Can we make an AI respect TEI XML?
An experiment with a Small-Scale explainable AI
Presentation for DH2025 in Lisbon, Portugal and Balisage: The Markup Conference 2025
Wednesday, 16 July 2025 and Wednesday, 6 August 2025
Alexander C. Fisher, Hadleigh Jae Bills, and Elisa Beshero-Bondar
Penn State Erie, The Behrend College
Link to Slides:
https://bit.ly/
digitai-dh25
What DigitAI will be
A tool that assists scholars and editors in applying the TEI Guidelines to their own corpora
- Built for users who are new to TEI or seeking deeper understanding
- Acts as a TEI aware tutor NOT an Auto-Encoder
- Helps scholars and editors understand best practices in TEI encoding
- Helps TEI Developers to find inconsistencies in the guidelines
Supports learning by providing relevant examples, explanations, and excerpts directly from the TEI Guidelines
Presentation for DH2025 in Lisbon, Portugal
Wednesday, 16 July 2025
Why TEI?
- One of us regularly edits TEI Guidelines
- TEI Guidelines : delivered as
- website (and pdf, etc)
- a code schema (TEI-all.rng)
- most importantly, built in TEI XML
-
p5.xml = a single highly structured, systematic code base and knowledge resource, containing:
- chapters
- examples
- "coding specs" that define the building blocks of the TEI elements, attributes, macros, modules, datatypes, etc.
- built from component parts when we release new versions of the Guidelines
- one of the best-organized resources we can imagine for designing a knowledge base for a RAG system*
* Literally p5.xml is designed from the ground-up to be the TEI community's "ground truth" about our encoding system!
Presentation for DH2025 in Lisbon, Portugal
Wednesday, 16 July 2025
p5.xml → XSLT → digitai-p5.json
TEI Guidelines p5.xml: a single XML document that contains the entire built TEI Guidelines. It's a hierarchical (tree) structure:
-
contains front matter, 24 Chapters, each with sections and nested subsections.
-
Paragraphs in chapters
- contain encoding that defines rules for elements and attribute classes, model classes (connecting related elements and attributes together), datatype specifications.
-
Paragraphs in chapters
- We are writing XSLT to translate this XML tree into
- a JSON structure (digitai-p5.json) for a database to ingest
- Query scripts (in Cypher) to direct this database to translate this JSON into a knowledge graph
Designing the Memory Graph
- Stores TEI Guidelines as Nodes and Relationships
- Enables retrieval of structured and semantically relevant text
- Preserves relationships of the XML structure
- Queried using Cypher, Neo4j's native query language
- Allows us to retrieval precise, contextual excerpts
Neo4j is like a digital card catalog— it doesnt just store texts, it understands how they relate to each other
WITH doc, doc_data
FOREACH (part_data_1 IN doc_data.CONTAINS_PARTS |
MERGE (part:Part {name: part_data_1.PART}) SET part.sequence = part_data_1.SEQUENCE
MERGE (doc)-[:HAS_PART]->(part)
WITH part, part_data_1
FOREACH (chapter_data_2 IN part_data_1.CONTAINS_CHAPTERS |
MERGE (chapter:Chapter {chapter_id: chapter_data_2.ID})
SET chapter.sequence = chapter_data_2.SEQUENCE,
chapter.title = chapter_data_2.CHAPTER,
chapter.links = [x IN chapter_data_2.RELATES_TO WHERE x IS NOT NULL | x.ID]
MERGE (part)-[:HAS_CHAPTER]->(chapter)
WITH chapter, chapter_data_2 FOREACH (example_data_7 IN paragraph_data_6.TEI_ENCODING_DISCUSSED.CONTAINS_EXAMPLES |
MERGE (example:Example {example: example_data_7.EXAMPLE})
SET example.language = example_data_7.LANGUAGE
MERGE (paragraph)-[:HAS_EXAMPLE]->(example)
WITH example, example_data_7
FOREACH (paragraph_data_8 IN example_data_7.CONTAINS_END_PARAS |
MERGE (paragraph:TerminalPara {parastring: paragraph_data_8.PARASTRING})
SET paragraph.files_mentioned =
[x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.FILES_MENTIONED
WHERE x IS NOT NULL | x.FILE],
paragraph.parameter_entities_mentioned =
[x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.PES_MENTIONED WHERE x IS NOT NULL | x.PE],
paragraph.elements_mentioned = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.ELEMENTS_MENTIONED
WHERE x IS NOT NULL | x.ELEMENT_NAME], paragraph.attributes_mentioned =
[x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.ATTRIBUTES_MENTIONED
WHERE x IS NOT NULL | x.ATTRIBUTE_NAME],
paragraph.sequence = paragraph_data_8.SEQUENCE,
paragraph.frags_mentioned = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.FRAGS_MENTIONED
WHERE x IS NOT NULL | x.FRAG],
paragraph.ns_mentioned = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.NSS_MENTIONED
WHERE x IS NOT NULL | x.NS],
paragraph.classes_mentioned = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.CLASSES_MENTIONED
WHERE x IS NOT NULL | x.CLASS],
paragraph.modules_mentioned = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.MODULES_MENTIONED
WHERE x IS NOT NULL | x.MODULE],
paragraph.macros_mentioned = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.MACROS_MENTIONED
WHERE x IS NOT NULL | x.MACRO],
paragraph.speclist_links = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.CONTAINS_SPECLISTS.SPECLIST
WHERE x IS NOT NULL | x.ID],
paragraph.relaxng_mentioned = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.RNGS_MENTIONED
WHERE x IS NOT NULL | x.RNG],
paragraph.datatypes_mentioned = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.DATATYPES_MENTIONED
WHERE x IS NOT NULL | x.DATATYPE], paragraph.links = [x IN paragraph_data_8.RELATES_TO.SECTION
WHERE x IS NOT NULL | x.ID],
paragraph.parameter_entities_mentioned_ge = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.PES_MENTIONED
WHERE x IS NOT NULL | x.GE],
paragraph.schemas_mentioned = [x IN paragraph_data_8.TEI_ENCODING_DISCUSSED.SCHEMAS_MENTIONED
WHERE x IS NOT NULL | x.SCHEMA]
MERGE (example)-[:HAS_END_PARAGRAPH]->(paragraph)
) Neo4j's Graph Model JSON... Beyond Human Readability?
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:0", "text": null, "labels": ["Document"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:1", "text": null, "labels": ["Part"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:2", "text": null, "labels": ["Part"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:3", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:4", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:5", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:6", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:7", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:8", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:9", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:10", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:11", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:12", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:13", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:14", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:15", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:16", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:17", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:18", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:19", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:20", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:21", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:22", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:23", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:24", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:25", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:26", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:27", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:28", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:29", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:30", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:31", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:32", "text": null, "labels": ["Chapter"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:33", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:34", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:35", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:36", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:37", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:38", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:39", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:40", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:41", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:42", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:43", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:44", "text": null, "labels": ["Section"]}
{"id": "4:223785fa-2e25-4598-887d-0a5b446743b2:45", "text": null, "labels": ["Section"]}Designing a RAG with the TEI
Making a knowlege graph: the challenge!
<XML>
<XSLT>
Neo4j
Knowledge Graph
LLM
Nodes
Relationships
<Cypher>
Core 4— Qwen2:7B: The LLM
FAISS
Neo4j Database
BGE-M3
Qwen LLM
Neo4j Database
BGE-M3
FAISS
RAG Embeddings
.JSONL
RAG Embeddings
.FAISS
Embedded Prompt
Generates Vector Embeddings from Node Text
RAG Embeddings Converted to FAISS Formatted Data Map
Data Map Compared to Embedded Query
Constructs Query
Top Matched Embeddings Retrieved as Text From Neo4j
Human Prompt
# === Get user input ===
query = input("❓ Enter your query: ").strip()
if not query:
print("⚠️ No query provided. Exiting.")
exit()# === Encode the query ===
query_embedding = model.encode(query)
# Convert to 2D array and normalize if cosine similarity is enabled
if normalize:
query_embedding = sk_normalize([query_embedding], norm="l2")
else:
query_embedding = np.array([query_embedding])
query_embedding = query_embedding.astype("float32")
# === Perform FAISS similarity search ===
TOP_K = 5 # Number of top matching texts to retrieve
scores, indices = index.search(query_embedding, TOP_K)
# Filter out invalid results (-1 = no match)
matched_ids = [id_map[i] for i in indices[0] if i != -1]
if not matched_ids:
print("⚠️ No relevant matches found in the FAISS index.")
exit()# === Fetch matching texts from JSONL file ===
def fetch_node_texts_by_ids(node_ids):
texts = []
with open(neo4jNodes, "r", encoding="utf-8") as f:
for line in f:
record = json.loads(line)
if record.get("id") in node_ids and record.get("text"):
texts.append(record["text"])
return texts
texts = fetch_node_texts_by_ids(matched_ids)
if not texts:
print("❌ No node texts found for matched IDs in local file.")
exit()# === Construct prompt for the LLM ===
context = "\n".join(f"- {text}" for text in texts)
prompt = f"""You are a chatbot that helps people understand
the TEI guidelines which specify how to encode machine-readable
texts using XML.
Answer the question below in the **same language the question is asked in**.
Use examples from the provided context as needed — they can be in any language.
Do not translate them.
Context:
{context}
Question:
{query}
"""
# === Send prompt to local Ollama LLM ===
def ask_ollama(prompt, model):
try:
response = requests.post(
"http://localhost:11434/api/generate",
json={
"model": model,
"prompt": prompt,
"stream": False
}
)
return response.json().get("response", "[ERROR] Empty response from LLM.")
except Exception as e:
print("❌ Error while querying Ollama:", e)
return "[ERROR] Could not get response from local LLM."
# === Query the model ===
print(f"🤖 Sending prompt to LLM ({llm_model})...")
answer = ask_ollama(prompt, llm_model)
# === Display the result ===
print("\n🧾 Response:\n")
print(answer)XSLT Process so far
What did we learn?
- Data structures and graph modeling
- How to write Cypher Scripts
- Differences between "fine-tuning", "RAG augmenting" and "training"
- This is harder than we thought!!!
- Also accessible to learn together (students and professor alike!
Presentation for DH2025 in Lisbon, Portugal
Wednesday, 16 July 2025
What's next for DigitAI?
- Finish refining the knowledge graph
- Fine-tuning with the text and code specs of the P5 Guidelines
- Optimization and packaging (user interface over local network, docker container)
Presentation for DH2025 in Lisbon, Portugal
Wednesday, 16 July 2025
Questions?
Presentation for DH2025 in Lisbon, Portugal
Wednesday, 16 July 2025
https://bit.ly/
digitai-github
GitHub:
https://bit.ly/
digitai-dh25
Slides:
https://bit.ly/
digitai-dh25
https://bit.ly/
digitai-jupyter
Jupyter Notebooks:
