The document presents a hierarchical approach for indexing and extracting terminology from semi-structured documents. It extracts the logical structure of documents, indexes terms at different levels of the hierarchy using top-down propagation, and mines semantic relations between terms. It constructs a taxonomy and identifies similarity measures and possible classifications of terms based on their relations. Experimental results on a corpus of web pages show the approach effectively extracts hypernyms, hyponyms and relations between terms. Future work involves exporting the extracted knowledge as an ontology and improving retrieval and evaluation.

1. University of Tunis El Manar, Faculty of sciences of Tunis
A hierarchical Approach for Semi-Structured
Document Indexing and Terminology
Extraction
By Ibrahim Bounhas & Yahya Slimani

2. Outline
•
•
•
–
–
–
–
•
Introduction
Related work
The proposed approach
Logical Structure Extraction
Hierarchical Indexing
Logical Structure Mining
Semantic Relations Inferring
Experimentation and Results

3. Introduction
•
Structured information retrieval:
Documents have a structure (e.g. papers,
books, encyclopaedia):
1. How to index documents and fragments?
2. How to exploit the structure for terminology
extraction and organisation?
3. How to model queries?
4. What to retrieve? a document, a section, a
paragraph,…,etc.
5. How to evaluate results?

4. Related work
• Method of indexing
– Disjoint units: ignore the structure of documents
 Unable to characterize and retrieve internal
nodes.
– Path indexing: Path indexing consists in identifying
terms that discriminate each path
 Unable to characterize and retrieve internal
nodes
 Unable to discriminate nodes having the same
path and different content.
– Bottom-up
index
propagation:
terms
are
propagated from child nodes to their parents using
statistical measures (e.g. tf-idf, entropy)
 Unable to distinguish terms which index
composite nodes having the same level.
 Terms weighting does not take into account levels
of text nodes

5. Type of Structural Information
• Type of Structural Information
– Physical structure: physical attributes constitute the
context of text nodes: e.g. HTML or XHTML tags. 
tags are very ambiguous: the same tag may have
different usages for different authors.
– Content tags: XML tags describe the logical role of
each node  logical tags does not characterize
semantically the content.
– Logical structure: the hierarchy of titles of the
document is explicitly represented.

6. Structure Based Knowledge Extraction
• Existing approaches:
– Extract instances of the concepts of a given ontology
(Kuo et al, 2006)
 Need an existing ontology + highly structured
documents.
– Concept extraction based on HTML tags (Kruschwitz,
2005; Karoui, 2008)
 No relations between concepts.
– Co-hyponyms detection from XHTML pages (Brunzel
and Spiliopoulou, 2006)
 No vertical relations

7. The proposed approach
1.
Logical Structure Extraction (Bounhas and
Slimani, 2009a):
–
Physical analyser: identify physical blocks and font
styles.
–
Macro-logical analyser: attribute a level to each text
block.
 The title of the document gets the level M and
paragraphs the level 1.

8. The proposed approach
2.
Hierarchical
Indexing
with
propagation:
a. The number of occurrences:
top-down
LogOcc(t , d ) = ∑ occ(t , ndi ) * level (ndi )
i
b. The frequency
LogFreq (t , d ) = LogOcc(t , d ) / ∑ LogOcc(ti, d )
i
c. Apply TF-IDF for documents
LogWeight (t , d ) = LogFreq(t , d ) * log( N / Nt )
d. Apply iteratively the same formulae to
index nodes: define a threshold for index
propagation from a node to its children.

9. The proposed approach
3.
Logical Structure Mining  a contextual network
of terms
–
The Ancestor-child Relation
∀ i ∈d , ∀ j ∈d ,
nd
nd
path( ndi, ndj ), level ( ndi ) > level ( ndj )
∀ i ∈ndi, ∀ j ∈ndj , ti ≠ tj ⇒
t
t
Sup (ti , tj ) = LogWeight (tj , ndj ) /(level ( ndi ) −level ( ndj ))
–
The Brother Relation
∀p ∈ d , level ( p ) = 1
∀ti ∈ p, ∀tj ∈ p , ti ≠ tj ⇒ sim(tj , ti ) = ( weight (ti, p ) + weight (tj , p )) / 2

10. The proposed approach
4.
Semantic Relations Inferring
–
Taxonomy Construction
•
•
–
Select the root:
i
Build the other levels by using the “Sup” relation
RootWeight (t ) = ∑Sup (t , ti )
Similarity Measuring and Points of View Study
The LLR score
The contingency table
LLR (u, v) = -2 log(
L(O11, C1, r) * L(O12, C2, r)
)
L(O11, C1, r1) * L(O12, C2, r2)
t1=v
t1≠v
t2=u
O11
O12
where
T2≠u
O21
O22
L(k,n,r ) = k r * (1 −r ) n−k
r = R1 / N , r1 =O11 / C 1, r 2 =O12 / C 2

11. Experimentation and Results
• The corpus: 182 Web pages = 11.61 M.B
• Preparing the corpus: lemmatisation, stop words and
MWT handling: integrate a linguistic toolbox: (Bounhas
and Slimani, 2009b)
• Evaluation metrics: Precision, recall and F-measure in
1,2
1
0,8
Recall
0,6
Precision
0,4
F-measure
0,2
0
0
•
terms of the extracted relations.
The propagation threshold
0,2
0,4
0,6
0,8

12. ‫‪Experimentation and Results‬‬
‫‪Hypernyms of‬‬
‫)‪ » (lion of the sea‬أسد البحر“‬
‫‪Hyponyms of‬‬
‫)‪” (amphibians‬برمائيات“‬
‫‪Terms ranked by‬‬
‫‪the RootWeight‬‬
‫‪Score‬‬
‫‪Term‬‬
‫‪Score‬‬
‫‪Term‬‬
‫‪RootWeight‬‬
‫‪Term‬‬
‫52,5‬
‫برمائيات‬
‫40,9‬
‫سمندجر‬
‫03,392‬
‫حيوان‬
‫80,2‬
‫حيوانات مفترسة‬
‫40,9‬
‫ضفدع‬
‫94,45‬
‫حبليات‬
‫20,0‬
‫حيوان‬
‫40,9‬
‫علجوم‬
‫08,05‬
‫حيوانات مائية‬
‫40,9‬
‫علجوم أخضر‬
‫77,64‬
‫حشرة‬
‫40,9‬
‫علجوم سوجرينام‬
‫14‬
‫حيوانات مفترسة‬
‫40,9‬
‫فيل البحر‬
‫83‬
‫حيوانات برية‬
‫62,5‬
‫أسد البحر‬
‫23‬
‫حيوانات عاشبة‬
‫62,5‬
‫فقمة‬
‫62,52‬
‫حافريات‬
‫52‬
‫جرأسيات الجرجل‬
‫73,42‬
‫فقاجريات‬
‫96,22‬
‫ثانويات الفم‬
‫62,61‬
‫بقريات‬
‫51‬
‫ذوات القوقعة النابية‬
‫..…‬

13. ‫‪Experimentation and Results‬‬
‫‪Similarity Measuring and Points of View Study‬‬
‫برمائيات‬
‫حيوانات عاشبة‬
‫حيوانات برية‬
‫حيوانات مفترسة‬
‫حيوانات مائية‬
‫0‬
‫54,41‬
‫0‬
‫81,01‬
‫69,661‬
‫حيوانات مائية‬
‫7,0‬
‫0‬
‫45,0‬
‫54,141‬
‫81,01‬
‫حيوانات مفترسة‬
‫0‬
‫49,3‬
‫66,26‬
‫45,0‬
‫0‬
‫حيوانات برية‬
‫2,0‬
‫44,44‬
‫49,3‬
‫0‬
‫54,41‬
‫حيوانات عاشبة‬
‫11,15‬
‫2,0‬
‫0‬
‫7,0‬
‫0‬
‫برمائيات‬
‫:‪• Two possible classifications‬‬
‫‪ (terrestrial‬حيوانات برية ,)‪ (marine animals‬حيوانات مائية –‬
‫)‪ (amphibians‬برمائيات ,)‪animals‬‬
‫)‪ (herbivores‬حيوانات عاشبة ,)‪ (carnivores‬حيوانات مفترسة –‬

14. Conclusion and future work
• Results:
– A model for semi-structured document
analysis and indexing
– An approach for structured based
terminology extraction and organisation
 a toolbox for document analysis and
knowledge extraction
• Future work
– Export knowledge as an ontology
– How to model queries?
– What to retrieve? a document, a section, a
paragraph,…,etc.
– How to evaluate results?