=Paper=
{{Paper
|id=Vol-2180/ISWC_2018_Outrageous_Ideas_paper_2
|storemode=property
|title=Decentralizing the Semantic Web through Incentivized Collaboration
|pdfUrl=https://ceur-ws.org/Vol-2180/ISWC_2018_Outrageous_Ideas_paper_2.pdf
|volume=Vol-2180
|authors=Ruben Verborgh
|dblpUrl=https://dblp.org/rec/conf/semweb/Verborgh18
}}
==Decentralizing the Semantic Web through Incentivized Collaboration==
https://ceur-ws.org/Vol-2180/ISWC_2018_Outrageous_Ideas_paper_2.pdf
Decentralizing the Semantic Web
through Incentivized Collaboration
Ruben Verborgh
IDLab, Dep. of Electronics and Information Systems, Ghent University – imec
ruben.verborgh@ugent.be
Abstract. Personal data is being centralized at an unprecedented scale, and this
comes with widely known and far-reaching consequences, considering the recent
data scandals with companies such as Equifax and Facebook. Decentralizing
personal data storage allows people to take back control of their data, and Semantic
Web technologies can facilitate data integration at runtime. However, such data
processing over decentralized data requires far more expensive algorithms, while at
the same time, less processing power is available in individual stores compared to
large-scale data centers. This article presents a vision in which nodes in decentral-
ized networks are incentivized to collaborate on data processing using a distributed
ledger. By leveraging the collective processing capacity of all nodes, we can
provide a sustainable alternative to the current generation of centralized solutions,
and thereby put people back in control without compromising on functionality.
1 Decentralizing personal data storage to regain control
The past couple of years, we have witnessed an unprecedented centralization of personal
data on the Web. Large-scale social media networks collect our information, with or
without our conscious approval, and store and process it centrally in powerful data
warehouses. People are requested to hand over the control of their personal data in
order to receive the services they want. For instance, on many social platforms, creating
a photo album for sharing with family members involves uploading your photos to those
platforms. Serious data scandals with companies such as Equifax and Facebook point
to the inherent dangers of bringing such large amounts of data together in one place.
Unsurprisingly, taking back control of our own data and obtaining trusted information are
two of three major challenges formulated by Web inventor Tim Berners-Lee in 2017 [2].
Putting people back in control of their data means offering them the choice of storing
that data wherever they want, independently of the applications they want to use. This is
a core idea behind initiatives such as Solid [5]: data is decentralized in the sense that
everyone can store their data in their own space, and applications are decoupled from data
because resources created with one application can be read and modified by another.
An example can be seen in Fig. 1, where a social feed can display pictures and events
created by other applications. Moreover, the social feed is constructed by querying data
from multiple storage locations, without prior centralization. This way, people are free to
choose their storage provider and their application provider independently, and can move
their data away at will. They can give applications, other people, or companies access to
specific parts of their data as they see fit, and revoke or restrict that permission at any
given point in time. This results in true data ownership and full control.
2 Ruben Verborgh
photo photo
gallery editor Alice’s
storage
pictures
social Ben’s
my personal feed storage
data storage
agenda
meeting
scheduler document Carol’s
editor storage
contacts
applications
Fig. 1. Rather than demanding ownership, applications query data from decentralized locations.
Such a wide cross-application interoperability without strong prior agreements can
be achieved by encoding semantics along with data and queries, as is possible with
Semantic Web technologies like rdf and sparql. Data can be represented through
a choice of widely used and custom ontologies. Every person is free to pick their
ontologies and, because of semantics, reasoning can bridge ontological differences.
In other words, the decentralized aspects of Linked Data and the uncoordinated nature
of rdfs and owl ontologies are a good fit for such scenarios [5].
2 Performance problems of decentralization
Compared to centralized systems, decentralized systems are facing a double disadvantage:
individual nodes are not only solving a harder problem, they are doing so with far
fewer resources. On the one hand, algorithms for decentralized data processing require
significantly more processing power and network bandwidth than their centralized
counterparts, because of heterogeneity and distribution. On the other hand, each individual
node in the network—be it a data store or a client running an application—possesses far
less computational power and bandwidth than large centralized data centers.
Furthermore, many of our data processing algorithms are not prepared for the scale
of decentralization entailed by full data ownership. As a simple but realistic example,
building the social media feed of a person with 500 friends requires executing a query
over 500 different data sources in the worst case, where each of those friends store their
data at a different location. State-of-the-art federated sparql query engines consider
use cases of a dozen of large datasets with entirely different data shapes. In contrast,
decentralized data storage will require federated queries over hundreds of small datasets
with highly similar shapes. Current summarization and source selection strategies, crucial
to federated performance, are not designed to function under such conditions.
Finally, exposing personal data storage through query endpoints comes with challenges
of its own. Federated sparql query engines are usually benchmarked in private networks.
On the public Web, sparql endpoints have long suffered from availability problems [3],
and regardless of whether the causes are technological or managerial, there is a non-
negligible risk that such problems would manifest themselves with at least a part of
personal data stores. While less expressive query interfaces have shown promise on
public networks [7], as data becomes spread across an increasing number of nodes, we
can expect to run into severe bandwidth usage and associated query slowdowns.
Decentralizing the Semantic Web through Incentivized Collaboration 3
3 Leveraging strength in numbers through collaboration
Decentralized networks have a particular asset: even though individual nodes have limited
resources compared to large-scale server clusters, collectively, these nodes possess a far
larger amount of computational power and bandwidth. Every single personal data store,
as well as every client (computers, smartphones, tablets, . . . ), brings their own cpus—
which, in a centralized environment, are typically underused. If we find a way for these
nodes to collaborate, we solve the resources problem in decentralized networks. If we
take optimization measures, such as performing preparatory work on the nodes closest to
the data, we can counter the increased complexity of decentralized algorithms.
Let us apply this insight to the data gathering phase of applications, which in
a decentralized network amounts to federated query evaluation. A straightforward query
to collect the recent activity of one’s contacts would involve the application sending
subqueries to each of those contacts’ data stores. However, social media networks typically
contain overlapping clusters of people, so any person on a contact list is likely to have
a subset of that list as contacts too. Therefore, we can set up agreements along the lines
of “I will help you execute your query if you help me execute mine”. Then instead of
sending subqueries to, for instance, 500 contact nodes, we can delegate larger subqueries
to 10 or 20 hubs in parallel. Instead of executing data gathering entirely at the server or
the client [7], we thus dynamically redistribute query execution across the network.
4 Providing incentivization and trust through distributed ledgers
In order to reach sustainable collaborations, nodes need to be incentivized to act as
a contributor to the network. Otherwise, a node cannot be sure that, if it helps other nodes
while idle, the others will return the favor when needed. However, when incentives are
created, nodes also gain a reason for dishonest behavior, so we will need a trust mechanism
to verify whether the work was completed correctly. For lack of a centralized entity
in the network, such incentives and trust need to be established through decentralized
consensus. This is possible through distributed ledgers [6], which can keep track of the
work performed and hence the right to receive help from others.
One category of distributed ledgers are blockchains [6], which require a proof in
order to add something to a ledger. Whereas the popular Bitcoin ledger is known for an
essentially meaningless computation as proof-of-work, newer types of ledgers such as
Filecoin [1] introduce more meaningful purposes for this proof. With Filecoin, people can
pay others to securely store and retrieve their data, and a proof-of-replication confirms that
the data is there at all times. We would similarly need to develop a proof-of-query-results
that captures both the work performed as well was the correctness of the results.
Figure 2 shows the architectural components of an individual node in the network.
When a query arrives, the node determines what incentive it is willing to accept, and what
incentives it is wiling to pay others for subquery delegation. After possibly delegating
some parts, and performing the remaining work itself, it maintains provenance of the
data and generates a correctness proof of the results. Transactions are registered on
the blockchain, such that all participants receive their reward. Some nodes might start
performing preparatory work, such as precomputing partial results of common queries in
the network, or locally caching other stores’ data to speed up querying.
4 Ruben Verborgh
collaborative ecosystem
Query
discovery of clients and stores
selection
Processor
planning
Provenance Storage
& Proof private
Incentives
Verification
blockchain … tasks tasks tasks …
Fig. 2. Each node in the network has a query processor that can evaluate queries itself or (partially)
delegate to others. Incentive modeling captures the required reward, and provenance and proof
provide correctness guarantees. Performed tasks and their incentives are recorded on a blockchain.
5 Projected impact
This idea goes beyond data marketplaces [4] by in essence proposing a service marketplace
between nodes in a decentralized semantic data network. While the example applies this
to query execution over personal data, other kinds of services can be auctioned as well,
such as reasoning to convert data to different ontologies. All such applications rely on
the principle that client cpus are idle most of the time, so by allowing others to use it
when we do not, we can rely on them at the moment we need it ourselves.
This proposal can have a strong impact on the scale at which we apply Semantic Web
technologies, especially in absence of clear business models. It opens up new directions
in decentralized algorithms, and creates a connection between the Semantic Web and
agent theory, as well as economic models for incentives. We also must pay attention to
challenges such as privacy, perhaps through encryption. Most importantly, this vision
sketches a Web-oriented future path to a Semantic Web for large and small players.
References
1. Filecoin: A decentralized storage network. Whitepaper, Protocol Labs (Aug 2017), https:
// filecoin.io/ filecoin.pdf
2. Berners-Lee, T.: Three challenges for the Web, according to its inventor. World Wide Web
Foundation (Mar 2017), https:// webfoundation.org/ 2017/ 03/ web-turns-28-letter/
3. Buil-Aranda, C., Hogan, A., Umbrich, J., Vandenbussche, P.Y.: sparql Web-querying infras-
tructure: Ready for action? In: Proc. of the 12th Int. Semantic Web Conference (2013)
4. Grubenmann, T., Dell’Aglio, D., Bernstein, A., Moor, D., Seuken, S.: Decentralizing the
Semantic Web: who will pay to realize it? In: Proceedings of the Workshop on Decentralizing
the Semantic Web (2017)
5. Mansour, E., Sambra, A.V., Hawke, S., Zereba, M., Capadisli, S., Ghanem, A., Aboulnaga,
A., Berners-Lee, T.: A demonstration of the Solid platform for social Web applications. In:
Companion Proceedings of the 25th International Conference on World Wide Web (2016)
6. Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system, https:// bitcoin.org/ bitcoin.pdf
7. Verborgh, R., Vander Sande, M., Hartig, O., Van Herwegen, J., De Vocht, L., De Meester, B.,
Haesendonck, G., Colpaert, P.: Triple Pattern Fragments: a low-cost knowledge graph interface
for the Web. Journal of Web Semantics 37–38 (Mar 2016)