=Paper=
{{Paper
|id=Vol-2456/paper2
|storemode=property
|title=Extending Ontologies in the Nanotechnology Domain using Topic Models and Formal Topical Concept Analysis on Unstructured Text
|pdfUrl=https://ceur-ws.org/Vol-2456/paper2.pdf
|volume=Vol-2456
|authors=Huanyu Li,Rickard Armiento,Patrick Lambrix
|dblpUrl=https://dblp.org/rec/conf/semweb/0001AL19
}}
==Extending Ontologies in the Nanotechnology Domain using Topic Models and Formal Topical Concept Analysis on Unstructured Text==
https://ceur-ws.org/Vol-2456/paper2.pdf
Extending Ontologies in the Nanotechnology
Domain using Topic Models and Formal Topical
Concept Analysis on Unstructured Text
Huanyu Li, Rickard Armiento, and Patrick Lambrix
The Swedish e-Science Research Centre & Linköping University, Linköping, Sweden
firstname.lastname@liu.se ??
Abstract. In the data-driven workflows in the materials science domain,
much of the data and knowledge is stored in different heterogeneous
data sources maintained by different groups. This leads to a reduced
availability of the data and poor interoperability between systems in
this domain. Ontology-based techniques are an important way to reduce
these problems and a number of efforts have started. In this paper, we use
a phrase-based topic model approach and formal topical concept analysis
on unstructured text in this domain to suggest additional concepts and
axioms for the ontology that should be validated by a domain expert.
1 Introduction
More and more researchers in materials science have realized that data-driven
techniques could accelerate the discovery and design of materials. Therefore, a
large number of research groups and communities have developed data-driven
workflows including data repositories (for an overview see [4]) and data ana-
lytics tools for particular purposes. Taking nanotechnology as an example, [6]
states that there exists a gap between data generation and shared data access.
The domain lacks standards for collecting and systematically representing nano-
material properties. To solve these challenges, it is proposed that ontologies and
ontology-based techniques can play a significant role in the data-driven materials
science as ontologies provide a formal and explicit representation of knowledge
of a domain which will enable reproduction, sharing and integration of data.
However, developing ontologies is not an easy task and often the resulting
ontologies are not complete. In addition to being problematic for the correct
modelling of a domain, such incomplete ontologies also influence the quality
of semantically-enabled applications such as ontology-based search and data
integration. Incomplete ontologies when used in semantically-enabled applica-
tions can lead to valid conclusions being missed. For instance, in ontology-based
search, queries are refined and expanded by moving up and down the hierar-
chy of concepts. Incomplete structure in ontologies influences the quality of the
search results.
??
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
We propose a novel method for extending existing ontologies by detecting
new concepts that should be included in the ontologies. We do this by presenting
an approach, formal topical concept analysis, that integrates a variant of topic
modeling and formal concept analysis.
2 Approach
Our approach for extending ontologies, shown in Fig. 1, contains the following
steps. In the first step creation of a phrase-based topic model documents related
to the domain of interest are used to create topics (upper part in Fig. 1). We
use the phrases-based topic model in the ToPMine system [1]. Given a corpus of
documents and the number of requested topics, representations of latent topics
in the documents are computed. The phrases as well as the topics are suggestions
that a domain expert should validate or interpret and relate to concepts in the
ontology. In the second step the (possibly validated and updated) topics are used
in a formal topical concept analysis which returns suggestions to the domain
expert regarding relations between topics and thus concepts in the ontology. We
define a new variant of formal concept analysis (e.g., [2]) and use this new variant
on topics (lower part in Fig. 1). These topics can come directly from the previous
step or can be a modified version of the topics of the previous step, where non-
relevant topics or phrases are removed. Both steps lead to the addition of new
concepts and (subsumption) axioms to the ontology.
Fig. 1. Approach.
As shown in Fig. 1, a domain expert is involved in the different steps in our
approach to validate and interpret the results of the phrase-based topic model
and the formal topical concept analysis in terms of interpreting all phrases
appearing in all topics, interpreting topics using the representative phrases or
a subset of representative phrases in a topic. The outcome can be divided into
Table 1. Results
label Nanoparticle eNanoMapper
# of general concepts No-g 14 12
# of existing concepts EXIST(-m) 46 52
# of new concepts ADD(-m) 35 32
# of new axioms ADD(-m) 42 37
# of queries Q 49 49
# of influenced concepts - 72 37
existing knowledge (EXIST(-m)), and new knowledge ADD(-m), where the ‘-m’
qualifier denotes that a modified version of the label exists or should be added
(e.g., core-shell nanoparticle for core shell ). Further, the label could refer to a
too general concept for the ontology (No-g) or not represent relevant knowledge
(No). For the topic interpretation there is an additional outcome (Q) referring
to too specific concepts for the ontology, but such concepts could be defined
using concepts in the ontologies and OWL constructs. Finally, the domain expert
interprets the formal concept analysis-based lattice and may find new
concepts and subsumption axioms.
3 Experiments and Results
Data and Experiments. The corpus that we use is based on reports on
nanoparticles from the Nanoparticle Information Library (http://nanoparticlelibrary.
net). For each nanoparticle report, we take the text in ‘Research Abstract’
as well as the abstracts (or only the titles if there is no abstract) from the
publications in ‘Related Publications’. The final corpus contains 627 abstracts
(or titles). The ontologies that we extend are the Nanoparticle ontology [5]
(1904 concepts and 81 relations) and the eNanoMapper ontology [3] (12,531
concepts and 4 relations). Both ontologies are available via BioPortal (https:
//bioportal.bioontology.org/).
Results and discussion of results. In Table 1 we show the results regarding
the interpretation of phrases, topics and lattice nodes from the experiments.
We show the number of general concepts (No-g), existing concepts (EXIST(-
m)), new concepts (ADD(-m)), new axioms (ADD(-m)), queries (Q) and influ-
enced concepts by adding the new axioms. Our results showed that the approach
generated many EXIST(-m) cases. This provides a sanity check for our approach
as it shows that existing concepts can be found. Further, the approach found 35
and 32 new concepts for the NanoParticle ontology and the eNanoMapper ontol-
ogy respectively, as well as 42 and 37 new axioms. In addition to the new concepts
and new axioms, also other concepts are influenced. Indeed, for a new axiom A
is-a B, the sub-concepts of A receive B and all its super-concepts as its super-
concepts (and thus inherit their properties), and all super-concepts of B receive
A and its sub-concepts as sub-concepts (and thus all instances of these concepts
are also instances of B and its super-concepts). In this experiment, 72 concepts
from NanoParticle ontology were influenced by the new axioms. Therefore, the
quality of NanoParticle ontology-enabled applications is improved whenever one
of the 35 new or 72 influenced concepts is used. For the eNanoMapper ontology
the number of influenced existing concepts by adding new axioms is 37.
For the experiments we have currently used few resources, i.e. circa 600 ab-
stracts and less than 10 hours for each of three experts (a domain expert and two
knowledge engineering experts). As the quality of the ontologies and their use is
raised, it is clear that the effort for extending the ontologies was worth-while.
4 Conclusions
In this paper we have used a phrase-based topic model approach and our own
variant of formal concept analysis for extending ontologies. A domain expert
interprets the results which are phrases, topics and a lattice. This leads to the
confirmation of ontological concepts (EXIST(-m)) or to the addition of new
concepts and axioms (ADD(-m)). The latter is the actual extension of the on-
tologies. Also, concepts from more general or other domains may be found, as
well as very specific concepts in the domain that need not be added to the ontol-
ogy. We have shown the usefulness of the approach by extending two ontologies
in the nanotechnology domain using approximately 600 abstracts.
One issue that the domain expert noted was that it was not always easy to
decide which level of granularity to use during the interpretation. In the future
we will investigate how to help the domain expert dealing with this issue. In
particular, the lattice appears to help refining topics into concepts that are more
general and meaningful in the domain. This may be a useful step forward towards
a higher level of automation in the process of extracting ontology information
out of unstructured text. Furthermore, we will investigate the scalability of our
approach by experimenting with more documents. Another possible direction is
to investigate synergy possibilities between the topics and the ontology concepts,
e.g., by using the ontologies to generate the corpora, or by iterating between topic
generation and interpretation.
References
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