=Paper=
{{Paper
|id=None
|storemode=property
|title=PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods
|pdfUrl=https://ceur-ws.org/Vol-654/paper3.pdf
|volume=Vol-654
|dblpUrl=https://dblp.org/rec/conf/semweb/AbeyruwanVLS10
}}
==PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods==
https://ceur-ws.org/Vol-654/paper3.pdf
PrOntoLearn: Unsupervised Lexico-Semantic
Ontology Generation using Probabilistic
Methods
Saminda Abeyruwan1 , Ubbo Visser1 , Vance Lemmon2 , and Stephan Schürer3
1
Department of Computer Science, University of Miami, Florida, USA
{saminda,visser}@cs.miami.edu
2
The Miami Project to Cure Paralysis, University of Miami Miller School of
Medicine, Florida, USA
vlemmon@miami.edu
3
Department of Molecular and Cellular Pharmacology, University of Miami Miller
School of Medicine, Florida, USA
sschuerer@med.miami.edu
Abstract. Formalizing an ontology for a domain manually is well-known
as a tedious and cumbersome process. It is constrained by the knowledge
acquisition bottleneck. Therefore, researchers developed algorithms and
systems that can help to automatize the process. Among them are sys-
tems that include text corpora for the acquisition. Our idea is also based
on vast amount of text corpora. Here, we provide a novel unsupervised
bottom-up ontology generation method. It is based on lexico-semantic
structures and Bayesian reasoning to expedite the ontology generation
process. We provide a quantitative and two qualitative results illustrat-
ing our approach using a high throughput screening assay corpus and
two custom text corpora. This process could also provide evidence for
domain experts to build ontologies based on top-down approaches.
Keywords: Ontology Modeling, Ontology Learning, Probabilistic Meth-
ods
1 Introduction
An ontology is a formal, explicit specification of a shared conceptualization [10],
[22]. Formalizing an ontology for a given domain with the supervision of domain
experts is a tedious and cumbersome process. The identification of the struc-
tures and the characteristics of the domain knowledge through an ontology is a
demanding task. This problem is known as the knowledge acquisition bottleneck
(KAB) and a suitable solution presently does not exist.
There exists a large number of text corpora available from different domains
(e.g., the BioAssay high throughput screening assays4 ) that need to be classified
into ontologies to faciliate the discovery of new knowledge. A domain of discourse
4
http://bioassayontology.org/
(i.e., sequential number of sentences) shows characteristics such as 1) redundancy
2) structured and unstructured text 3) noisy and uncertain data that provide a
degree of belief 4) lexical disambiguity, and 5) semantic heterogeneity problems.
We discuss in depth the importance of these characteristics in section 3. Our
goal in this research is to provide a novel method to construct an ontology from
the evidence collected from the corpus. In order to achieve our goal, we use the
lexico-semantic features of the lexicon and probabilistic reasoning to handle the
uncertainty of features. Since our method is applied to build an ontology for
a corpus without domain experts, this method can be seen as an unsupervised
learning technique. Since the method starts from the evidence present in the
corpus, it is can be seen as a reverse engineering technique. We use WordNet5 to
handle lexico-semantic structures, and the Bayesian reasoning to handle degree
of belief of an uncertain event. We implement a Java based application to serialize
the learned conceptualization to OWL DL6 format.
The rest of the paper is organized as follows: section 2 provides a broad inves-
tigation of the related work. Section 3 provides details of our research approach.
Section 4 provides a detail description of the experiments based on three dif-
ferent text corpora and the discussion. Finally, section 5 provides the summary
and the future work.
2 Related Work
The problem of learning a conceptualization from a corpus has been studied in
many disciplines such as machine learning, text mining, information retrieval,
natural language processing, and Semantic Web. Table 1 shows the pros and cons
of different techniques to solve the problem of ontology learning. Each method
covers some portion of the problem and each method learns the conceptualization
from terms, and present it as taxonomies and axioms to an ontology. On the other
hand, most of the methods use a top-down approach, i.e., an initial classification
of an ontology is given. The uncertainty inherited from the domain is usually
dealt with by a domain expert, and the conceptualization is normally defined
using predefined rules or templates. These methods show the characteristics of
a semi-supervised and a semi-automated learning paradigm.
3 Approach
Our research focuses on an unsupervised method to quantify the degree of belief
that a grouping of words in the corpus will provide a substantial conceptual-
ization of the domain of interest. The degree of belief in world states influences
the uncertainty of the conceptualization. The uncertainty arises from partial
observability, non-determinism, laziness and theoretical and practical ignorance
[19]. The partial observability arises from the size of the corpus. Even though
5
http://wordnet.princeton.edu/
6
http://www.w3.org/TR/owl-guide/
Table 1. The summary of the related work. Probabilistic learning (PR), never end-
ing language learning (NELL), discovery and aggregation of relations in text (DART),
recognizing textual entailment (RTE), automated theorem proving (ATP), natural lan-
guage understanding (NLU), formal concept analysis (FCA), and ontology population
(OP).
Work Purpose T-Box A-Box Method
PR [9], [12], [14] and [17] reasoning available available prob. theory
NELL [3] 24 × 7 learning fixed dynamic ML techniques
DART [7] world knowledge × × semi-automated
RTE [2], and [13] entailment × × ATP
NLU [20] commonsense rules × × semi-supervised
√ √
Text2Onto [6] ontology learning semi-supervised
√
LexO [24] complex classes × semi-supervised
√
FCA [5] taxonomy × FCA
OP [4], and [23] ontology population available available semi-/supervised
a corpus many be large, it might not contain all the necessary evidence of an
event of interest. A corpus contains ambiguous statements about an event that
leads to a non-determinism of the state of the event. The laziness arises from
the too much work that needs to be done in order to learn exceptionless rules
and it is too hard to learn such rules. The theoretical and practical ignorance
arises from lack of complete evidence and it is not possible to conduct all the
necessary tests to learn a particular event. Hence, the domain knowledge, and
in our case the domain conceptualization, can at best provide only a degree of
belief of the relevant groups of words. We use probability theory to deal with the
degrees of belief. As mentioned in [19], the probability theory has the same onto-
logical commitment as the formal logic, though the epistemological commitment
differs. The process of learning and presenting a probabilistic conceptualization
is divided into four phases as shown in Figure 1. They are, 1) pre-processing
2) syntactic analysis 3) semantic analysis, and 4) representation.
3.1 Pre-processing
A corpus contains a plethora of structured and unstructured sentences. A lexicon
of a language is its vocabulary built from lexemes [11], [15]. A lexicon contains
words belonging to a language and in our work individual words from the corpus.
In pure form, the lexicon may contain words that appear frequently in the corpus
but have little value in formalizing a meaningful criterion. These words are called
stop words or in our terminology: negated lexicon, and they are excluded from the
vocabulary. We, first, part-of-speech tagged the corpus with the Penn Treebank
English POS tag set [16]. We use the subset of tagset NN, NNP, NNS, NNPS, JJ,
JJR, JJS, VB, VBD, VBG, VBN, VBP, and VBZ. The word length WL above
some threshold WLT is also considered. The length of a word, with respect to
POS context, is the sequence of characters or symbols that made up the word.
By default, we consider that a word with WL > 2 sufficiently formalizes to some
criterion.
Fig. 1. Overall process: process categorizes into four phases; pre-processing, syntactic
analysis, semantic analysis & representation
The pure form of the lexicon might contain words that need to be further
purified according to some criterion. We use regular expressions for this task.
Then we normalize and case-fold the words [15]. In addition to this there are
families of derivationally related words with similar meanings. We use stemming
and lemmatization to reduce the inflectional forms and derivational forms of a
word to a common base form [15]. We achieve this with the aid of WordNets’
stemming algorithms. We couple the knowledge of POS tag of the word to get
the correct context when finding the common base form.
3.2 Syntactic Analysis
The primary focus on this phase is to look at the structure of the sentences and
learn the associations among the vocabulary. We assume that each sentence of
the corpus follows the POS pattern 1. 1,
(SubjectN ounP hrase +)(V erb+)(ObjectN ounP hrase +) (1)
We hypothesize that the associations learned from this phase provides the po-
tential candidates for concepts and relations of the ontology. But the vocabulary
itself does not provide sufficient ontology concepts. We use a notion of grouping
of consecutive sequence of words to form an OWL concept. This grouping is done
using an appropriate N-gram model [1]. We illustrate this idea using Figure 2.
Fig. 2. An example three-gram model
The group w1 ◦ w2 forms a potential concept in the conceptualization. We
use the notation x ◦ y to show that the word y is appended to the word x. The
groups w2 ◦w3 , w3 ◦w4 etc. form other potential concepts in the conceptualization.
Word w3 comes after group w1 ◦w2 . According to the Bayes viewpoint, we collect
information to estimate the probability P (w3 |{w1 ◦ w2 }), which will be used to
form IS-A relationships, w1 ◦ w2 v w3 using an independent Bayesian network
with conditional probability P ({w1 ◦ w2 }|w3 ). In addition to this, we count the
groups appear in the left hand side and the right hand side of the expression 1
and the association of of these groups given the verbs. These counts are used in
the third phase to create the relations among concepts.
3.3 Semantic Analysis
This phase conducts the semantic analysis with probabilistic reasoning, which
constitutes the most important operation of our work. This phase determines the
conceptualization of the domain using a probability distribution for IS-A rela-
tions and relations among the concepts. Our main definition of concept learning
is given in Definition 1.
Definition 1. The set W = {w1 , . . . , wn } represents words of the vocabulary
and each wi has a prior probability θi > τ . τ is a prior threshold, which is known
as the knowlege factor. The set G = {g1 , . . . , gm } represents N-gram groups
learned from the corpus and each gj has a prior probability ηj . When w ∈ W
and g ∈ G, P (w|g) is the likelihood probability π learned from the corpus. The
entities w and g represent the potential concepts of the conceptualization and
the set W provide the potential super-concepts of the conceptualization. Within
this environment, an IS-A relationship between w and g is given by the posterior
probability P (g|w) and this is represented with a Bayesian network having two
nodes w and g and is modeled by the equation,
π×η
P (g|w) = P . (2)
i p(w|gi ) × p(gi )
Using the Definition 1, the probabilistic conceptualization of a domain is
defined as follows.
Definition 2. The probabilistic conceptualization of the domain is represented
by an n-number of independent Bayesian networks sharing groups.
Fig. 3. w1 , w2 , w3 , w4 and w5 are super-concepts. g1 , g2 , g3 and g4 are candidate sub-
concepts. There are 5 independent Bayesian networks. Bayesian networks 2 and 5 share
the group g2 when representing the concepts of the conceptualization
Figure 3 shows a simple example of the Definition 2. The interpretation of
Definition 2 is: Let a set G contains an n-number of finite random variables
{g1 , . . . , gn }. There exist a group gi , which is shared by m words {w1 , . . . , wm }.
Then, with respect to the Bayesian framework, BNi of P (gi |wi ) is calculated and
max(P (gi |mi )) is selected for the construction of the ontology. This means that if
there exists two Bayesian networks and the Bayesian network one is given by the
pair w1 , g1 and the Bayesian network two is given by the pair {w2 , g1 } then the
Bayesian network that has the most substantial IS-A relationship is obtained
through maxBNi (P (g1 |w1 )) and this network is retained and other Bayesian
networks will be ignored when building the ontology. If all P (g1 |w1 ) remains
equal, then the Bayesian network with the highest super-concept probability
will be retained. These two conditions will resolve any naming issues.
The next step is to induce the relationships to complete the conceptualiza-
tion. In order to do this, we need to find semantics associated with each verb.
We hypothesize that relations are generated by the verbs and the definition is
as follows.
Definition 3. The relationships of the conceptualization are learned from the
syntactic structure model by the expression 1 and the semantic structure model
by the lambda expression λobj.λsub.V erb(sub, obj), where β-reduction is applied
for obj and sub of the expression 1. If there exists a verb V between two groups
of concepts C1 and C2 , the relationship of the triple (V, C1 , C2 ) is written as
V (C1 , C2 ) and model with conditional probability P (C1 , C2 |V ). The Bayesian
network for relationship is and the model semantic relationship is given by,
P (C1 , C2 |V ) = p(C1 |V )p(C2 |V ) → V (C1 , C2 )
Fig. 4. Bayesian networks for relations modeling. C1 and C2 are groups and V is a
verb
The relations learned from Defintions 3 needs to be subjected to a lower
bound. The lower bound is known as the relations factor. When the corpus is
substantially large, the number of relations is proportional to the number of
verbs. Not all relations may relevant and the factor is used as the threashold. A
verb may have antonyms. If a verb is associated with some concepts and these
concepts happen to be associated with a antonym, the verb with the highest
Bayesian probability value is selected for the relations map and the other rela-
tionship will be removed. Finally, the probabilistic conceptualization is serialized
as an OWL DL ontology in the representation phase.
Our implementation of the above phases is based on Java 6 and it is named
as PrOntoLearn (Probabilistic Ontology Learning).
4 Experiments
We have conducted experiments on three main data corpora, 1) the PCAssay, of
the BioAssay Ontology (BAO) project, Department of Molecular and Cellular
Pharmacology University of Miami, School of Medicine 2) a sample collection of
38 PDF files from ISWC 2009 proceedings, and 3) a substantial portion of the
web pages extracted from the University of Miami, Department of Computer
Science7 domain . We have constructed ontologies for all three corpora with
different parameter settings.
The first corpus contains high throughput screening assays performed on
various screening centers. This corpus grows rapidly each month. We specifi-
cally limited our dataset to assays available on the 1st of January 2010. Table
2 provides the statistics of the corpus. We extract the vocabulary generated
7
http://www.cs.miami.edu
from [a-zA-Z]+[- ]? \w* regular expression, and normalized them to create the
vocabulary.
Table 2. The PCAssay (the BioAssay Ontology project) corpus statistics
Title Statistics Description
All documents are XHTML
Documents 1,759 formated with a given template
Normalized candidate concept words from
Unique ConceptW ords 13,017 NN, NNP, NNS, JJ, JJR & JJS
using [a-zA-Z]+[- ]?\w*
Normalized verbs from
Unique V erbs 1,337 VB, VBD, VBG, VBN, VBP & VBZ
using [a-zA-Z]+[- ]?\w*
Total ConceptW ords 631,623
Total V erbs 109,421
T
Total Lexicon 741,044 Lexicon = ConceptW ords V erbs
Total Groups 631,623
The average file size of the corpus is approximately 6 Kb. We conducted
these experiments in a Genuine Intel(R) CPU 585 @ 2.16GHz, 32 bits, 2 Gb
Toshiba laptop. It is found that the time required to build the conceptualiza-
tion grows linearly. We use precision, recall and F1 measures to evaluate the
ontology and recommendations from domain experts, specially to get comments
on the generated bioassay ontology. The ontology that is generated is too large
to show in here.Instead, we provide a few distinct snapshots of the ontology
with the help of Protégé OWLViz plugin. Figures 5 and 6 show snapshots of
the ontology created from the BioAssay Ontology corpus for input parameters
KF = 0.5, N-gram = 3, and RF = 0.9. Figure 5 shows the IS-A relationships
and Figure 6 shows the binary relationships.
According to experts, the ontology contains rich set of vocabulary, which is
very useful for top-down ontology construction. The experts also mentioned that
the ontology has good enough structure. The www.cs.miami.edu corpus is used
to calculate quantitative measurements. The gold standard based approaches
such as precision (P ) and recall (R) and F-measure (F1 ) are used to evaluate
ontologies [8]. We use a slightly modified version of [21] as our reference ontology.
Table 3 shows the results. The average precision of the constructed ontology is
approximately 42%. It is to be noted that we use only one reference ontology. If
we use another reference ontology the precision values varies. This means that
the precision value depends on the available ground truth.
The results show that our method creates an ontology for any given domain
with acceptable results. This is shown in the precision value, if the ground truth
Fig. 5. An example snapshot of the BioAssay Ontology corpus with IS-A relations
Fig. 6. An example snapshot of the BioAssay Ontology corpus with binary relations
Table 3. Precision, recall and F1 measurement for N -gram=4 and RF=1 using ex-
tended reference ontology
KF Precision Recall F1
0.1 0.424 1 0.596
0.2 0.388 1 0.559
0.3 0.445 1 0.616
0.4 0.438 1 0.609
0.5 0.438 1 0.609
0.6 0.424 1 0.595
0.7 0.415 1 0.587
0.8 0.412 1 0.583
0.9 0.405 1 0.576
1.0 0.309 1 0.472
is available. On the other hand, if the domain does not have ground truth the
results are subject to domain expert evaluation of the ontology. One of the po-
tential problems we have seen in our approach is search space. Since our method
is unsupervised, it tends to search the entire space for results, which is computa-
tionally costly. We thus need a better method to prune the search space so that
out method provide better results. According to domain experts, our method
extracts good vocabulary but provides a flat structure. They have proposed a
sort of a semi-supervised approach to correct this problem, by combining the
knowledge from domain experts and results produced by our system. We left the
detailed investigation for future work.
Since our method is based on the Bayesian reasoning (which uses N-gram
probabilities), it is paramount that the corpus contains enough evidence of the re-
dundant information. This condition requires that the corpus to be large enough
so that we can hypothesize that the corpus provides enough evidence to build
the ontology.
We hypothesize that a sentence of the corpus would generally be subjected to
the grammar rule given in expression 1. This constituent is the main factor that
uses to build the relationships among concepts. In NLP, there are many other
finer grained grammar rules that specifically fit for given sentences. If these
grammar rules are used, we believe we can build a better relationship model.
We have left this for future work.
At the moment our system does not distinguish between concepts and the
individuals of the concepts. The learned A-Box primarily consists of the proba-
bilities of each concepts. This is one area where we are eager to work on. Using
the state-of-the art NLP techniques, we plan to fill this gap in a future work.
Since our method has the potential to be used in any corpus, it could be seen
that the lemmatizing and stemming algorithms that are available in WordNet
would not recognize some of the words. Specially in the BioAssay corpus, we
observe that some of the domain specific words are not recognized by WordNet.
We use the Porter stemming algorithm [18] to get the word form and it shows
that this algorithm constructs peculiar word forms. Therefore, we deliberately
remove it from the processing pipeline.
The complexity of our algorithms is as follows. The bootstrapping algorithm
available in the syntactic layer has a worst case running time of O(M ×max(sj )×
max(wk )), where M is the number of documents, sj is a the number of sentences
in a document, and wk is the number of words in a sentence. The probabilistic
reasoning algorithm has the worst case running time of O(|L|×|SuperConcepts|),
where |L| is the size of the lexicon and |SuperConcepts| is the size of the su-
per concepts set. The ontologies generated from the system are consistent with
Pellet8 and FaCT++9 reasoners.
Finally, our method provides a process to create a lexico-semantic ontology
for any domain. For our knowledge, this is a very first research on this line of
8
http://clarkparsia.com/pellet
9
http://owl.man.ac.uk/factplusplus/
work. So we continue our research along this line and to provide better results
for future use.
5 Conclusion
We have introduced a novel process to generate an ontology for any random text
corpus. We have shown that our process constructs a flexible ontology. It is also
shown that in order to achieve high precision, it is paramount that the corpus
should be large enough to extract important evidence. Our research has also
shown that probabilistic reasoning on lexico-semantic structures is a powerful
solution to overcome or at least mitigate the knowledge acquisition bottleneck.
Our method also provides evidence to domain experts to build ontologies us-
ing a top-down approach. Though we have introduced a powerful technique to
construct ontologies, we believe that there is a lot of work that can be done to
improve the performance of our system. One of the areas our method lacks is the
separation between concepts and individuals. We would like to use the generated
ontology as a seed ontology to generate instances for the concepts and extract
the individuals already classified as concepts. Finally, we would like to increase
the lexicon of the system with more tags available from the Penn Treebank tag
set. We believe that if we introduce more tags into the system, our system can
be trained to construct human readable (friendly) concepts and relations names.
Acknowledgements
This work was partially funded by the NIH grant RC2 HG005668.
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