=Paper=
{{Paper
|id=Vol-2181/paper-10
|storemode=property
|title=A Personal Agent-based Approach for API Evolution
|pdfUrl=https://ceur-ws.org/Vol-2181/paper-10.pdf
|volume=Vol-2181
|authors=Cristian Vasquez
|dblpUrl=https://dblp.org/rec/conf/semweb/Vasquez18
}}
==A Personal Agent-based Approach for API Evolution==
https://ceur-ws.org/Vol-2181/paper-10.pdf
A personal agent-based approach for API
evolution
Cristian Vasquez
IDLab, Department of Electronics and Information Systems, Ghent University – imec,
cristian.vasquezpaulus@ugent.be
Abstract. Personal agents and APIs can enable personalized applica-
tions that facilitate the daily processes that a person uses to gather,
classify, persist and retrieve information in daily activities. General-
purpose APIs will cover part of the functionality, but others need higher
degrees of customization. This thesis focus on systems where to count
with common models is difficult, and investigate a bottom-up approach
where personal agents expose hypermedia APIs and copy, transform and
combine API features from others, as a mechanism to improve their
means of interaction.
Keywords: agents, hypermedia, emergent semantics, semantic web ser-
vice discovery, personal dataspace
1 Introduction
We refer to a personal dataspace as the grouping of all kind of digital information
that is gathered and stored by an individual over long periods of time, under
that person’s control (but not necessarily exclusively so)[1].
In the last years, several decentralized social networks have been proposed [2],
with applications can access a unified, user-controlled dataspace. In such a
decentralized setup, one would want intelligent agents to assist users in their
activities while having services tailored to their needs.
Furthermore, the agents might interact with other agents through APIs, requiring
(i) syntactical interoperability, i.e., establish the basic symbols to exchange a
representation without losing structure or information, and (ii) semantic inter-
operability, i.e., reducing interpretation gaps to trigger acceptable behaviors
through interactions.
Interoperability is traditionally achieved when peers can refer to a common
reference model or ontology but become increasingly difficult in decentralized
scenarios. The representations agents exchange, depends on how their schemes
are aligned, but in some cases, direct mappings to common models are not
available. In these setups, an agent would prefer to gather, infer or compose
ad-hoc mappings during interaction with their peers, to interpret, transform
or negotiate structured content. Also, in a growing ecosystem of applications
that are separated from storage, we can expect a significant number of models
changing over time. In this context, provenance is desired to help to interpret
the original intended meaning in the long term.
This work focuses on cases where to reach global interoperability is difficult, and
investigate an approach where APIs are decomposed in features to be exchanged
in a network utilizing clone, mutation, divergence and convergence mechanisms
while persisting provenance trails of the API evolution. The utility of such
approach is three-fold: (i) to promote API evolution through agents that collect
API functionality from others, (ii) to generate useful artifacts for service discovery
(iii) and to incrementally build mappings between the underlying models of each
agent, to improve their means of interaction.
2 Relevancy
Fig. 1. Personal agent exposing APIs generated by means of reasoning and resources
in the web
There is a growing interest in APIs that adapt to the data of a user, as they show
significant improvements over fixed designs according to a specific user profile.
One use case is health-care applications, where care plans need to be tailored
to medical patient profiles and coordinated across several care providers. A
centralized approach has drawbacks since is difficult to acquire and continuously
update the user models required to provide sophisticated tailoring [3]. One
approach is to have applications that consume APIs exposed by a personal agent
with access to a personal dataspace and external sources. This was the case of
the GPS4IntegratedCare1 project, which developed a Smart Workflow Engine to
suggest and execute care paths tailored to complex medical patient profiles. One
of the outputs was an agent designed according to the following requirements: (i)
simplicity of a uniform interface (ii) visibility of features by other service agents
(iii) portability of components by moving from program code to data.
The result was a modular design that enables building APIs as a stock of building
blocks or features to add to the agent dataspace. A feature is defined by patterns
that define what RDF triples to use as data, which N3 documents to use as rules
and a query. The approach extensively applies hypermedia notions to Semantic
Web Services and will be further explored in this work.
3 Related work
To create the proposed system I will use existing research from two fields, which I
categorize as Hypermedia-driven APIs and Bottom-up Interoperability Elicitation.
3.1 Hypermedia-driven APIs
Extensive research has been done regarding the discovery, composition, and
orchestration of Semantic Web Services [4] which traditionally makes use of
central registries where developers advertise their services. More recent and
complementary approaches are the ones based in hypermedia. Hydra [5] is a
lightweight vocabulary to create hypermedia-driven Web APIs, whereas RESTdesc
[6] focus on capturing functionality of APIs to be used by agents. Discovery of
linked data interfaces has been proposed in [7] employing hypermedia links and
controls to facilitate source selection.
3.2 Bottom-up interoperability elicitation.
Since this study focuses on cases where to reach global interoperability is difficult,
of particular interest are the approaches that do not aim to reach it. One is the
Frisco report [8] that considers constructivist’s notions as essential to building
information systems, where shared knowledge can be seen here as the sum of
all individual conceptions of the community, whereas personal knowledge is
1
https://www.imec-int.com/en/what-we-offer/research-portfolio/gps4integratedcare
closer to the individual’s inner reflections. Taking this into consideration, we
can say models need to co-evolve along with their communities of use, disclosing
multiple perspectives. A community can engineer vocabularies and ontologies
through distinct, strategies or ontology-engineering methodologies [9] that address
the inherent difficulty of managing a dynamic artifact that reflects our gradual
understanding of reality. How to characterize ontology change was studied through
layered change operators [10].
The contrast of classical data integration techniques such as ontology matching
[11] and bottom-up interoperability elicitation is studied in [12] and summarized
in [13]. Of particular relevance is the bottom-up approach presented by Karl
Aberer et al. [14], that aims to incrementally develop a global agreement in a
network of peers gossiping queries while building semantic neighborhoods, a
notion adopted in this approach.
4 Research question and Hypotheses
The following questions (Q) and related hypothesis (H) investigate how hyper-
media APIs can be applied in the context of personal agents interacting in the
Web, and how can provenance trails be used for service discovery in a decen-
tralized setup. This work refers to provenance trails as a record of how API
features are copied, transformed and exchanged between agents and corresponds
to sequences of operators that transform API features into others and can be
collected by following links.
– (Q1) Can agents expose resources and service descriptions from their under-
lying dataspaces as Linked Data to be discoverable by others?
– (Q2) How can trails be represented, so they are suitable to characterize API
feature change?
– (Q3) To what extent can information derived from provenance trails influence
the relevance and precision of API feature search and discovery?
– (H1) An agent can discover and clone services from other agents by relying
solely on Linked Data principles.
– (H2) It is possible to gather provenance trails and discover Semantic Web
Services of high relevance without the need of a central service registry.
5 Preliminary results
The model is used in the GPS4IC agent, which is part of the GPS4IntegratedCare2
project to provide RESTful APIs to be used by integrated healthcare applications.
The agent is a nodeJS application that interprets JSON-LD3 files that contain
2
https://www.imec-int.com/en/what-we-offer/research-portfolio/gps4integratedcare
3
https://json-ld.org/spec/latest/json-ld
sets of API features that use patient data, Domain-specific knowledge rules,
and other external data sources to build at APIs at runtime for the health-care
domain.
The GPS4IC agent organizes data in private and public dataspaces, namely (1)
patient dataspaces, (2) service definition dataspaces and (3) knowledge dataspaces,
which are logical constructs that group resources in a directory structure for
simple storage, navigation, and access. Within each directory node, one can define
inference operations O with links to input data and queries. Data can be N3 files,
responses from a SPARQL endpoint, invocations to other operations and so on.
An API is generated at runtime and exposed at a path P using a set of O that
resides in a path equivalent to P of the directory.
Fig. 2. The current agent uses an interpreter of file-based service descriptions to generate
a feature dependency graph that is used by a hypermedia controller to expose APIs
matching a request
All the core functionality such as generating dynamic workflows for the patient,
detecting medication conflicts and domain-specific knowledge like the definition
of diseases, etc. do not reside in the Agent.
Functionality exists as resources that can be linked, cloned and shared using
Change Operators such as: (i) add/remove inference feature (ii) import feature (iii)
add/remove a resource. Operations often need definitions of resources sets which
in this case, are matches of URI template patterns4 applied to the corresponding
directory structure. Agents do not maintain explicit internal schemas but commit
to (believe in) a set of health-care knowledge resources.
6 Approach
This approach is framed in Web environments, specifically in the domain of Web
APIs elicitation and intelligent agent support.
6.1 Feature-based
From an API perspective, this work refers to agents that can play client and
server roles indistinguishably, consuming and also exposing APIs to others.
Each API consists of an interface implemented by components called features,
a logical construct that can afford interaction or functionality to be re-used
[15]. A feature can be exposed through Hypermedia to be discovered, copied,
modified and so on. It can be self-contained, aggregated or linked to outputs
of other features, for instance, a my contacts list feature can be used by a
today birthdays list through filtering it, or a dynamic workflow can depend
on features that expose execution steps. Having API interfaces separated from
features give flexibility to agents, enabling them to choose the features that best
match their models [7] or to perform modifications at implementation level to
minimize the change of their API Interfaces.
This approach requires features to be transferred between agents, although this
can be done in several ways, I already had results with an agent design where
service descriptors are interpreted to expose APIs by using only N3 rules, RDF
data, and queries. In this way, the API functionality can be directly exchanged
through the Web using simple dereferenciation or linking. API responses also ex-
pose links to functional descriptions [6] of the underlying features, with metadata
about dependencies such as knowledge bases, external API or other features.
6.2 Provenance trails
Each time a feature changes within an agent or is cloned from another agent,
new data is registered into what I call provenance trails, that refer to the
history of feature change and exchange between agents.
To characterize and keep track of feature change, I adopt an approach used in
ontology engineering based on layered change operators [10]. In this way, a feature
is a result of applying an operator to the previous one, enabling to represent
4
https://tools.ietf.org/html/rfc6570
provenance as chains of operations. Layers are used to support distinct degrees of
granularity, categorizing change operators from atomic, i.e. adding a triple to
high-level, i.e. add resource collection: photo gallery. The use of layered
change operators allows characterizing feature change as a complete sequence
of steps and makes easier to understand API evolution through inspection of
sequences of high-level operators or their equivalent atomic ones. Other uses are
to extract new high-level operators from atomic ones through pattern mining
[16] or enabling feature discovery through computing similarity between their
respective operator sequences [17]. I expect these trails to be valuable data
to support bottom-up interoperability through data mining. For instance, if a
transitive closure of same functionality links forms a cycle within a network
of features, we can suspect that they can be marked for reconciliation through a
single interface.
6.3 Mappings between personal dataspaces
In the network, agents might have joined semantic communities and peers pro-
viding similar contents (semantic neighborhoods) [18]. If two agents want to
interact using features with models located in two disjoint neighborhoods, they
might exchange their features and models and provide mappings between them.
This results in directed graphs of mappings that allow querying between neigh-
borhoods, translating queries in terms of its originator, possibly providing access
to the data of a target dataspace. In this sense, trails can valuable to form and
find semantic neighborhoods in the network by means of following links.
6.4 Metrics of feature evolution
Data about how API features change can be useful to learn about the dynamics
of agents and their underlying models. For example, operator sequences allow
generating quantitative indicators of feature articulation efforts, the speed of
change or reusability. The data can then be analyzed by tools and techniques
for mining software repositories, such as dependency network analysis [19]. On
the other hand, feature exchange graphs can be used to analyze the dynamics of
the agent network, identifying the most transferred features or dominant API
interfaces or observe how semantic neighborhoods converge or diverge when
the semantics of underlying models change. Metrics will be used in a trade-off
analysis to distinguish under what conditions this approach is effective.
7 Evaluation plan
The hypothesis will be tested through two research aspects:
1. To evaluate the effects of provenance trails via simulations of agents that
exchange features through hypermedia in a decentralized environment. To be
measured through API evolution metrics and benchmarked against current
semantic web service discovery approaches.
2. The impact that keeping such trails has for users while customizing their
agents assisted by service discovery mechanisms without a central service
registry.
H1 will be validated if instruments of (1) demonstrate automatic feature discovery
and exchange using hypermedia APIs and collecting trails. H2 will be validated
if (2) demonstrate fewer API articulation efforts by part of the users.
Several tasks still need to be done to perform the studies and evaluate results,
answering the research questions.
– To develop vocabularies or ontologies to represent (i) feature exposition,
to expose and transfer functional descriptions and their dependencies, (iii)
feature change operators, with representative instances organized in layers,
(ii) and feature exchange. (Q1),(Q2).
– An extensive trade-off analysis of the approach (Q3).
8 Conclusion
In this work, we present an approach where a personal agent collects functionality
from other agents, improving their means of interaction while keeping model
autonomy. It also introduces provenance trails to support automated API
evolution when possible, and if not, diminish the costs that API composition
has for humans. I still have considerable work regarding the implementation
of ontologies and experiments, which I believe will contribute to state of the
art. Being this approach specific, I understand it as complementary to existing
approaches such as automatic schema mapping algorithms [11], mapping gossiping
[14] between peers and the current Semantic Web Service techniques [4].
There is space for innovation regarding personal highly customizable personal
agents, assembling functionality around personal data or even, in more abstract
terms, to any relevant life event of a user. I believe that feature reuse is a good
compromise between a one-size fits all solution and complex API programming. In
the same way, personal agents can help them reach a certain degree of alignment
and coherence between personal dataspaces, something essential if users want to
interact together and build something valuable with the data they own.
9 Acknowledgments
I would like to thank Miel Vander Sande for his useful advice.
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