=Paper=
{{Paper
|id=None
|storemode=property
|title=Knowledge Discovery meets Linked APIs
|pdfUrl=https://ceur-ws.org/Vol-1056/salad2013-7.pdf
|volume=Vol-1056
|dblpUrl=https://dblp.org/rec/conf/esws/HoxhaMK13
}}
==Knowledge Discovery meets Linked APIs==
https://ceur-ws.org/Vol-1056/salad2013-7.pdf
Knowledge Discovery meets Linked APIs
Julia Hoxha1 , Maria Maleshkova1 , and Peter Korevaar2
1
Institute AIFB, Karlsruhe Institute of Technology (KIT), Germany
{julia.hoxha,maria.maleshkova}@kit.edu
2
Institute KSRI, Karlsruhe Institute of Technology (KIT), Germany
peter.korevaar@partner.kit.edu
Abstract. Knowledge Discovery and Data Mining (KDD) is a very well-
established research eld with useful techniques that explore patterns
and regularities in large relational, structured and unstructured datasets.
Theoretical and practical development in this eld have led to useful and
scalable solutions for the tasks of pattern mining, clustering, graph min-
ing, and predictions. In this paper, we demonstrate that these approaches
represent great potential to solve a series of problems and make further
optimizations in the setting of Web APIs, which have been signi cantly
increasing recently. In particular, approaches integrating Web APIs and
Linked Data, also referred to as Linked APIs, provide novel opportunities
for the application of synergy approaches with KDD methods.
We give insights on several aspects that can be covered through such
synergy approach, then focus, speci cally, on the problem of API usage
mining via statistical relational learning. We propose a Hidden Relational
Model, which explores the usage of Web APIs to enable analysis and
prediction. The bene t of such model lies on its ability to capture the
relational structure of API requests. This approach might help not only
to gain insights about the usage of the APIs, but most importantly to
make active predictions on which APIs to link together for creating useful
mashups, or facilitating API composition.
1 Introduction
Knowledge discovery is an interdisciplinary area that focuses on methodologies
for identifying novel, potentially useful and meaningful patterns from data. Data
mining is an important part of the eld. The rapid growth of data on the Web
and the widespread use of large databases have resulted in an increased demand
for knowledge discovery and data mining (KDD) methods.
At the same time, the Web of Data has grown to one of the largest publicly
available collections of structured data, spurred by the Linked Open Data ini-
tiative1 . A more dynamic way to access such data is through APIs, which can
provide access to a wealth of up-to-date information [12]. Recent approaches [13,
18,20] have investigated the integration of Web APIs, or data-providing services,
with Linked Data, referred here as Linked APIs. The overall goal is to enable
1
http://linkeddata.org/
SALAD 2013 Workshop – Services and Applications over Linked APIs and Data 56
2 Julia Hoxha1 , Maria Maleshkova1 , and Peter Korevaar2
the communication of APIs at a semantic level, so that they can consume and
produce Linked Data. Table 1 illustrates an example of the invocation of the orig-
inal GeoNames API2 , which nds nearest GeoNames feature to a given point and
links a geographic point to nearby resources from DBpedia. The API is invoked
via a HTTP request. If exposed as Linked API, its request would be in the
Linked Data format, mapped to public vocabularies and serialized in RDF. The
same goes for the response retrieved by the API invocation.
API Invocation
Request URL Input RDF
ipedia?lat=49.0080848&lng=8.4037563 a gn:Feature;
geo:lat "49.0080848 ;
geo:long "8.4037563" .
Response XML Linked Output RDF
@prefix dbpedia: .
API Response
gw:findNearbyWikipedia?lat=49.01&lng=8.41#point
foaf:based_near
en dbpedia:Federal_Constitutional_Court_of_German;
Federal Constitutional Court of Germany foaf:based_near dbpedia:Karlsruhe.
...
49.0125
8.4018
...
....
Fig. 1. An example of Web API request and response in dierent formats
As in the general case where the growing information on the Web necessitates
the application of knowledge discovery, we argue that the setting of Linked APIs
also demands such techniques to tackle a series of open problems. In this position
paper, we aim to investigate the potential of a synergy between KDD methods,
on one hand, and research on Web APIs and Linked Data integration, on the
other hand. Based on state-of-the-art approaches and new insights, we discuss
(1) how KDD methods can tackle problems related to Linked APIs, and (2)
how Linked APIs can be used to leverage existing KDD methods. Our goal is
to stimulate the interest of both communities to explore novel approaches for
mutual research.
2 Synergy Approach
We discuss how KDD methods can tackle problems related to Linked APIs, and
how Linked APIs can be used to leverage existing KDD methods. There are
two main questions that we address: (1) how can the KDD methods be leveraged
and pushed forward using contributions from Linked APIs, and (2) how can we
tackle the problems and main research questions of Linked APIs by applying
KDD methods ?
2
http://www.geonames.org/
SALAD 2013 Workshop – Services and Applications over Linked APIs and Data 57
Knowledge Discovery meets Linked APIs 3
In the following sections, we propose a few areas where a synergy is promising
in each of these two directions.
2.1 Linked APIs for KDD
In the past few years, there has been a growing realization in the research com-
munity of data mining and knowledge discovery that semantics and structure
can greatly enhance existing methods by boosting their performance. This is-
sue has been investigated in research areas such as, among others, search and
information retrieval, Web mining, recommender systems and social network
analysis. While this eld is very large and of high variety, we select here only
a few concrete topics we think are promising and elaborate on some ideas how
KDD methods can be enhanced via the use of Linked APIs.
Federated Search. Currently, search on the Web is going beyond the re-
trieval of textual Web sites, taking advantage of the growing amount of struc-
tured data. As an application of the broad eld of information retrieval (IR),
federated search allows users to submit a real-time search in parallel to mul-
tiple, distributed information sources and retrieve aggregated, ranked and de-
duplicated results. Recent focus of IR research has been entity search [2, 6, 17],
where the units of retrieval are structured entities instead of textual documents.
These entities reside in dierent sources and, instead of having a centralized
solution, an investigated approach is to directly search entities over distributed
data sources.
An interesting research ground would be the investigation of federated search,
e.g. federated entity discovery, over distributed Web APIs. If the IR community
shifts the interest on these APIs, they can nd the needed setting where abudant
data is oered in structured format and in distributed sources. From a research
point of view, in such a setting they can address the problems of data com-
pleteness, ranking, or information redudancy via on-the- y entity consolidation
techniques.
Pattern Mining. Several works [1, 9, 10, 19, 25] investigate the eect of se-
mantic information on mining frequent patterns. The common idea behind this
research is to enable semantic (association) pattern mining based on ontology
knowledge representation. The goal is to let machines provide the capability of
understanding the semantics of text data, and learning and reasoning automat-
ically. Generally, they indicate an increase in pattern quality when patterns are
semantically enriched.
While the results that they show are very promising, it is noticable that
these approaches are generally based on small datasets and toy or quite small,
manually-built ontologies. This is an issue raised very recently within the data
mining community [23], which addresses the problem of the lack of interesting
large datasets. Most of their research is based on on old, solid evaluation bench-
marks, but less compelling Big Data. On the other side, the Web of Data has
grown to one of the largest publicly available collections of structured, cross-
domain data sets. In our opinion, one very suitable way to reach these datasets
is through APIs. As such, research community of data mining, in general, and of
SALAD 2013 Workshop – Services and Applications over Linked APIs and Data 58
4 Julia Hoxha1 , Maria Maleshkova1 , and Peter Korevaar2
pattern mining speci cally, can greatly pro t by getting more acquainted with
the setting of Linked Data and Web APIs, whose scale and heterogeneity would
certainly pose research challenges and novel contributions. The same idea ap-
plies also to social network mining, where current analytic approaches can be
leveraged with data requested by social graph APIs, e.g. Facebook's Graph API
to explore linked objects and connections in Facebook's social graph [22]. Cur-
rent interesting theoretical works on social network mining with probabilistic
relational models, which work with small datasets, can be extended with data
acquired over social graph APIs.
Recommender Systems. The eld of recommender systems is well estab-
lished with solid practical developments in various elds during the last years.
Based on preferences of the users and their browsing history on the Web, recom-
mendation approaches are able to predict relevant items and pages to the users.
Still, these systems deal with the limitations of preference sparsity and cold start
problem. Another limitation is the lack of exibility to incorporate contextual
factors in the recommendation methods. To a great extent, these issues can be
related to a limited description and exploitation of the semantics underlying
both user and item representations. As such, a lot of research [35, 11, 15, 16, 21]
is focused on harnessing the power of domain knowledge and semantic data,
utilizing ontological concepts and relations, to provide more eective top-N rec-
ommendations.
As mentioned also earlier, one aspect how Linked APIs could help is through
the semantic enrichment and plentiness of structured data that can be retrieved.
Since these APIs provide a way to automatically produce semantic knowledge
bases and item annotations from public sources, they yield an attractive and
challenging setting for scalability evaluation. Furthemore, we believe an inter-
esting research directions based upon these techniques is the extensions with
recommendations of requests directed to Web APIs. With the growth of APIs,
one can envision a shift from item/page recommendation to Web API request
recommendation.
We have listed above only a few suggestions, with the goal of stimulating the
interest of communities to explore novel approaches for mutual research.
2.2 KDD for Linked APIs
Very recently, researchers have argued that research on knowledge discovery, par-
ticularly machine learning (ML), can oer a large variety of methods applicable
to dierent expressivity levels of Semantic Web knowledge bases [14]. In this
section, we go a step further and elaborate on how KDD methods can be useful
to tackle problems related to Web APIs, especially to the setting of Linked APIs.
Semantic Models of Web APIs While there are several bene ts from
integrating Web APIs with the Linked Data cloud, a key challenge is the di culty
of building the required semantic models to describe and deploy APIs, so that
they directly consume Linked Data and generate RDF linked to the input data.
KDD methods can be helpful to alleviate this process, e.g. statistical models
for pattern recognition and machine learning, or structure learning [8], can be
SALAD 2013 Workshop – Services and Applications over Linked APIs and Data 59
Knowledge Discovery meets Linked APIs 5
deployed in this case. Methods from inductive logic programming and ML have
also been applied in the semantic Web for ontology learning. It is promising to see
there adaptation for the API setting, e.g. to generate the semantic models. One
such approach, more precisely Conditional Random Fields, is recently applied in
the modeling process of Linked APIs [20], in order to construct the inputs/out-
puts worksheet from the invocation URLs, and further generate a formal model
type of data by assigning semantic types to the data types. Another important
data mining task that can be applicable to RDF data of API requests is the
clustering of instances, also called group detection [7], to further help automate
the process of building semantic models of APIs.
Web API usage mining Of special interest of a provider is to understand
how the oered APIs are being used. In that respect, techniques of Web usage
mining can be extended or adapted for Web API usage mining. The idea would
be to apply KDD techniques upon Web APIs usage data. Particular methods of
interest that we can mention here: (i) event detection and pattern discovery, e.g.
to automatically detect anomalies or failures in Web APIs based on the analysis
of logs of requests and responses, (ii) frequent pattern analysis, (iii) statistical
relational learning, based on which we develop a model (Sec. 3) to show how
research in this area can be helpful.
We elaborate in more details on the synergy approach of applying KDD
methods to APIs usage data. The dierence in this approach, when compared
to typical usage mining, is that the requests/responses are structured and an-
notated (or if not fully annotated, they can still be semantically described with
existing approaches). This is a powerful aspect to be used as the basis for the
application of semantically-leveraged KDD techniques. This enables ways of in-
creasing the expressivity of the query to be posed over the usage dataset for
mining purposes. It also enables prediction based on relational ML methods,
which are shown to be more eective than methods where no structural infor-
mation is captured.
By discovering recurring patterns in the requests and responses directed to
one or more Web APIs, one can detect possible problems or bottlenecks. Fur-
thermore, the analytics power is used to make predictions on what will most
likely occurr in the future, as such discovery potentials for optimization of a
speci c API or composition of several APIs.
3 Mining Web API requests with Statistical Relational
Learning
3.1 API Requests Network
An API is normally invoked by sending HTTP requests, which can be broken
down into the elements of which they are composed. Table 1 illustrates an ex-
ample of a database of requests to dierent APIs that a provider may receive.
The elements of the request include the base URL, input variables (name and
value), and output variables. When several requests are issued in sequence by the
SALAD 2013 Workshop – Services and Applications over Linked APIs and Data 60
6 Julia Hoxha1 , Maria Maleshkova1 , and Peter Korevaar2
Table 1. Web API Requests Database
Req. Link API Description
r1 http://maps.googleapis.com/ Google Provides latitude and longitude for
maps/api/geocode/json?address= GeoCoding a given street address.
Karlsruhe&sensor=false API
r2 http://ws.geonames.org/ GeoNames Finds the nearest GeoNames fea-
findNearbyWikipedia?lat=49. ture to a given point and links a
0080848&lng=8.4037563 geographic point to resources from
DBpedia that are nearby.
r3 http://www.worldweatheronline. World Provides weather information on a
com/feed/search.ashx?query= Weather given city name.
Karlsruhe&num_of_results=3 Online
same user (i.e. identi ed by the same IP), they can be interlinked by a nextTo
relationship. We propose a graphical representation for this rich collection of
objects, which we refer to as API requests network. Note that this representation
is related to the RDF graph, which makes it coherent with the beforementioned
representation of Linked APIs. An example of such a network, based on the
sample requests of Table. 1, is illustrated in Fig. 2.
3.2 Hidden Relational Model
Based on the API requests network, various applications of KDD techniques are
interesting including relationship prediction, usage pattern discovery, API rec-
ommendations, etc. In this paper, we focus more on the task of relationship
prediction and propose a model based on statistical relational learning (SRL)
[7]. SRL is a prominent area of machine learning research, which combines for-
malisms of expressive knowledge representation with statistical approaches for
performing probabilistic inference and learning on relational networks. An SLR
approach is more appropriate for the task at hand, since we would like to explore
the rich relational information embedded in the usage data of APIs.
Fig. 2 (right) illustrates a simple SRL model of the API requests network. We
introduce a random variable, denoted as Ri,j , for each potential edge to describe
its state. As such, there is binary variable associated with the edge between
object request req1 of the GeoNames API and object request req1 of the Google
GeoCoding API. The variable is 1 if the request is nextT o another request in
the transaction, and 0 otherwise. The edge between an object (e.g. req1 ) and
object property (e.g. input is address) is also associated with a random variable
denoted by Gi , whose value describes the pro le of the API request. To infer
whether a request is next to another request of an API, we learn the probabilistic
dependencies between the random variables.
To explore non-local dependencies in the model, for each object request a
hidden variable is introduced, denoted in the gure as Zi . The state of the
hidden variable represents unknown attributes of the request that still impact the
relationship of interest. This model is referred to as the hidden relational model
(HRM) [24]. In this case, we assume that the relation nextT o is conditioned
SALAD 2013 Workshop – Services and Applications over Linked APIs and Data 61
Knowledge Discovery meets Linked APIs 7
49.0080848 8.4037563 49.0080848 8.4037563
Karlsruhe false Karlsruhe false
value value
value value
geo:lat geo:lng geo:lat geo:lng
address sensor address sensor G5
i1 i2 G1 G2 G6
i1 i2 foaf: foaf:
nextTo nextTo G7 based_near
Req1 Req2 o1 based_near Req
Z1 1 R1,2 Req
Z2 2
API value API
geonames o2 dbpedia: geonames G3 G4 dbpedia:
o1 Karlsruhe Karlsruhe
API
geo:lat geo:lng geo:lat geo:lng R2,3
Google Google
49.0080848 8.4037563
GeoCoding 49.0080848 8.4037563
GeoCoding
Req3 Req
Z3 3
i1 API
i2 o1 API
num_of num_of worldweather
worldweather temp
G10
query _results temp query
G8 G9
_results online
online
value value value
Karlsruhe 2 10 Karlsruhe 2 10
Fig. 2. Left: graph of Web API requests based upon API usage transactions database.
Right: hidden relational model (HRM) of the graph. Each edge is associated with a
random variable that determines the state of the edge. The directed arcs indicate direct
probabilistic dependencies.
on the attributes of the API request (i.e. inputs and outputs). In Fig. 2, for
simplicity, we introduce variables for the input names only. Information in the
model can propagate via interconnected hidden variables.
To predict whether request Req1 is next to another request Req2 to the
GeoCoding API, we need to predict the relationship R1,2 . The probability is com-
puted on the evidence about (1)the attributes of the requests, i.e. {G1 ,...,G7 }, (2)
the known relationships associated with the objects of interests, i.e. the relations
R2,3 of request Req2 , and (3) information transferred biy hidden variables, i.g.
information on G8 , G9 , G10 propagated via Z3 . Through the hidden variables, in-
formation is globally distributed in the ground network de ned by the relational
structure, which consists here of attribute variables exchanging information via
a network of hidden variables.
The model provides also a cluster analysis of the API requests network. The
hidden variables are drawn from a discrete probability distributions, thus they
can be interpreted as cluster variables where similar API requests are grouped
together. The cluster assignments (or hidden states) of the objects are decided
not only by their attributes, but also by their relations.
We complete the model by introducing the parameters in Fig. 3 as in [24].
The state of Zi speci es the cluster of a request reqi . With K denoting the num-
ber of clusters, Z follows a multinomial distribution with parameter vector π ,
specifying the probability of a request belonging to a cluster, i.e. P (Zi = k) = πk .
It is drawn from a conjugated Dirichlet prior with hyperparameters α0 . The at-
tributes of the requests are assumed to be discrete and multinomial variables,
drawn from a multinomial distribution with parameters θk , also referred to as
SALAD 2013 Workshop – Services and Applications over Linked APIs and Data 62
8 Julia Hoxha1 , Maria Maleshkova1 , and Peter Korevaar2
API Request
θ G0
Z Attributes
Request
π α
followedBy R φ G0R
Fig. 3. Hidden Relation Model (HRM) of Web API requests
mixture component associated with the cluster k . These mixture components
are independently drawn from a prior G0 that, following [24], can be a con-
jugated Dirichlet prior with hyperparameters β . As the crucial components of
this model, the relationships (nextT o) are also assocuated to variables and pa-
rameters, where relationship R is discrete with two states. Each Ri,j is drawn
from a binomial distribution with parameter φk,l , where k and l denote cluster
assignments of the request reqi and the request reqj , respectively. Each φk,l is
independently drawn from the prior Gr0 , which can be de ned as a a conjugated
Beta distribution with hyperparameters β r .
Inference. Given certain evidence of the ground network, the goal would
then be to compute the probabilities of the relationships R for unobserved vari-
ables in the data. This is the inferential problem of computing the posterial
probabilities, for which approximate inference methods, such as Markov chain
Monte Carlo (MCMC) sampling, can be applied.
3.3 Practical Applications of HRM
As mentioned earlier, this relational model can be used for cluster analysis as
well as relationship prediction. As such, based on past usage logs, we can explore
which API makes more sense to request next given a speci c coming request.
Through the clustering analysis, we are able to group APIs together, based on
how they have been frequently requested by agents.
In both cases, this not only helps to gain insights about the usage of the
APIs, but most importantly can generate active knowledge on which APIs to
link together to create useful mashups. Furthermore, we foresee the application
of such predictive models to facilitate the automation of Web API compositions.
In this case, the composition process will be founded on APIs matching driven
by the respective usage behavior.
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