2007], [05-10-2011]). This correctly represents that Steve Jobs was CEO from January 9, 2007 to October 5, 2011, while maintaining dual-time modeling with t_obs = [09-01-2007, 05-10-2011] to track when each piece of information was observed.

For more technical details, see:

• itext2kg/atom/atom.py: Core logic for building, merging, and updating knowledge graphs.

---

Latency & Scalability

ATOM achieves significant latency reduction (93.8% vs. Graphiti, 95.3% vs. iText2KG) by replacing serial bottlenecks with a fully parallel architecture.

Key architectural advantages include:

1. Parallel 5-Tuple Extraction: ATOM extracts 5-tuples in a single, parallelized step. This avoids the separate entity and relation extraction steps used by iText2KG and Graphiti, which double LLM calls and increase latency.
2. LLM-Independent Merging: The framework uses efficient distance metrics (cosine similarity) for entity/relation resolution. This avoids the computational bottlenecks of LLM-based resolution (used by Graphiti) and allows true parallelization as the graph scales.
3. Parallel Atomic Merge: Atomic TKGs are merged using an iterative pairwise algorithm, which runs in parallel (e.g., 8 threads with a batch size of 40).
4. Early Temporal Resolution: Temporal logic is handled during the extraction phase (Module-2), not during the merge phase.

As a result, the parallel merge process (Module-3) accounts for only 13% of ATOM's total latency. The remainder is attributed to API calls, which can be further minimized by increasing the batch size or scaling local LLM hardware.

---

Installation

pip install --upgrade itext2kg

LLM Compatibility

ATOM is compatible with all language models supported by LangChain. To use ATOM, you will need both a chat model and an embeddings model. For available chat models, refer to the options listed at: https://python.langchain.com/docs/integrations/chat/. For embedding models, explore the choices at: https://python.langchain.com/docs/integrations/text_embedding/

Please ensure that you install the necessary package for each chat model before use.

ATOM Arguments

Initialization:

• llm_model: A LangChain chat model instance for extraction
• embeddings_model: A LangChain embeddings model instance for entity/relation matching
• kg_store_dir (Path | str, optional): If set, each build_graph call persists {obs_timestamp}.json (+ .npz for embeddings); multi-obs merges also write merged.json / merged.npz

extract_atomic_facts function:

• texts (List[str]): Input texts (chunks / paragraphs) to decompose
• observation_timestamp (str): Observation date used to ground relative temporal expressions
• domains (List[str], optional): Allowed domain labels. Empty / omitted → no domain classification (DomainedFact.domain stays empty). Non-empty → each fact is labeled with one of the allowed domains

build_graph function: (This function can also build static KGs: fix a single observation time and pass your atomic facts.)

• atomic_facts (List[str]): A list of atomic facts (short, self-contained text snippets) to process
• obs_timestamp (str): The observation timestamp when the atomic facts were collected
• existing_knowledge_graph (KnowledgeGraph, optional): An existing knowledge graph to merge with the new one
• domains (List[str], optional): Per-fact domain labels; if provided, length must match atomic_facts. Non-empty domains are written onto relationships
• ent_threshold (float, default=0.8): Similarity threshold for entity resolution during merging
• rel_threshold (float, default=0.7): Similarity threshold for relationship resolution during merging
• entity_name_weight (float, default=0.8): Weight for entity name in similarity calculations
• entity_label_weight (float, default=0.2): Weight for entity label in similarity calculations
• max_workers (int, default=8): Maximum number of parallel workers for processing

build_graph_from_different_obs_times function:

• atomic_facts_with_obs_timestamps (dict): Keys are observation timestamps (str); values are lists of atomic facts for each timestamp
• existing_knowledge_graph (KnowledgeGraph, optional): An existing knowledge graph to merge with the new ones
• domains_with_obs_timestamps (dict, optional): Same keys as atomic_facts_with_obs_timestamps; values are per-fact domain lists passed through to build_graph
• ent_threshold (float, default=0.8): Similarity threshold for entity resolution during merging
• rel_threshold (float, default=0.7): Similarity threshold for relationship resolution during merging
• entity_name_weight (float, default=0.8): Weight for entity name in similarity calculations
• entity_label_weight (float, default=0.2): Weight for entity label in similarity calculations
• max_workers (int, default=8): Maximum number of parallel workers for processing

Examples: Building KGs with ATOM

⚠️ Performance note: For optimal performance, run ATOM in dedicated Python scripts rather than Jupyter notebooks. ATOM's parallel processing can slow down under notebook event-loop / thread contention.

Ready-to-run utility scripts

End-to-end builders live under useful_scripts/:

Script	What it builds
build_atom_kg_from_json.py	Dynamic / temporal KG from a JSON list of dated texts (atomic facts → per-row snapshots → merged KG as JSON + NPZ)
build_atom_kg_from_pdf.py	Static KG from a PDF (PyMuPDF extract → token gate / semantic chunking → atomic facts → one build_graph at a single observation time)

Edit the globals at the top of each script (paths, model names, thresholds, Neo4j toggle), then:

python useful_scripts/build_atom_kg_from_json.py
python useful_scripts/build_atom_kg_from_pdf.py

Minimal example (dynamic TKG)

The following builds a dynamic TKG from atomic facts of the 2020-COVID-NYT dataset.

import pandas as pd
import asyncio
import ast

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from itext2kg.atom import Atom
from itext2kg.graph_integration import Neo4jStorage

openai_api_key = "#"
openai_llm_model = ChatOpenAI(
    api_key=openai_api_key,
    model="gpt-4.1-2025-04-14",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
)

openai_embeddings_model = OpenAIEmbeddings(
    api_key=openai_api_key,
    model="text-embedding-3-large",
)

news_covid = pd.read_pickle("./datasets/atom/nyt_news/2020_nyt_COVID_last_version_ready.pkl")

def to_dictionary(df: pd.DataFrame, max_elements: int | None = 20):
    if isinstance(df["factoids_g_truth"][0], str):
        df["factoids_g_truth"] = df["factoids_g_truth"].apply(lambda x: ast.literal_eval(x))
    grouped_df = df.groupby("date")["factoids_g_truth"].sum().reset_index()[:max_elements]
    return {
        str(date): factoids
        for date, factoids in grouped_df.set_index("date")["factoids_g_truth"].to_dict().items()
    }

news_covid_dict = to_dictionary(news_covid)

atom = Atom(llm_model=openai_llm_model, embeddings_model=openai_embeddings_model)

kg = await atom.build_graph_from_different_obs_times(
    atomic_facts_with_obs_timestamps=news_covid_dict,
)

URI = "bolt://localhost:7687"
USERNAME = "neo4j"
PASSWORD = "##"
Neo4jStorage(uri=URI, username=USERNAME, password=PASSWORD).visualize_graph(knowledge_graph=kg)

Evaluation Scripts, Dataset and Prompts

• ATOM: COVID-19 NYT dynamic temporal dataset in ./datasets/atom (also on Hugging Face). Evaluation scripts: ./evaluation/atom (exhaustivity, stability, latency, merge, quintuples_quality). Prompts: ./itext2kg/atom/models/.
• C-Unseen: datasets under ./datasets/c-unseen; run / ablation / scoring scripts under ./evaluation/c_unseen. Prompts: ./itext2kg/c_unseen/models/.

---

C-Unseen

C-Unseen is a self-interpretable framework for weak signal detection in Dynamic Temporal Knowledge Graphs (DTKGs). A weak signal is defined as a rare, semantically coherent subgraph that proliferates across consecutive TKG snapshots.

Overview

Keyword-, topic-, and untyped-graph methods miss the semantic and relational structure through which weak signals appear. C-Unseen operates on DTKGs (e.g. those built with ATOM) through two modules:

1. Rare Subgraphs Extractor — an LLM with chain-of-thought reasoning identifies subgraphs whose content is in tension with the dominant snapshot narrative.
2. Weak Signal Alerter — tracks those rare subgraphs across time steps to isolate true weak signals via corroboration.

Experimental results show that C-Unseen outperforms keyword-, topic-, and graph-based baselines. Paper: arXiv:2608.26870.

Package entry points

• Core API: itext2kg/c_unseen/c_unseen.py (CUnseen.process_snapshot / process_snapshots)
• Rare elements detector & weak-signal alerter under itext2kg/c_unseen/
• Evaluation / run scripts under evaluation/c_unseen/

from itext2kg.c_unseen import CUnseen
from itext2kg.c_unseen.models import SignalKnowledgeGraph

c_unseen = CUnseen(llm_model=openai_llm_model, embeddings_model=openai_embeddings_model)

# kg: SignalKnowledgeGraph snapshot; previous_kgs: prior snapshots for corroboration
await c_unseen.process_snapshot(
    kg=kg,
    previous_kgs=previous_kgs,
    snapshot_label="2024-01-16",
    central_entity_name="OpenAI",
)

---

Public Collaboration

We welcome contributions from the community to improve ATOM, C-Unseen, and iText2KG.

Citation

If you use this work, please cite the relevant paper(s):

@inproceedings{lairgi2024itext2kg,
  title={itext2kg: Incremental knowledge graphs construction using large language models},
  author={Lairgi, Yassir and Moncla, Ludovic and Cazabet, R{\'e}my and Benabdeslem, Khalid and Cl{\'e}au, Pierre},
  booktitle={International Conference on Web Information Systems Engineering},
  pages={214--229},
  year={2024},
  organization={Springer}
}

@inproceedings{lairgi2026atom,
  title={ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs},
  author={Lairgi, Yassir and Moncla, Ludovic and Benabdeslem, Khalid and Cazabet, R{\'e}my and Cl{\'e}au, Pierre},
  booktitle={Findings of the Association for Computational Linguistics: EACL 2026},
  pages={950--966},
  year={2026}
}

@article{lairgi2026c,
  title={C-Unseen: Weak Signal Detection in Dynamic Temporal Knowledge Graphs via LLM Reasoning},
  author={Lairgi, Yassir and Moncla, Ludovic and Benabdeslem, Khalid and Cazabet, R{\'e}my and Cl{\'e}au, Pierre},
  journal={arXiv preprint arXiv:2608.26870},
  year={2026}
}