The document discusses how semantic technologies can help make customer support systems more intelligent by understanding customer needs better. It proposes using a knowledge model and controlled vocabulary to translate between technical terms used by suppliers and plain language used by customers. This helps provide a more user-friendly digital guidance system within the customer support system. The approach results in a semantic index of content, improved search and recommendation capabilities, and the ability to combine information from multiple knowledge bases automatically.

1. http://www.poolparty.biz/
Smart Customer Support
PoolParty Solutions Systems
How can semantic technologies help to make
customer support systems more intelligent in
Andreas Blumauer order to understand customer's needs better?
a.blumauer@semantic-web.at
25.02.2013
Addressed problem
Customer support systems frequently cause
disorientation due to the technical terms used and a
lack of transparent and easily comprehensible
navigation structures. Providers of products and
services from various sectors (telecommunications,
public administration, law, etc.) use different
languages and differing categories than consumers
(or citizens) do. Thus, in many cases clients have
to deal with frustrating translation work which leads
to misunderstandings and increasing costs in the
Contents call center.
Smart Customer Support Systems ......................... 1
Enterprise Linked Data Integration ......................... 2 Our solution approach
Vocabulary Management ........................................ 3
Users benefit from a guidance system which helps
Semantic Content Management ............................. 4
to achieve orientation at any point of the support
Text Mining of Business News ............................... 5
system. The guidance system consists of semantic
Vertical Search Solutions ....................................... 6
search facilities like search filters (faceted search),
Knowledge Bases ................................................... 7
search refinements, similarity search (see also:
Linked Open Data ................................................... 8
recommender system) and integrated fact boxes
Recommender Systems ......................................... 9
which display further details about the search term
Semantic Search .................................................. 10
which might refer to a product, for example. As a
prerequisite for these improvements, a knowledge
model consisting of concepts (e.g. products,
technologies, services, etc.) its relations and
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differing names (including synonyms) has to be
created. This model is based on an open W3C Enterprise Linked Data
standard called SKOS which makes the effort
future-proof. In some cases it is advisable to split Integration
up the thesaurus into (at least) two modules. A
semantic layer of this kind helps to translate How can linked data be used as a more agile
between the two worlds (supplier/vendor vs. and flexible methodology for enterprise data
client/customer). While the supplier’s thesaurus still integration?
links its concepts to the corresponding parts of the
client’s thesaurus, the thesauri can be managed
separately from each other.
Results
• Semantic index of content base of the
support system
• User-friendly digital guidance system
• Facilities to refine search queries to find
answers to specific questions more easily
• Help users to learn quickly: combine search
results with facts from other knowledge
bases automatically
Used methods, technologies and Addressed problem
standards Putting all the information in one place which
describes a business object like a product, a
• PoolParty knowledge modelling approach customer or a certain technology can ease the life
of many people significantly. Unfortunately, the
• Simple Knowledge Organization System automatic integration of data from various sources
(SKOS) can cause tremendous efforts. Data in enterprises
is organised such that data remains locked up in its
• PoolParty Thesaurus Server
database. Knowledge workers are forced to collect
information from a series of data silos manually to
• PoolParty Extractor
put those pieces together like a puzzle in order to
create the basis for a decision making process.
• PoolParty Search
Data integration projects most often are built upon
yet another inflexible data structure. Numerous
amendments or additions made to the structure or
to the semantics of an information component
cannot be reflected properly by the integration
layer. The result is a landscape consisting of data
silos which are scarcely connected to each other.
Intelligent linkages happen only in the course of ad
hoc processes which are not readily
comprehensible.
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Our solution approach
Vocabulary Management
Web data, but also data in enterprises are
characterized by a great structural diversity as well How can controlled vocabularies become an
as frequent changes. This poses a great challenge easily accessible source of knowledge to link
for applications based on that data. We address information sources more efficiently?
this problem by using a flexible data model that
supports the integration of heterogenous and
volatile data. We make use of linked data
technologies for data integration purposes which
relies on graph-based models. This allows to
incrementally extend the schema by various
properties and constraints. Linked data is based on
open standards which makes the effort future-proof.
Results
• 360o views on specific business objects
('topic pages') like products, companies,
technologies etc.
• Reports based on sometimes complex
queries which can only be answered if data
is used from various sources Addressed problem
• Mashups of unstructured (e.g.: business Benefits from creating and using vocabularies still
news, social media, etc.) and structured seem to be below the invested effort. Whereas
data (e.g.: statistics, legacy data, etc.) controlled vocabularies can build the basis for a
richer metadata management system, it remains
• Mashups of data from the web (e.g.: open still unclear how thesauri or ontologies can also be
government data) and internal data sources used as a valuable information source on its own.
Vocabulary management can help to overcome the
Babylonian language confusion. A thesaurus can
Used methods, technologies and be used by knowledge workers as an encyclopedia
standards to better understand unclear, unintelligible or
ambiguous terms and phrases which occur in a
• Linked data stack large proportion of the documents, mails or
protocols they have to deal with on a daily basis.
• Semantic web standards (RDF, SKOS,
SPARQL etc.)
Our solution approach
• Linked data alignment
In order to get (enterprise) vocabularies widely
• Linked data manager accepted the costs for the creation and
development of such thesauri and vocabularies
• PoolParty Semantic Integrator have to stay as low as possible. This can be
achieved if thesaurus managers get support by
• PoolParty Extractor appropriate methods and software tools to produce
high-quality semantic metadata built upon open
• Large scale RDF triple stores (e.g.: standards. In case the enterprise (or domain-
Virtuoso) specific) thesaurus is built upon W3C's Simple
Knowledge Organization System (SKOS) it can
also build the core of an organization's knowledge
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graph to be reused by many other applications. In
addition, built-in text analytics, several importers Semantic Content
and linked data enrichment tools help to extend the
enterprise vocabulary further and further while Management
keeping the efforts as low as possible. A
comprehensive library of quality- and validity How can linked data help to establish a
checks makes sure that the outcome will meet the metadata layer across systems to link content
highest demands for quality. Putting an enterprise from multiple sources?
vocabulary to the right place means, that it should
be reused by other applications as often as
possible. Several standard APIs allow quick
integration as well as complex queries over the
resulting knowledge graph.
Results
• Enterprise vocabularies fully compatible
with W3C's semantic web standards
(SPARQL, RDF, SKOS)
• Ready to be used within a linked data
enterprise architecture
• Highly comfortable thesaurus editor, fully
web-based with hundreds of features
• Importers for legacy data sources Addressed problem
• Integrations with frequently used enterprise Managing content in a CMS is a cost-intensive
systems like Sharepoint, Confluence or task. To take care of metadata as an integral part of
Drupal professional content management is likely to be
neglected. Using referencable metadata on top of
• Facilities to enrich thesauri with terms from our content is key to increase the value of such
document collections and linked open data cost-intensive assets.
Used methods, technologies and Our solution approach
standards
Text analytics based upon controlled vocabularies
• PoolParty Thesaurus Server can help to keep the cost of managing metadata in
a CMS as low as possible. Annotating and
• Simple Knowledge Organization System categorizing content by using thesauri also makes
(SKOS) sure that a highly-expressive semantic index of our
content repositories can be built later on. Automatic
• PoolParty Knowledge Modeling Approach text analytics in combination with comfortable user-
dialogues for semi-automatic content tagging can
• Linked Data enrichment be used to link, categorize and annotate content.
Our solution approach is aiming to establish a
• Data importers and text analytics metadata layer outside the actual content
management system to make an integration with
• Thesaurus Quality and Validity Checker other content repositories as easy as possible.
(qSKOS)
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Results
Text Mining of Business
• Automatic document annotation and
categorization (XML documents, plain text)
News
• Semi-automatic tagging dialogues based on How can semantic technologies help to filter
tag recommender out news items and to put them in a specific
context automatically?
• Rule-based named entity recognition
• Sentiment analysis
• Connectors to enterprise linked data
repository
Used methods, technologies and
standards
• Concept-based annotation
• Simple Knowledge Organization System
(SKOS)
• Natural language processing
• PoolParty Extractor
Addressed problem
Working as an analyst, researcher, product
manager or as a journalist means that one has to
skim through hundreds of news articles per day. On
the one hand the usage of social networks and
attached reputation systems can help to narrow
down the number of relevant sources, on the other
hand an ever increasing amount of information has
ended up on our desktops since we have become
active members of Twitter, Linkedin or other social
media channels. Unstructured information makes
up the largest portion of frequently quoted 'big
data'. Being able to deal with unstructured
information in combination with structured data like
statistics or relational databases has become a key
ability to succeed in a variety of knowledge
intensive industries.
Our solution approach
Domain-specific text mining becomes more precise
when built upon controlled vocabularies. The
analysis of large amounts of mainly short
documents like business news requires highly
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performant algorithms built on top of specific
knowledge graphs. The outcome of text mining in Vertical Search Solutions
the context of linked data is rather a 'web of linked
entities' than simply a 'semantic document index'.
How can semantic knowledge models
By using linked data based knowledge models in its contribute to a highly efficient topical search
core, PoolParty platform is able to combine text engine?
mining with graph databases.
Results
• Precise and highly performant text mining
for specific domains
• Extraction of highly structured knowledge
graphs from semi-structured and
unstructured information
• Basis for integrated views over
heterogeneous information sources
Used methods, technologies and
standards
• PoolParty Extractor
Addressed problem
• Natural Language Processing
Common paradigms of search engine development
• SKOS not necessarily reach the optimal results when
specialized information put into a specific context or
process has to be retrieved. A vertical search
engine, in contrast to a general web or enterprise
search engine, focuses on a specific knowledge
domain. To bring such a topical search engine to its
full potential the underlying index has to be built
upon a specific knowledge model. A vertical search
engine, as distinct from a general search tool,
makes also use of an individual user interface and
domain-specific navigational elements. But most
importantly, in case the search engine shall cover a
clearly defined scope, the use of semantic
knowledge models achieves a very good cost-
benefit ratio.
Our solution approach
Structured information as well as unstructured text
can build the basis for vertical search solutions. By
reflecting the knowledge about the search domain
with means of a thesaurus, a more precise
semantic document index can be built. Using linked
data based knowledge graphs for document
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indexing instead of pure term-based vocabularies
allows to further enrich the document basis by facts Knowledge Bases
from other knowledge models. Users benefit from
richer search results not only consisting of
How can semantic technologies help to make
documents but also of facts and figures related to collaborative knowledge bases better
the actual information needs. Since a vertical accessible for employees?
search solution is built around a well-defined scope,
it is also advisable to generate and provide specific
search assistants like facets or search refinement
tools.
Results
• Smart search assistants (faceted search
etc.)
• Precise search results
• Search application and interfaces
customized to the needs of the subject
matter experts
• Integrated views on structured and
unstructured information alike Addressed problem
Transforming a simple document server into a
Used methods, technologies and collaborative knowledge base which serves as a
standards valuable source for knowledge workers in their daily
work is not as simple as it seems to be. On the one
• PoolParty Thesaurus Server hand collaboration platforms like enterprise wikis
most often are the right choice to encourage people
• PoolParty Search Server to collect ideas for new content or to make
knowledge about products and services better
accessible. On the other hand knowledge bases
tend to get tattered over time.
Our solution approach
In order to make specific knowledge about
business processes, methods or technologies
available for as many employees as possible, we
combine the best of three worlds: enterprise
collaboration software, text mining and controlled
vocabularies. This results in solutions which fulfill
the demand for highly dynamic and flexible
knowledge bases, still stable (technical and
content-wise) enough to be used in professional
environments. Since the knowledge base is
generated around a controlled vocabulary acting as
a meta-layer, traditional navigation structures like
trees no longer act as a rigid corset which makes
traversing of graph-like structures impossible.
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Semi-automatic tools for linking, categorizing and
content indexing is key to overcome this problem. Linked Open Data
Putting a controlled vocabulary in place which
grows in parallel to the content base demands new
How can open semantic web standards
and more agile patterns of taxonomy or thesaurus stimulate new ways to distribute and reuse data
management than 'traditional' approaches would and information across intraorganisational and
provide. extraorganisational boundaries?
Results
• Linked knowledge objects on top of
enterprise collaboration platforms like
Confluence or Sharepoint
• Semantic search over knowledge bases
• Automatic content enrichment
Used methods, technologies and
standards
• Atlassian Confluence
• Microsoft Sharepoint Addressed problem
• Drupal For many organizations the efficient distribution of
its data has become a main task. For example,
• PoolParty PowerTagging NPOs or NGOs which want to stimulate specific
markets can free up their information, make it
• Semantic Sharepoint available and accessible to allow new entrants.
Publishers have recognized that opening up (parts
• Semantic Confluence of) their databases can stimulate the demand for
even more information inducing finally the act of
purchase. Open semantic web standards play a
key role in this distribution policy since they allow a
high degree of reusability and linking.
Our solution approach
The strict usage of semantic web standards, not
only as an export format but as the way to
represent data internally allows us to bring linked
data to its full potential. Initial phases of a
knowledge graph project might start with the
creation of a SKOS thesaurus further enriched by
facts or ontological statements from other linked
data sources. The publication of linked data inside
corporate boundaries or of linked open data on the
(semantic) web is technically spoken the same
task. In both cases data can be accessed
programmatically by the usage of standard APIs
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like SPARQL. Data becomes a self-describing
digital asset to build semantically enhanced Recommender Systems
applications or mashups.
How can semantic technologies help to enrich a
DMS or CMS with intelligent functions like
Results recommender systems?
• Linked Data Server as part of the PoolParty
Thesaurus Server
• Linked Open Data Portals
• Linked Data Manager to retrieve, extract
and transform open data automatically and
periodically
• Linked Data alignment tools
Used methods, technologies and
standards
• PoolParty Thesaurus Server
• Linked Data Manager Addressed problem
• Semantic Web Standards (SKOS, RDF Given the plethora of information in large document
Schema, SPARQL) collections or content repositories, the provision of
digital assistants can become essential to survive.
• Drupal Who else has been working on a similar document
or a related issue I am working on right now? Is
• Large scale RDF triple stores (e.g.: there a corresponding slide deck available which
Virtuoso) deals with the same questions like the paper I am
writing just now? Typical document or content
management systems are still more focussed on
workflow management or archiving solutions than
on functionalities which help to put content into the
context of the actual work step.
Our solution approach
Recommender engines work on top of semantic
fingerprints. Each business object (resource) is
represented by its semantic metadata which is a
fragment of the overall enterprise knowledge graph.
This meta information is used to detect hidden links
between objects like persons or documents.
Controlled vocabularies based on SKOS and linked
data build the backbone to express the semantic
fingerprint of each resource. Algorithms which
calculate the 'similarity' between such graph
fragments are used as core elements for the
recommendation engine.
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Results
Semantic Search
• Content management workflows free of
interruptions and media breaks How can semantic search (which goes beyond
search over documents only) be realized in the
• Avoid unnecessary overlapping and context of enterprise information systems?
duplications of work
• Support and stimulate cross-reading in
knowledge bases or cross-selling in shop
systems
• Enable serendipity effects
Used methods, technologies and
standards
• Semantic fingerprints
• Similarity algorithms and machine learning
• SPARQL
• PoolParty Search Server Addressed problem
• Large scale triple stores (e.g.: Virtuoso) Search has become a more and more important
functionality in most information management
systems. Learning from web search engines, most
intranet searches have already introduced some
useful assistance functions like auto-complete.
Semantic search can go far beyond those rather
simple features and can help to reduce search
times to a minimum while user experience will
improve noticeably. Looking at digital assistants like
Apple's Siri, it becomes obvious that the role of
search systems will become more and more
important for the next generations of knowledge
bases. Semantic search and search in general is
still very focused on the idea of retrieving a list of
relevant documents whilst in reality knowledge
workers have to find and link information from a
huge variety of sources including statistical
databases, videos or personnel databases.
Our solution approach
Semantic search in the context of linked data
means to search over a knowledge graph including
document search. This approach makes complex
queries possible, e.g.: show me all business news
which mention at least one of our suppliers of
components used in product ABC. The basis for
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such complex queries is made up by an enterprise
linked data store containing a 'semantic index' of
various legacy data sources combined with the
knowledge graph plus enrichments from other
linked data sources, taxonomies and ontologies.
Results
• search engine which provides means for
complex queries
• queries over various kinds of information
(documents, relational databases,
taxonomies, etc.)
• personalized search
Used methods, technologies and
standards
• PoolParty Search Server
• SPARQL
• Large scale triple stores (eg.: Virtuoso)
This work is licensed under a
Creative Commons Attribution-NoDerivs 3.0
Unported License.
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