- This work presents a method founded in instancebased learning for inductive (memory-based) reasoning on ABoxes. The method, which exploits a semantic dissimilarity measure between concepts and instances, can be employed both to answer class membership queries and to predict new assertions that may be not logically entailed by the knowledge base. In a preliminary experimentation, we show that the method is sound and it is actually able to induce new assertions that might be acquired in the knowledge base.
Most of the research on ontology reasoning has been
focusing on methods based on deductive reasoning. However,
important tasks that are likely to be provided by new
generation knowledge-based systems, such as construction, revision,
population and evolution are likely to be supported also by
inductive methods. This has brought to an increasing interest
in machine learning and knowledge discovery methods applied
to ontological representations (see [
We propose an algorithm which is based on a notion of
concept similarity for performing a form of lazy learning on
typical ontological representations. Namely, by combining an
instance-based (analogical) approach with a notion of semantic
dissimilarity, this paper intends to demonstrate the
applicability of inductive reasoning in this field which can be considered
another form of approximate reasoning (see discussion in
[
The standard ontology languages are founded in Description
Logics (henceforth DLs) as they borrow the language
constructors for expressing complex concept definitions.
Instancebased methods depend on a similarity measure defined on
the instance space. In this perspective, a variety of measures
for concept representations have been proposed (see [
It has been observed that, adopting richer representations,
the structural properties have less and less impact in assessing
semantic similarity. Hence, the vision of similarity based only
on a structural (graph-based) approach, such as in [
This analogical reasoning procedure like this may be employed to answering class membership queries through analogical rather than deductive reasoning which may be more robust with respect to noise and is likely to suggest new knowledge (which was not logically derivable). Specifically we developed the method also for an application of semantic web service discovery where services are annotated in DLs.
Another application might regard supporting various tasks for the knowledge engineer, such as the acquisition of candidate assertions for enriching ontologies with partially populated ABoxes: the outcomes given by the procedure can be utilized as recommendations. Indeed, as we show in the experimentation, the newly induced assertions are quite accurate (commission errors, i.e. predicting a concept erroneously, were rarely observed). In turn, the outcomes of the procedure may also trigger other related tasks such as induction (revision) of (faulty) knowledge bases.
The paper is organized as follows. In the next section, the representation language is briefly presented. Two concept dissimilarity measures are recalled and exploited in a modified version of the k-NN classification procedure. The results of a preliminary experimental evaluation of the method using these two measures are shown and, finally, possible developments of the method are examined.
II. ALC KNOWLEDGE BASES
We recall the basics of ALC [
In DLs, descriptions are inductively defined starting with a set NC of primitive concept names and a set NR of primitive roles. Complex descriptions are built using primitive concepts and roles and the constructors showed in the following. The semantics of the descriptions is defined by an interpretation I = (ΔI , ·I ), where ΔI is a non-empty set, the domain of the interpretation, and ·I is the interpretation function that maps each A ∈ NC to a set AI ⊆ ΔI and each R ∈ NR to RI ⊆ ΔI × ΔI .
The top concept > is interpreted as the whole domain of objects ΔI , while the bottom concept ⊥ corresponds to ∅. Complex descriptions can be built in ALC using the following constructors. The language supports full negation: given any description C, denoted ¬C, it amounts to ΔI \ CI . Concept conjunction, denoted C1 u C2, yields the extension C1I ∩ C2I ; dually, concept disjunction, denoted C1 t C2, yields the union C1I ∪ C2I . Finally, the existential restriction, denoted ∃R.C, is interpreted as {x ∈ ΔI | ∃y ∈ ΔI ((x, y) ∈ RI ∧ y ∈ CI )} and the value restriction ∀R.C has as its extension the set {x ∈ ΔI | ∀y ∈ ΔI ((x, y) ∈ RI → y ∈ CI )}.
The main inference is subsumption between concepts based on their semantics: given two descriptions C and D, C subsumes D, denoted by C w D, iff for every interpretation I it holds that CI ⊇ DI . When C w D and D w C then they are equivalent, denoted with C ≡ D.
A knowledge base K = hT , Ai contains a TBox T and an ABox A: • T is the set of definitions C ≡ D, meaning that, for every interpretation I, CI = DI , where C is the concept name and D is its description constructed as above; • A contains concept and role assertions about the worldstate, e.g. C(a) and R(a, b), meaning that, for every I, aI ∈ CI and (aI , bI ) ∈ RI .
A related inference used in the following is instance
checking, that is deciding whether an individual is an instance of a
concept [
Semantically equivalent (yet syntactically different)
descriptions can be given for the same concept. Nevertheless,
equivalent concepts can be reduced to a normal form by means of
rewriting rules that preserve their equivalence [
A description D is in ALC normal form iff D ≡ ⊥ (then D := ⊥) or if D ≡ > (then D := >) or if D = D1 t · · · t Dn (∀i = 1, . . . , n, Di 6≡ ⊥) with l
A u l A∈prim(Di) • exR(Di) is the set of concepts in the existential restrictions of the role R.
For any R, every sub-description in exR(Di) and valR(Di) is in normal form.
In the following, let L = ALC/≡ be the quotient set of ALC descriptions in normal form.
III. DISSIMILARITY MEASURES IN DESCRIPTION LOGICS
We recall two definition of dissimilarity measures for ALC
descriptions expressed in normal form [
The first measure, is derived from a measure of the overlap between concepts, that can be defined as follows:
Definition 1 (overlap function): Let I be the canonical interpretation of the ABox A. The overlap function f is defined1: fC :=LF×n L 7→ R+ defined for descriptions C, D ∈ L, with i=1 Ci and D = Fm
j=1 Dj disjunctive level: f (C, D) := ft(C, D) = 0∞ maxi,j fu(Ci, Dj )
C ≡ D C u D ≡ ⊥ otherwise
X ft(valR(Ci), valR(Dj ))
R∈NR existential restrictions:
N f∃(Ci, Dj ) := RX∈NR kX=1 p=m1,a..x.,M ft(Cik, Djp) where Cik ∈ exR(Ci) and Djp ∈ exR(Dj ) and we suppose w.l.o.g. that N = |exR(Ci)| ≥ |exR(Dj )| = M , otherwise the indices N and M as well as Ci and Dj are to be exchanged in the formula above.
1The name A of the ABox is omitted for simplicity.
The function f represents a measure of the overlap between two descriptions expressed in ALC normal form. It measures the similarity of the input concepts based on the similarity between their extensions (approximated with the retrieval) and also on the similarity of the concepts reached by the role restrictions. Namely, It is defined recursively beginning from the top level of the descriptions (a disjunctive level) up to the bottom level represented by (conjunctions of) primitive concepts.
Overlap at the disjunctive level is treated as the maximum
overlap between the disjunctive forms of the input concepts. At
conjunctive levels, instead of simply considering the similarity
like in Tversky’s measure [
As for the role restrictions overlap, for the universal restrictions we simply add the overlap measures varying the role, while for the existential restrictions the measure is trickier: borrowing the idea of the existential mappings we consider, per role, all possible matches between the concepts in the scope of existential restrictions, then we consider the maximal sum of overlaps resulting from all the matches.
Now, it is possible to derive a dissimilarity measure based on f as follows:
Definition 2 (overlap dissimilarity measure): The overlap
dissimilarity measure is a function d : L × L 7→ [
1 if f (C, D) = 0 d(C, D) := 0 if f (C, D) = ∞
f (C1,D) otherwise
Function d simply measures the level of dissimilarity between two concepts as the inverse of the overlap function f . Particularly, if f (C, D) = 0, i.e. there is no overlap between the considered concepts, then d must indicate that the two concepts are totally different, indeed d(C, D) = 1, the maximum value of its range. If f (C, D) = ∞ this means that the two concepts are totally overlapped and consequently d(C, D) = 0 that means that the two concept are indistinguishable, indeed d assumes the minimum value of its range. If the considered concepts have a partial overlap then their 2We tried also other solutions, such as considering minima or products of the three overlap measures, yet experimentally this did not yield a better performance whereas the computation time was increased. For practical reasons, the maximal measure ∞ has been replaced with large numbers depending on the cardinality of the set of individuals in the ABox: |Ind(A)|. dissimilarity is inversely proportional to their overlap, since in this case f (C, D) > 1 and consequently 0 < d(C, D) < 1.
As discussed in [
In order to approximate this probability for a certain concept
C, we recur to its extension w.r.t. the considered ABox in a
fixed interpretation. Namely, we chose the canonical
interpretation IA, which is the one adopting the set of individuals
mentioned in the ABox as its domain and the identity as
its interpretation function [
pr(C) = |CIA |/|ΔIA | Finally, we can compute the information content of a concept, employing this probability:
I C(C) = − log pr(C)
A measure of the concept dissimilarity is now formally
defined [
canonical interpretation I. The information content dissimilarity measure is a function g : L × L 7→ R+ defined recursively for any two normal form concept descriptions C, D ∈ L, with C = Fn j=1 Dj
i=1 Ci and D = Fm disjunctive level: g(C, D) := gt(C, D) = 0 ∞ min gu(Ci, Dj ) 11 ≤≤ ij ≤≤ nm
if C ≡ D if C u D = ⊥ otherwise gu(Ci, Dj ) := gP (prim(Ci), prim(Dj ))+
+λ(g∀(Ci, Dj ) + g∃(Ci, Dj )) conjunctive level: where Cik ∈ exR(Ci) and Djp ∈ exR(Dj ) and we suppose w.l.o.g. that N = |exR(Ci)| ≥ |exR(Dj )| = M , otherwise the indices N and M are to be exchanged in the formula above.
The function g represents a measure of the dissimilarity between two descriptions expressed in ALC normal form. It is defined recursively beginning from the top level of the descriptions (a disjunctive level) up to the bottom level represented by (conjunctions of) primitive concepts.
Now g has values in [0, ∞]. It may be useful to derive a normalized dissimilarity measure as shown in the following.
ABox with canonical interpretation I. The normalized
information content dissimilarity measure is the function d :
L × L 7→ [
1 1 − g(C,D) if g(C, D) = 0 if g(C, D) = ∞ otherwise
The notion of Most Specific Concept is commonly exploited for lifting individuals to the concept level.
and an individual a, the most specific concept of a w.r.t. A is the concept C, denoted MSCA(a), such that A |= C(a) and for any other concept D such that A |= D(a), it holds that C v D.
In case of cyclic ABoxes expressed in a DL with
existential restrictions the MSC may not be expressed by a finite
description [
On performing experiments related to another similarity
measure exclusively based on concept extensions [
Let us recall that, given the ABox, it is possible to calculate the most specific concept of an individual a w.r.t. the ABox, MSC(a) or at least its approximation MSCk(a) up to a certain description depth k. In some cases these are equivalent concepts but in general we have that MSCk(a) w MSC(a).
Given two individuals a and b in the ABox, we consider MSCk(a) and MSCk(b) (supposed in normal form). Now, in order to assess the dissimilarity between the individuals, d measure can be applied to these descriptions:
d(a, b) := d(MSCk(a), MSCk(b))
This may turn out to be handy in several tasks, namely both in inductive reasoning (construction, repairing of knowledge bases) and in information retrieval.
We proved in [
As previously mentioned, we have also attempted slightly different measure definitions (e.g. considering minima or products at the conjunctive level that sound more intuitive) which experimentally did not prove more effective than the simple (additive) definition given above.
The computational complexity of the measures depends
on the complexity of the required reasoning services for
computing the concepts retrieval. Namely both subsumption
and instance-checking are P-space for the ALC logic[
Obviously when computing the dissimilarity measures for cases involving individuals, the extra cost of computing MSCs (or their approximations) has to be added.
Nevertheless, in practical applications, these computations may be efficiently carried out exploiting the statistics that are maintained by the DBMSs query optimizers. Besides, the counts that are necessary for computing the concept extensions could be estimated by means of the probability distribution over the domain.
IV. A NEAREST NEIGHBOR CLASSIFICATION PROCEDURE
IN DESCRIPTION LOGICS
We briefly review the basics of the k-Nearest Neighbor method (k-NN ) and propose how to exploit the classification procedure for inductive reasoning. In this lazy approach to learning, a notion of distance (or dissimilarity) measure for the instance space is employed to classify a new instance.
Let xq be the instance that requires a classification. Using a dissimilarity measure, the set of k nearest pre-classified instances is selected. The objective is to learn a discrete-valued target function h : IS 7→ V from a space of instances IS to a set of values V = {v1, . . . , vs}. In its simplest setting, the k-NN algorithm approximates h for new instances xq on the ground of the value that h assumes in the neighborhood of xq, i.e. the k closest instances to the new instance in terms of a dissimilarity measure. Precisely, it is assigned according to the value which is voted by the majority of instances in the neighborhood. The the classification function hˆ can be formally defined as follows: k hˆ(xq) := argmax X δ(v, h(xi))
v∈V i=1 where δ is a function that returns 1 in case of matching arguments and 0 otherwise. Note that the hypothesized function hˆ is defined only extensionally, that is the k-NN method does not return an intensional classification model (e.g. a function or a concept definition), it merely gives an answer for new query instances to be classified, employing the procedure above.
This simple formulation does not take into account the similarity among instances, except when selecting the instances to be included in the neighborhood. Therefore a modified setting is generally adopted, weighting the vote on the grounds of the similarity of the instance: k hˆ(xq) := argmax X wiδ(v, h(xi)) v∈V i=1 (1) where, usually, wi = 1/d(xi, xq) or wi = 1/d(xi, xq)2.
Usually this method is employed to classify vectors of features in some n-dimensional instance space (e.g. often IS = Rn). Let us now turn to adapt the method to the more complex context of DLs descriptions. Preliminarily, it should be observed that a strong assumption of this setting is that it can be employed to assign a value (e.g. a class) to a query instance among a set of values which can be regarded as a set of pairwise disjoint concepts/classes. This is an assumption that cannot be always valid. In this case, indeed, an individual could be an instance of more than one concept.
Let us consider a new value set V = {C1, . . . , Cs} of concepts Cj that may be assigned to a query instance xq. If they were to be considered as disjoint (like in the standard machine learning setting), the decision procedure would adopt the hypothesis function defined in Eq. (1), with the query instance assigned the single concept voted by the weighted majority of instances in the neighborhood.
In the general case considered in this paper, when the disjointness of the classes cannot be assumed (unless explicitly stated in the TBox), one can adopt another answering procedure, decomposing the multi-class problem into smaller binary classification problems (one per concept). Therefore, a simple binary value set (V = {−1, +1}) is to be employed. Then, for each single concept (say Cj ), a hypothesis hˆj is computed on the fly during the classification phase: v∈V
k hˆj (xq) := argmax X δ(v, hj (xi))
d(xq, xi)2 i=1 ∀j ∈ {1, . . . , s} (2) where each function hj , simply indicates the occurrence (+1) or absence (−1) of the corresponding assertion in the ABox for the k training instances xi: Cj (xi) ∈ A. Alternately3, hj may return +1 when Cj (xi) is logically entailed by the knowledge base K, and −1 otherwise.
The problem with non-explicitly disjoint concepts is also related to the Closed World Assumption usually made in the context of Information Retrieval and Machine Learning. That is the reason for adapting the standard setting to cope both with the case of non-disjoint concepts and with the OWA which is commonly made in the Semantic Web context. To deal with the OWA, the absence of information on whether a certain instance x belongs to the extension of concept Cj should not 3For the sake of simplicity and efficiency, this case will not be considered in the following. be interpreted negatively; rather, it should count as neutral information. Thus, one can still adopt the decision procedure in Eq. (2), however another value set has to be adopted for the hj ’s, namely V = {−1, 0, +1}, where the three values denote, respectively, positive occurrence, absence and negative occurrence (positive for the concept negation). Formally: hj (x) = +1 0 −1
Cj (x) ∈ A Cj (x) 6∈ A ∧ ¬Cj (x) 6∈ A ¬Cj (x) ∈ A
Again, a more complex procedure may be devised by simply substituting the notion of occurrence (absence) of assertions in (from) the ABox with one based on logic entailment (denoted with `) from the knowledge base, i.e. K ` Cj (x), K 6` Cj (x) nor K 6` ¬Cj (x) and K ` ¬Cj (x), respectively. Although this may help reaching the precision of deductive reasoning, it is also much more computationally expensive, since the simple lookup in the ABox must be replaced with instance checking.
From a computational viewpoint this procedure could be
implemented to provide an answer even more efficiently than
with a standard deductive reasoner. Indeed, once the
retrieval of the primitive concepts is computed, the dissimilarity
measures can be easily computed by means of a dynamic
programming algorithm. Besides, the various measures could
be maintained in an ad hoc data structure which would allow
for an efficient retrieval of the nearest neighbors, such as the
kD-trees or ball trees [
In order to assess the validity of the presented method with the dissimilarity measures proposed in Sect. III, we have applied it to the instance classification problem, with four different ontologies represented in OWL: FSM, SURFACEWATER-M ODEL from the Prote´ge´ library4, the FINANCIAL ontology5 employed as a testbed for the PELLET reasoner and a small FAMILY ontology written in our lab. Although they are represented in languages that are different from ALC, we simply discarded these details when constructing the MSC approximations to be able to apply the presented measures.
FAMILY is an ALCF ontology describing kinship relationships. It is made up of 14 concepts (both primitive and defined), some of them are declared to be disjoint, 5 object properties, 39 distinct individual names. Most of the individuals are asserted to be instances of more than one concept, and are involved in more than one role assertions. This ontology has been written to have a small yet more complex case with respect to the following ones. Indeed, while the other ontologies are more regular, i.e. only some concepts are employed in the assertions (the others are defined only intensionally), in the FAMILY ontology every concept 4See the webpage: http://protege.stanford.edu/plugins/owl/owl-library 5See the webpage: http://www.cs.put.poznan.pl/ alawrynowicz/financial.owl has at least one instance asserted. The same happens for the assertions on roles; particularly, there are some cases where role assertions constitute a chain from an individual to another one, by means of other intermediate assertions.
The FSM ontology describes the domain of finite state machines using the SOF (D) language. It is made up of 20 (both primitive and defined) concepts (some of them are explicitly declared to be disjoint), 10 object properties, 7 datatype properties, 37 distinct individual names. About half of the individuals are asserted as instances of a single concept and are not involved in any role (object property).
SURFACE-W ATER-M ODEL is an ALCOF (D) ontology describing the domain of the surface water and the water quality models. It is based on the Surface-water Models Information Clearinghouse (SMIC) of the USGS. Namely, it is an ontology of numerical models for surface water flow and water quality simulation. The application domain of these models comprises lakes, oceans, estuaries etc.. These models are classified based on their availability, application domain, dimensions, partial differential equation solver, and characteristics types. It is made up of 19 concepts (both primitive and defined) without any specification about disjointness, 9 object properties, 115 distinct individual names; each of them is an instance of a single class and only some of them are involved in object properties.
FINANCIAL is an ALCIF ontology that describes the domain of eBanking. It is made up of 60 (both primitive and defined) concepts (some of them are declared to be disjoint), 17 object properties, and no datatype property. It contains 17941 distinct individual names. From the original ABox, we randomly extracted assertions for 652 individuals.
The classification method was applied to all the individuals in each ontology; namely, the individuals were checked to assess if they were instances of the concepts in the ontology through the analogical method. The performance was evaluated comparing its responses to those returned by a standard reasoner6 as a baseline.
Specifically, for each individual in the ontology the MSC is computed and enlisted in the set of training (or test) examples. Each example is classified applying the adapted kNN method presented in the previous section. As a value of k we chose p|Ind(A)|, as advised in the instance-based learning literature.
The experiment has been repeated twice adopting a leaveone-out cross validation procedure with both the dissimilarity measures defined in Section III.
For each concept in the ontology, we measured the following parameters for the evaluation: • match rate: number of cases of individuals that got exactly the same classification by both classifiers with respect to the overall number of individuals; • omission error rate: amount of unlabeled individuals (our method could not determine whether it was an instance 6We employed PELLET: http://pellet.owldl.com or not) while it was to be classified as an instance of that concept; • commission error rate: amount of individuals (analogically) labeled as instances of a concept, while they (logically) belong to that concept or vice-versa • induction rate: amount of individuals that were found to belong to a concept or its negation, while this information is not logically derivable from the knowledge base We report the average rates obtained over all the concepts in each ontology and also their standard deviation.
By looking at Tab. I reporting the experimental outcomes with the dissimilarity measure based on the overlap (see Def. 2), preliminarily it is important to note that, for every ontology, the commission error was quite low. This means that the classifier did not make critical mistakes i.e. cases when an individual is deemed as an instance of a concept while it really is an instance of another disjoint concept.
In particular, by looking at the outcomes related to the FAMILY ontology, it can be observed that the match rate is the lowest while the highest rate of omission errors was reported. This may be due to two facts: 1) very few individuals were available w.r.t. the number of concepts7; 2) sparse data situation: instances are irregularly spread over the concepts, that is they might be instances of different concepts, which are sometimes disjoint. Hence the MSC approximations that were computed also resulted very different one from another, which reduces the possibility of significantly matching similar MSCs. This is a known drawback of the Nearest-Neighbor methods. Nevertheless, as mentioned above, it is important to note that the algorithm did not make any commission error and it is able to infer new knowledge (11%).
As regards the FSM ontology, we have observed the maximum match rate with respect to the classification given by the logic reasoner. Moreover, differently from the other ontologies, both the omission error rate and induction rate were null. A very limited percentage of incorrect classification cases was observed. These outcomes were probably due to the fact that individuals in this ontology are quite regularly divided by the assertions on concepts and roles, so computing their MSCs, these are all very similar to each other and so the amount 7Instance-based methods make an intensive use of the information about the individuals and improve their performance with the increase of the number of instances considered. it is well known that the NN procedure becomes more and more precise as more instances can be considered. The price to be paid was a longer computation time.
Match Commission Omission Induction
Rate Rate Rate Rate FAMILY .608±.230 .000±.000 .330±.216 .062±.217
FSM .899±.178 .096±.179 .000±.000 .005±.024
S.-W.-M. .820 ±.241 .000±.000 .064±.111 .116±.246 FINANCIAL .807±.091 .024±.076 .000±.001 .169±.046
In this work we have coupled with a method for inductive inference on ABoxes with two composite concept similarity measures. The method is based on the classification in analogy with the majority of neighbor training individuals.
It can be naturally exploited for predicting/suggesting missing of information they convey is very low. A choice of a lower information about individuals thus enabling a sort of inductive number k of neighbors could probably help committing those retrieval. For example, it may be employed a query to the residual errors. KB may be issued. Specifically we are targeting also the task
For the same reasons, also for the SURFACE-W ATER- of service discovery when for semantically annotated web MODEL ontology quite a high rate of matching classifications service. Even more so the outcomes of our method may be was reported (yet less then with the previous ontology); decisive for enabling a series of other inductive tasks such as moreover, some cases of omission error (6%) were observed. clustering, case-based reasoning, , etc.. The induction rate was about 12% which means that for The proposed method is able to induce new assertions, in this ontology our classifier always assigned individuals to addition to the knowledge already derivable by means of a the correct concepts but, in some cases, it could also induce reasoner. Then it seems to be able to enhance the standard new assertions. Since this rate represents assertions that were instance-based classification. Particularly, an increase in accunot logically deducible from the ontology and yet they were racy was observed when the instances increase in number and inferred inductively by the analogical classifier, these figures are homogeneously spread. would be a positive outcome (provided this knowledge were The presented measure can be refined tweaking the weightdeemed as correct by an expert). Particularly, in this case the ing factor λ for decreasing the impact of the dissimilarity increase of the induction rate has been due to the presence of between nested sub-concepts in the descriptions on the deassertions of mutual disjointness for some of the concepts. termination of the overall value.
Results are no different also for the case of the experiments Lately, we have defined another measure for ALN [
The average results obtained by adopting the procedure with tests based on ranked distances), in order to accept answers the measure based on information content (see Def. 4) are as correct with a high degree of confidence. Furthermore, reported in Table II. the k-NN method in its classical form is particularly suitable
By analyzing this table it is possible to note that no sensible for the automated induction of missing values for (scalar or
variation was observed in the classifications performed using numeric) datatype properties of an individual as an estimate
the first dissimilarity measure. Particularly, with both measures derived from the values of the datatypes for the surrounding
the method correctly classified all the individuals, without individuals.
commission errors. The reason is that, in most of the cases, Kernels are another means to express a notion of similarity
the individuals of these ontologies are instances of one concept is some unknown feature space. We are working at the
only and they are involved in a few roles (object properties). definition of kernel functions on DLs representations [
Surprisingly, the results on the larger ontologies (S.-W.-M. and FINANCIAL) perfectly match those obtained with the other ACKNOWLEDGMENTS measure which is probably due to the fact that we used a leave- This work was partially supported by the regional interest one-out cross-validation mode which yielded a high value for projects DIPIS (DIstributed Production as Innovative System) the number k of training instances for the neighborhood and and DDTA (Distretto Digitale Tessile Abbigliamento) in the context of the tasks related to the semantic web services discovery.