The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded, allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.

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N L P
Semantic Relation Classification:
Task Formalisation and Refinement
Vivian S. Silva
Manuela Hürliman
Brian Davis
Siegfried Handschuh
André Freitas

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Outline
• Motivation
• Revisiting Semantic Relation Classification
 Using Foundational Ontologies (DOLCE)
• Systematic Analysis
• Summary

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Motivation

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Source: Ontotext
Introduction
Semantic Relation Classification (SRC) is a fundamental task in
NLP, allowing the induction of semantic representation models
for both commonsense and domain-specific data.
Source: W3C
Source: Semantrix

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Our Goals
• Improve the coverage, description and the
formalisation of the semantic relation
classification task
• Provide a critique and generalization of the
existing SemEval-2010 task 8

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Semantic Relation Classification
• SemEval-2010 task 8
 Most common semantic relation set
 Relations covered:
• Cause-Effect (CE)
• Instrument-Agency (IA)
• Product-Producer (PP)
• Content-Container (CC)
• Entity-Origin (EO)
• Entity-Destination (ED)
• Component-Whole (CW)
• Member-Collection (MC)
• Message-Topic (MT)

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Semantic Relations Classification
The <e1> burst </e1> has been caused by water
hammer <e2> pressure </e2>.

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Semantic Relations Classification
• Despite the obvious intuition around the utility of SRC…
Semantic relations set and
their expressive coverage
has not been fully
grounded with regard to
an ontological framework
1
When projecting these
semantic relations back to
the corpora-level, it can be
observed that the majority
of the words within a text
does not have a direct
semantic relationship
connecting them
2

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Semantic Relations Classification
• SemEval-2010 task 8
Has some constraints… …that brings some limitations
Focus on Nominals: only noun
phrases are considered
Locality Constraint: only relations for
arguments in the same clause
Focus on Concrete Relations: most
relation refer to physical objects
Exclusion of Conditionals: conditional
clauses not considered
No relations between events, when
represented by verbs, and their objects
No relations between terms belonging
to different, subordinate clauses
No relations for abstract entities or
quantitative/qualitative roles
No relations expressing conditional
dependencies

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Revisiting Semantic Relation
Classification

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Main question
• Given two sets of content words in a
sentence, can we provide a semantic
relation between them?
• Can this task be useful as a semantic
interpretation mechanism?

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Main strategy
• Start using foundational ontologies for this task
• Define relation compositions
• Expand the model with custom abstract relations
that stand on the interface between dependency
relations and an ontology-based representation

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Why Foundational Ontologies?
Representation ReasoningData
Foundational ontologies are intended to represent the
world in the way people perceive it, classifying entities
into categories that are familiar to people’s common sense
can represent data
in a formal way
can reason over
data using high-
level restrictions

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When is a foundational ontology
useful?
• 1. When subtle distinctions are important
• 2. When recognizing disagreement is important
• 3. When rigorous referential semantics is important
• 4. When general abstractions are important
• 5. When careful explanation and justification of
ontological commitment is important
• 6. When mutual understanding is more important
than interoperability.
Guarino, 2006

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DOLCE
• DOLCE (Descriptive Ontology for Linguistic and
Cognitive Engineering)
• Strong cognitive/linguistic bias:
 Descriptive (as opposite to prescriptive) attitude
 Categories mirror cognition, common sense, and the
lexical structure of natural language
 Emphasis on cognitive invariants

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DOLCE
• Any term can be mapped to a DOLCE high level
category (class)
• It’s always possible to find a relation between
any two DOLCE categories, and, therefore,
between the entities mapped to them

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DOLCE Relations
• 23 immediate relations and 25 mediated (composed)
relations, many of them having sub-relations. Some
examples:
immediate-relation mediated-relation
instrument
performed-by
target
functional-participant
part
references
resource
temporally-coincides
precedes
temporal-relation
abstract-location
co-participates-with
temporally-overlaps
temporally-includes
… …

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Applications: Simple Text Entailment Example
Assumption Mary is a mother
Hypothesis Mary gave birth
Commonsense KB a mother is a woman who has given birth
Foundational
Ontology
Mapping
Mary mother give birth
agent role action
(agent plays role)
(role performs action)
(agent performs action)
(agent plays role) and (role performs action) -> (agent performs action)
Foundational
classes
Commonsense
concepts
Foundational
relations

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Systematic Analysis

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Corpus-based Analysis
1. Corpus construction
• Focused on the financial domain (merges both commonsense
with domain-specific discourse)
• Contains both factoid and definition type of discourse
• We created a financial corpus by crawling two distinct types of
sources:
a) definitions, from three sources: b) articles, from two sources:
Bloomberg Financial Glossary
SGM Glossary
Investopedia Definitions
Wikipedia
Investopedia

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Corpus Construction
• Definitions
 Bloomberg financial Glossary (8324 definitions;
212,421 tokens)
 SGM Glossary (1007 definitions; 43,638 tokens)
 Investopedia Definitions4 (15476 definitions;
2,462,801 tokens),
• Articles
 Investopedia (5890 articles; 5,129,793 tokens)
 Wikipedia (articles on Investment and Finance;
8306 articles; 6,714,129 tokens).

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Corpus-based Analysis
1. Corpus construction
• We created a financial corpus by crawling two distinct types of
sources:
• Word pair selection:
 Corpus split into sentences
 First word randomly selected among the sentence tokens
 Second word manually selected
a) definitions, from three sources: b) articles, from two sources:
Bloomberg Financial Glossary
SGM Glossary
Investopedia Definitions
Wikipedia
Investopedia

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[…] the legislation's include a lifting of a 40-year ban on the United States' exporting of crude oil
Corpus-based Analysis
2. Manual Classification Analysis
• 300 pairs of words manually annotated
• Words mapped to DOLCE classes
• Relation between them chosen among the set of relations that
exist between the classes assigned to the words
• 3 different scenarios occurred:
a) Direct relationship:
b) Relation composition:
c) No relation found:
Concepts too far away
After 30 days the trustee can then use the contributions to pay the insurance policy premium
target
target target
indirect-target

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Custom Relations
• DOLCE relations can be defined specifically for a class, or be
inherited from an ancestor class
 In the second case, the kind of relationship can become too general
 To avoid semantically vague relations, we proposed a small set of
custom relations. A few examples:
Relation Example
Correlated variation It also decreases the value of the currency - potentially
stimulating exports and decreasing imports - improving the
balance of trade.
Ownership The lessor is the legal owner of the asset.
Sibling concept Operating activities include net income, accounts receivable,
accounts payable and inventory.
Value component Valuation of life annuities may be performed by calculating the
actuarial present value of the future life contingent payments.

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Some Statistics
• Most common DOLCE relations: patient, patient-of, target, target-of
• Most common custom relations: qualifier, indirect-target, ownership
Relation
type
DOLCE
Relation
Custom
Relations
Total
Direct 35.32% 64.68% 72.67%
Composite 48.65% 51.35% 24.67%
Unclassified - - 2.66%

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Semantic Relation X Semantic Relatedness
• The corpus was further annotated by two domain experts in finance
• Two human annotators scored each of the 300 concept pairs for
semantic relatedness on a scale from 0 (unrelated) to 10 (identical
or highly related)
 Average of their scores taken as final score
 Comparing the semantic relation to the semantic relatedness
score assigned to the same pair:

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Summary
• This work described a preliminary study on the
improvement of the coverage, description and the
formalisation of the semantic relation classification task
• A foundational ontology (DOLCE), composite relations and
custom semantic abstract relations were used
• DOLCE accounted for 38.2% of the semantic relations
• 67 % of the pairs were assigned to a direct relation
• 2.66% of the pairs could not be classified
• Relevant research questions:
 The impact of foundational ontology models in distributional and
compositional-distributional semantics.
Data available at: http://bit.ly/2gpTkHT

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Some Limitations (Currently being
addressed)
• Scaling corpus annotation size (currently 300
elements)
• Grounding the custom relations into the
foundational ontology

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Work in Progress
• Train an automatic annotator, capable of identifying FO
classes semantic relations in text.