The site is updated hourly from blogs are selected by
Dave Beckett
from those on the Semantic Weblogs page.
Development work on this site was done by Dave Beckett,
Matt Biddulph, Edd Dumbill and Phil McCarthy.
2016-09-23, Stuart Sutton has announced his intention to step
down as DCMI Managing Director effective 30 June 2017. Over the
coming months, the DCMI Executive Committee and the Governing Board
will be engaged in succession planning and the process of replacing
the Managing Director. For additional information concerning the
succession and appointment process, contact DCMI Chair-Elect Paul
Walk at p[dot]walk[at]ed[dot]ac[dot]uk. Future announcements
concerning the succession process will be posted here from time to
time.
2016-09-23, DCMI is pleased to announce that Synaptica is
supporting
DC-2016 in Copenhagen as a Gold Sponsor. Since 1995, Synaptica
has been developing innovative software tools for organizing,
indexing and classifying information, and for discovering
knowledge. All of Synaptica's award-winning software products are
built on a foundation of open standards and a commitment to
client-led solutions and uncompromising customer service.
Synaptica's Linked Canvas will be featured in a demonstration
during the Conference Opening Reception. Linked Canvas is an
easy-to-use tool designed for the cultural heritage community as
well as schools and colleges to build interactive educational
resources. For more information about Synaptica and Linked Canvas,
visit
http://dcevents.dublincore.org/IntConf/index/pages/view/sponsors16#synaptica.
Visit http://dcevents.dublincore.org/IntConf/dc-2016
for more information about the conference and to register.
2016-09-23, The Danish
Bibliographic Centre (DBC) joins in supporting DC-2016 in
Copenhagen as Sponsor of the Conference Delegate Bags. The DBC's
main task in Denmark is the development and maintenance of the
bibliographic and IT infrastructure of Danish libraries. The DBC
handles registration of books, music, AV materials, Internet
documents, articles and reviews in newspapers and magazines in the
National Bibliography, develops Danbib, the Danish union catalogue,
and the infrastructure for interlibrary loan. Danbib is comprised
of the National Bibliography and the holdings of the libraries. DBC
also develops bibliotek.dk —
the citizen's access to all Danish publications and the holdings of
the Danish libraries. DBC's IT development is based on open source
and service oriented architecture. DBC is a public limited company
owned by Local Government Denmark and the Danish State.
2016-09-23, The DCMI Governing Board is pleased to announce
that Ana
Alice Baptista has been appointed to the DCMI Governing Board
as an Independent Member. She also assumes the role of Chair-Elect
of the Governing Board at the closing ceremony of DC-2016 in
Copenhagen. She will succeed Paul Walk as Chair of the Board in
2017. Ana is a professor at the Information Systems Department and
a researcher at ALGORITMI Center, both at University of Minho,
Portugal. She graduated in computer engineering and has a PhD on
Information Systems and Technologies. She is also a member of the
Elpub conference series Executive Committee, participated in
several R&D projects, and was an evaluator of project proposals
under FP7. For more information about the Governing Board, visit
the DCMI website at http://dublincore.org/about/oversight/.
2016-09-23,
DC-2016 in Copenhagen, Denmark on 13-16 October is rapidly
approaching. The program
promises a rich array of papers, project reports, presentations,
demonstrations, posters, special panels, workshops and an exciting
keynote by Elsevier's Bradley Allen. You will not want to miss this
one! Register now at http://dcevents.dublincore.org/IntConf/index/pages/view/reg16.
Extract Structured Content, Tag Concepts & Entities
Cognonto
is brand new. At its core, it uses a structure of nearly 40 000
concepts. It has about 138,000 links to external classes and
concepts that defines huge public datasets such as Wikipedia,
DBpedia and USPTO. Cognonto is not a children’s toy. It is huge and
complex… but it is very usable. Before digging into the structure
itself, before starting to write about all the use cases that
Cognonto can support, I will first cover all of the tools that
currently exist to help you understand Cognonto and its conceptual
structure and linkages (called KBpedia).
The embodiment of Cognonto that people can see are the tools we
created and that we made available on the cognonto.com web site. Their goal is to
show the structure at work, what ties where, how the conceptual
structure and its links to external schemas and datasets help
discover new facts, how it can drive other services, etc.
This initial blog post will discuss the demo section of the web
site. What we call the Cognonto demo is a web page crawler that
analyzes web pages to tag concepts, to tag named entities, to
extract structured data, to detect language, to identity topics,
and so forth. The demo uses the KBpedia structure and its linkages
to Wikipedia, Wikidata, Freebase and USPTO to tag content that appears in
the analyzed web pages. But there is one thing to keep in mind: the
purpose of Cognonto is to link public or private datasets to the
structure to expand its knowledge and make these tools (like the
demo) even more powerful. This means that a private organization
could use Cognonto, add their
own datasets and link their own schemas, to improve their
own version of Cognonto or to tailor it for their own
purpose.
Let’s see what the demo looks like, what is the information it
extracts and analyzes from any web page, and how it ties into the
KBpedia structure.
Analyzing a web page
The essence of the Cognonto demo is to analyze a web page. The
steps performed by the demo are:
Crawling the web page’s content
Extracting text content, defluffing and normalizing it
Detecting the language used to write the content
Extracting the copyright notice
Extracting metadata from the web page (HTML, microformats,
RDFa, etc.)
Querying KBpedia to detect named entities
Querying KBpedia to detect concepts
Querying KBpedia to find information about the web page
Analyzing extracted entities and concepts to determine the
publisher
Analyzing extracted concepts to determine the most likely
topics
What the demo shows is the header of the analyzed web
page. The header is composed of the title of the web page and
possibly a short description and an image. All of this information
comes from the extracted metadata content of the page. Then you are
presented with 5 tabs:
Concepts: which shows the body content and
extracted metadata of the web page tagged with all detected
KBpedia concepts
Entities: which shows the body content and
extracted metadata of the web page tagged with all detected
KBpedia named entities that exists in the knowledge base
Analysis: which shows all the different kinds of
analysis performed by the demo
graphs: which shows how the topics found during
the topic analysis step ties into the KBpedia conceptual
structure
export: which shows you what the final resultset
looks like
Concepts tab
The concepts tab is the first one that is presented to you. All
of the concepts that exist in KBpedia (among its ~40 000 concepts)
will appear in the body content of the web page and its extracted
metadata. There is one important thing to keep in mind here: the
demo does detect what it considers to be the body content of the
web page. It will defluff it, which means that it will remove the
header, footer and sidebars and all other irrelevant content that
can appear in the page surrounding the body content of that
page. The model used by the demo works better on articles like web
pages. So there are some web pages that may not end with much
extracted body content for that very reason.
All of the concepts that appear in red are the ones
that the demo considers to be the core concepts of the web page.
The ones in blue are all of the other ones. If you
mouse over any of these tagged terms, you will be presented a
contextual menu that will show you one or multiple concepts that
may refer to that surface form (the word in the text). For example,
if you mouse over administration, you will be
presented with two possible concepts for that word:
However, if you do the same for airstrikes then you
will be presented a single unambiguous concept:
If you click on any of those links, then you will be redirected
to a
KBpedia reference concept view page. You will see exactly how
that concepts ties into the broader KBpedia conceptual structure.
You will see all of its related (direct and inferred) concepts, and
how it links to external schemas, vocabularies and ontologies. It
will show you lists of related entities, etc.
What all of this shows you is how these tagged concepts are in
fact windows to a much broader universe that can be understood
because all of its information is fully structured and can be
reasoned upon. This is the crux of the demo. It shows that the
content of a web page is not just about its content, but its entire
context as well.
Entities tab
The entities tab presents information in exactly the same manner
as the Concepts tab. However the content that is
tagged is different. Instead of tagging concepts, we tag
named entities. These entities (in the case of this demo)
come from the entities datasets that we linked to KBpedia, namely:
Wikipedia, Wikidata, Freebase and USPTO. These are a different kind
of window than the concepts. These are the named things of this
World that we detect in the content of the web page.
But there is one important thing to keep in mind: these are the
named things that exist in the knowledge base at that
moment. The demo is somewhat constrained to tens of millions of
fully structured named entities that comes from these various
public data sources. However the purpose of a knowledge base is to
be nurtured and extended. Organizations could add private datasets
into the mix to augment the knowledge of the system or to
specialize it to specific domains of interest.
Another important thing to keep in mind is that we have
constrained this demo to a specific domain of things, namely
organizations. The demo is only considering a subset of entities
from the KBpedia knowledge base, namely anything that is an
organization. This shows how KBpedia can be sliced and diced to be
domain specific. How millions of entities can be categorized in
different kinds of domains id what leads to purposeful dedicated
services.
The tag that appears in orange in the text is the
organization entity that has been detected to be the organization
that published that web page. All the other entities appear in
blue. If you click on one of these entities, then you
will be redirected to the entity
view page. That page will show you all the structured
information we have related to these entities in the knowledge
base, and you will see how it ties to the KBpedia conceptual
structure.
Analysis tab
The analysis tab is to core of the demo. It presents some
analysis of the web page that uses the tagged concepts and entities
to generate new information about the page. These are just some
analysis we developed for the demo. All kinds of other analysis
could be created in the future depending on the needs of our
clients.
Language analysis
The first thing we detect is the language used to write the web
page. The analysis is performed on the extracted body content of
the page. We can detect about 125 languages at the moment. Cognonto
is multilingual at its core, but at the moment we only configured
the demo to analyze English web pages. Non-English web pages can be
analyzed, but only English surface forms will be detected.
Topic analysis
The topic analysis section shows what the demo considers to be
the most important concepts detected in the web page. Depending on
a full suite of criteria, one concept will score higher than
another. Note that all the concepts exist in the KBpedia conceptual
structure. This means that we don’t simply “tag” a concept. We tag
a concept that is part of an entire structure with hundreds and
thousands of parents or children concepts, and linked to external
schemas, vocabularies and ontologies. Again, these are not simple
tags, these are windows into a much broader [conceptual] world.
Publisher analysis
The publisher analysis section shows what we consider to be the
organization that published the web page. This analysis is much
more complex in its processing. It incurs an analysis pipeline that
includes multiple machine learning algorithms. However there is one
thing that distinguishes it at its core than other more
conventional machine learning pipelines: the heavy leveraging of
the KBpedia conceptual structure. We use the tagged named entities
the demo discovered, we check their types and then we analyze their
structure within KBpedia, by using their SuperTypes for
further analysis. Then we check the occurrence of their placements
in the page and we compute a final likelihood score and we
determine if one of these tagged entities can be considered the
publisher of the web page.
Organizational Analysis
The organizational analysis is one of the steps that is
performed by the Publisher analysis that we wanted to
make explicit. What we do here is to show all the
organization entities that we detected in the web page, and
where in the web page (metadata, body content, etc.) they do
appear.
The important thing to understand here is how we detect
organization. We do not just check if the entities are of
type Organization. What we do is to check if the entities
are of type Organization by inference. What does that mean?
It means that we use the KBpedia structure to keep all the tagged
named entities that can be inferred to be an Organization. All of
these organization entities are not defined to be of type
kbpedia:Organization. However, what this analysis does
is to check if the entities are of type
kbpedia:Organization. But how is that possible?
Cognonto does so by using the KBpedia structure, and its linkages
to external vocabularies, schemas and ontologies, to determine
which of the tagged named entities are of type
kbpedia:Organizationby inference.
Take a look at the
kbpedia:Organization page. Take a look at all the Core
structure and External structure linkage this
single concept has with external conceptual structure. It is this
structure that is used to determine if a named entity that exists
in the KBpedia knowledge base is an Organization or not. There is
no magic, but it is really powerful!
Metadata Extraction
All the extracted metadata by the demo is displayed at the end
of the Analysis tab. This meta data comes from the
HTML meta elements or some embedded microdata and RDFa
structured content. Everything that got detected is displayed in
this tab.
Graphs tab
The graph tab shows you a graphical visualization tool. The
purpose is just to contextualize the identified concepts of the
Topics analysis with the upper structure
of KBpedia. It shows how everything is interconnected. But keep in
mind that these are just tiny snapshots of the whole picture. There
exists millions of links between these concepts and billions of
inferred facts!
Here is a hierarchical view of the graph:
Here is a network view of that same graph:
Export tab
The export that is just a way for the user to visualize the
resultset generated by Cognonto, enabling the web user interface to
display the information you are seeing. It shows that all the
information is structured and could be used by other computer
systems for other means.
Conclusion
At the core of everything there is one thing: the KBpedia
conceptual structure. It is what is being used across the board. It
is what instructs machine learning algorithms, what helps us to
analyze textual content such as web pages, this is what helps us to
identify concepts and entities, it is what helps us to
contextualize content, etc. This is the heart of Cognonto and
everything else is just nuts and bolts. KBpedia can, and should, be
extended with other private and public data sources.
Cognonto/KBpedia is a living thing: it heals, it adapts and it
evolves.
I am proud to announce the start of a new
venture called Cognonto. I am
particularly proud of it because even if it is just starting, it is
in fact more than eight years old. It is the embodiment of eight
years of research, of experimentation, of a big deal of frustration
and of great joy with my long-time partner Mike.
Eight years ago, we set a 5-to-10-year vision for our work as
partners. We defined an initial series of technological goals for
which we outlined a series of yearly milestones. The goals were
related to help solving decades old problems with data integration
and interoperability using a completely new research field (at the
time): the Semantic Web.
And there we are eight years later, after working for an endless
number of hours to create all kinds of different projects and
services to pay for the research and the pieces of technologies we
develop for these purposes. Cognonto is the embodiment of that
effort, but it also created a series of other purposeful projects
such as the creation of Stuctured Dynamics, UMBEL, the Open Semantic Framework and
a series of other open source
collaterals.
We spent eight years to create, sanitize, to make coherent and
consistent, to generate and regenerate a conceptual structure of
now 38,930 reference concepts with 138,868 mapping links to 27
external schemas, vocabularies and datasets. This led to the
creation of KBpedia, which is the knowledge graph that drives
Cognonto. The full statistics
are available here.
I can’t thank Mike enough for this long and wonderful journey
that led to the creation of Cognonto. I sent him an endless number
of concepts lists that he diligently screened, assessed and mapped.
We spent hundred of hours to discuss the knots and bolts of the
structure, to argue about its core concepts and how it should be
defined and used. It was not without pain, but I believe that the
result is truly astonishing.
I won’t copy/paste the Cognonto
press release here, a link will suffice. I it is just not
possible for me to write a better introduction than the two pagers
that Mike wrote for the press release. I would also suggest that
you read his Cognonto introduction blog post:
Cognonto is on the Hunt for Big AI Game.
In the coming weeks, I will write a lot about Cognonto, what it
is, how it can be used, what are its use cases, how the information
that is presented in the demo and the knowledge graph sections
should be interpreted and what these pages tell you.
2016-09-02, DCMI is pleased to announce that Tongfang Co., Ltd. has become the
DC-2016 Reception Sponsor. Tongfang is a high-tech company
established in 1997. Over the years, Tongfang has taken 'developing
into a world-class high-tech enterprise' as its goal, and 'serving
the society with science and technology' as its mission. The
company, by making use of strengths of Tsinghua University in
research and human resources, has been implementing such strategies
as 'technology + capital', 'cooperation and development' and
'branding + internationalization'. With a corporate culture
featuring 'action, exploration and excellence; loyalty,
responsibility and value', Tongfang has been making explorations
and innovations in industries of information, energy and
environment. As of 2013, Tongfang's has a total asset of more than
$5 billion. Its annual revenue was over $3.5 billion. For more
information on becoming a sponsor for DC-2016, see http://dcevents.dublincore.org/public/sponsors/Sponsor-16.pdf.
Visit the conference website at
http://dcevents.dublincore.org/index.php/IntConf/dc-2016/schedConf/.
2016-09-02, DCMI is pleased to announce that Shanghai RFID System Technology Co.,
Ltd., has become a Gold Sponsor of
DC-2016 in Copenhagen. Founded on October 10, 2004, Shanghai
RFID System Technology Co., Ltd. is a leading automatic management
solution provider for libraries in China. The library of Chenyi
College, Jimei University was the first library installed with RFID
automatic system in China. In the past 12 years, the company has
provided services for more than 400 libraries in China such as
National Library of China, Shanghai Library, and Hangzhou Library.
The services provided include library self-service system, book
automatic sorting system, digital reading solution, device
monitoring, mini library, cloud and big data platform, knowledge
discovery system, and mobile applications in library. For more
information on becoming a sponsor for DC-2016, see http://dcevents.dublincore.org/public/sponsors/Sponsor-16.pdf.
Visit the conference website at
http://dcevents.dublincore.org/index.php/IntConf/dc-2016/schedConf/.
I’ve been interviewed a couple of times recently by people
interested in understanding how best to publish data to make it
useful for others. Once by a startup and a couple of times by
researchers. The core of the discussion has essentially been the
same question: “how do you know if a dataset will be useful to
you?”
I’ve given essentially the same answer each time. When I’m
sifting through dataset descriptions, either in a portal or via a
web search, my first stage of filtering involves looking
for:
A brief summary of the dataset: e.g. a title and a
description
The licence
Some idea of its coverage, e.g. geographic coverage, scope of
time series, level of aggregation, etc
Whether it’s in a usable format
Beyond, that there’s a lot more that I’m interested in: the
provenance of the data, its timeliness and a variety of quality
indicators. But those pieces of information are what I’m
looking for right at the start. I’ll happily jump through hoops to
massage some data into a better format. But if the licence or
coverage isn’t right then its useless to me.
We can frame these as questions:
What is it? (Description)
Can I use it? (Licence)
Will it help answer my question? (in whole, or part)
How difficult will it be to use? (format, technical
characteristics)
It’s frustrating how often these essentials aren’t readily
available.
Here’s an example of why this is important.
A weather data example
I’m currently working on a project that needs access to local
weather observations. I want openly licensed temperature readings
for my local area.
On the upcoming Monday (05.09.2016), AKSW group will discuss
topics related to Semantic Web and LOD Cloud Statistics. Also, we
will have invited speaker from University of Leipzig Library (UBL)
Dr. Astrid Vieler talking about OpenAccess at Leipzig
University.
Abstract: Over the past years, the
size of the Data Web has increased significantly, which makes
obtaining general insights into its growth and structure both more
challenging and more desirable. The lack of such insights hinders
important data management tasks such as quality, privacy and
coverage analysis. In this paper, we present the LODStats dataset,
which provides a comprehensive picture of the current state of a
significant part of the Data Web. LODStats is based on RDF datasets
from data.gov, publicdata.eu and datahub.io data catalogs and at
the time of writing lists over 9 000 RDF datasets. For each RDF
dataset, LODStats collects comprehensive statistics and makes these
available in adhering to the LDSO vocabulary. This analysis has
been regularly published and enhanced over the past five years at
the public platform lodstats.aksw.org. We give a comprehensive
overview over the resulting dataset.
OpenAccess at Leipzig University
Invited talk by Dr. Astrid Vieler from Leipzig
University Library (UBL). The talk will be about Open Access in
general and the Open
Access Policy of our University in special. She will tell us
more about our right, which we have toward the publishers, and she
gives us advice and hints on how we can increase the visibility of
our publications.
After the talks, there is more time for discussion in smaller
groups as well as coffee and cake. The colloquium starts at 3 p.m.
and is located on 7th floor (Leipzig, Augustusplatz 10,
Paulinum).
The Winnipeg City’s NOW
(Neighbourhoods Of Winnipeg) Portal is an initiative to create
a complete neighbourhood web portal for its citizens. At the core
of the project we have a set of about 47 fully linked, integrated
and structured datasets of things of interests to Winnipegers. The
focal point of the portal is Winnipeg’s 236 neighbourhoods, which
define the main structure of the portal. The portal has six main
sections: topics of interests, maps, history, census, images and
economic development. The portal is meant to be used by citizens to
find things of interest in their neibourhood, to learn their
history, to see the images of the things of interest, to find tools
to help economic development, etc.
The NOW portal is not new; Structured Dynamics was also
its main technical contractor for its first release in 2013.
However we just finished to help Winnipeg City’s NOW team to
migrate their older NOW portal from OSF 1.x to OSF 3.x and from Drupal 6
to Drupal 7; we
also trained them on the new system. Major improvements accompany
this upgrade, but the user interface design is essentially the
same.
The first thing I will do is to introduce each major section of
the portal and I will explain the main features of each. Then I
will discuss the new improvements of the portal.
Datasets
A NOW portal user won’t notice any of this, but the main feature
of the portal is the data it uses. The portal manages 47 datasets
(and growing) of fully structured, integrated and linked datasets
of things of interests to Winnipegers. What the portal does is to
manage entities. Each kind of entity (swimming pools, parks,
places, images, addresses, streets, etc.) are defined with multiple
properties and values. Several of the entities reference other
entities in other datasets (for example, an assessment parcel from
the Assessment Parcels dataset references neighbourhoods entities
and property addresses entities from their respective
datasets).
The fact that these datasets are fully structured and integrated
means that we can leverage these characteristics to create a
powerful search experience by enabling filtering of the information
on any of the properties, to bias the searches depending where a
keyword search match occurs, etc.
Here is the list of all the 47 datasets that currently exists in
the portal:
The most useful feature of the portal to me is its full-text
search engine. It is simple, clean and quite effective. The search
engine is configured to try to give the most relevant results a NOW
portal user may be searching. For example, it will positively bias
some results that comes from some specific datasets, or matches
that occurs in specific property values. The goal of this biasing
is to improve the quality of the returned results. This is somewhat
easy to do since the context of the portal is well known and we can
easily boost scoring of search results since everything is fully
structured.
Another major gain is that all the search results are fully
templated. The search results do not simply return a title and some
description for your search results. It does template all the
information the system has about the matched results, but also
displays the most relevant information to the users in the search
results.
For example, if I search for a
indoor swimming pool, in most of the cases it may be to call
the front desk to get some information about the pool. This is why
different key information will be displayed directly in the search
results. That way, most of the users won’t even have to click on
the result to get the information they were looking for directly in
the search results page.
Here is an example of a search for the keywords main
street. As you can notice, you are getting different
kind of results. Each result is templated to get the core
information about these entities. You have the possibility to focus
on particular kind of entities, or to filter by their location in
specific neighbourhoods.
Templated Search Results
Now let’s see some of the kind of entities that can be searched
on the portal and how they are presented to the users.
Here is an example of an assessment parcel that is located in
the St. John’s neighbourhood. The address, the value, the type and
the location of the parcel on a map is displayed directly into the
search results.
Another kind of entity that can be searched are the property
addresses. These are located on a map, the value of the parcels and
the building and the zoning of the address is displayed. The
property is also linked to its assessment parcel entity which can
be clicked to get additional information about the parcel.
Another interesting type of entity that can be searched are the
streets. What is interesting in this case is that you get the
complete outline of the street directly on a map. That way you know
where it starts and where it ends and where it is located in the
city.
There are more than a thousand geo-localized images of all
different things in the city that can be searched. A thumbnail of
the image and the location of the thing that appears on the image
appears in the search results.
If you were searching for a nursery for your new born child,
then you can quickly see the name, location on a map and the phone
number of the nursery directly in the search result.
There are just a few examples of the fifty different kind of
entities that can appear like this in the search results.
Mapping
The mapping tool is
another powerful feature of the portal. You can search like if you
were using the full-text search engine (the top search box on the
portal) however you will only get the results that can be
geo-localized on a map. You can also simply browse entities from a
dataset or you can filter entities by their properties/values. You
can persist entities you find on the map and save the map for
future reference.
In the example below, it shows that someone searched for a
street (main street) and then he persisted it on the map. Then he
search for other things like nurseries and selected the ones that
are near the street he persisted, etc. That way he can visualize
the different known entities in the portal on a map to better
understand where things are located in the city, what exists near a
certain location, within a neighbourhood, etc.
Census Analysis
Census information is vital to the good development of a city.
They are necessary to understand the trends of a sector, who
populates it, etc., such that the city and other organizations may
properly plan their projects to have has much impact as
possible.
These are some of the reason why one of the main section of the
site is dedicated to census
data. Key census indicators have been configured in the portal.
Then users can select different kind of regions (neighbourhood
clusters, community areas and electoral wards) to get the numbers
for each of these indicators. Then they can select multiple of
these regions to compare each other. A chart view and a table view
is available for presenting the census data.
History, Images & Points of Interest
The City took the time to write the history of each of its
neighbourhoods.
In additional to that, they hired professional photographs to
photograph the points of interests of the city, to geo-localize
them and to write a description for each of these photos. Because
of this dedication, users of the portal can learn a much about the
city in general and the neighbourhood they live in. This is what
the History and Image sections of the website are about.
Historic buildings are displayed on a map and they can be
browsed from there.
Images of points of interests in the neighbourhood are also
located on a map.
Find Your Neighbourhood
Ever wondered in which neighbourhood you live in? No problem, go
on the home page, put your address in the Find your Neighbourhood section
and you will know it right away. From there you can learn more
about your neighbourhood like its history, the points of interest,
etc.
Your address will be located on a map, and your neighbourhood
will be outlined around it. Not only you will know in which
neighbourhood you live, but you will also know where you live
within it. From there you can click on the name of the neigbourhood
to get to the neighbourhood’s page and start learning more about it
like its history, to see photos of points of interest that exists
in your neighbourhood, etc.
Browsing Content by Topic
Because all the content of the portal is fully structured, it is
easy to browse its content using a well defined topic structure. The
city developed its
own ontology that is used to help the users browse the content
of the portal by browsing topics of interest. In the example below,
I clicked the Economic Development node and then the Land
use topic. Finally I clicked the Map button to display
things that are related to land use: in this case, zoning and
assessment parcels are displayed to the user.
This is another way to find meaningful and interesting content
from the portal.
Depending on the topic you choose, and the kind of information
related to that topic, you may end up with different options like a
map, a list of links to documents related to that topic, etc.
Export Content
Now that I made an overview of each of the main features of the
portal, let’s go back to the geeky things. The first thing I said
about this portal is that at its core, all information it manages
is fully structured, integrated and linked data. If you get to the
page of an entity, you have the possibility to see the underlying
data that exists about it in the system. You simply have to click
the
Export tab at the top of the entity’s page. Then you will have
access to the description of that entity in multiple different
formats.
In the future, the City should (or at least I hope will) make
the whole set of datasets fully downloadable. Right now you only
have access to that information via that export feature per entity.
I hope because this NOW portal is fully disconnected from another
initiative by the city: data.winnipeg.ca, which uses
Socrata. The problem is that
barely any of the datasets from NOW are available on
data.winnipeg.ca, and the ones that are appearing are
the raw ones (semi-structured, un-documented, un-integrated and
non-linked) all the normalization work, the integration work, the
linkage work done by the NOW team hasn’t been leveraged to really
improve the data.winnipeg.ca datasets catalog.
New with the upgrades
Those who are familiar with the NOW portal will notice a few
changes. The user interface did not change that much, but multiple
little things got improved in the process. I will cover the most
notable of these changes.
The major changes that happened are in the backend of the
portal. The data management in OSF for Drupal 7 is incompatible
with what was available in Drupal 6. The management of the entities
became easier, the configuration of OSF networks became a breeze. A
revisioning system has been added, the user interface is more
intuitive, etc. There is no comparison possible. However, portal
users’ won’t notice any of this, since these are all site
administrator functions.
The first thing that users will notice is the completely new
full-text search engine. The underlying search engine is almost the
same, but the presentation is far better. All entity types have
gotten their own special template, which are displayed in a special
way in the search results. Most of the time results should be much
more relevant, filtering is easier and cleaner. The search
experience is much better in my view.
The overall site performance is much better since different
caching strategies have been put in place in OSF 3.x and OSF for
Drupal. This means that most of the features of the portal should
react more swiftly.
Now every type of entity managed by the portal is templated:
their webpage is templated in specific ways to optimize the
information they want to convey to users along with their search
result “mini page” when they get returned as the result of a search
query.
Multi-linguality is now fully supported by the portal, however
not everything is currently templated. However expect a fully
translated NOW portal in French in the future.
Creating a Network of Portals
One of the most interesting features that goes with this upgrade
is that the NOW portal is now in a position to participate into a
network of OSF instances. What does that mean? Well, it means that
the NOW portal could create partnerships with other local
(regional, national or international) organizations to share
datasets (and their maintenance costs).
Are there other organizations that uses this kind of system?
Well, there is at least another one right in Winnipeg City:
MyPeg.ca, also developed by
Structured Dynamics. MyPeg uses RDF
to model its information and uses OSF to manage its information.
MyPeg is a non-profit organization that uses census (and other
indicator) data to do studies on the well being of Winnipegers. The
team behind MyPeg.ca are research experts in indicator data. Their
indicator datasets (which includes census data) is top notch.
Let’s hypothetize that there would be interest between the two
groups to start collaborating. Let’s say that the NOW portal would
like to use MyPeg’s census datasets instead of its own since they
are more complete, accurate and include a larger number of
important indicators. What they basically want is to outsource the
creation and maintenance of the census/indicators data to a local,
dedicated and highly professional organization. The only things
they would need to do is to:
Formalize their relationship by signing a usage agreement
The NOW portal would need to configure the MyPeg.ca OSF network
into their OSF for Drupal instance
The NOW portal would need to register the datasets it want to
use from MyPeg.ca.
Once these 3 steps are done, taking no more than a couple of
minutes, then the system administrators of the NOW portal could
start using the MyPeg.ca indicator datasets like they were existing
on their own network. (The reverse could also be true for MyPeg.)
Everything would be transparent to them. From then on, all the
fixes and updates performed by MyPeg.ca to their indicator datasets
would immediately appear on the NOW portal and accessible to its
users.
This is one possibility to collaborate. Another possibility
would be to simply on a routine basis (every month, every 6 months,
every year) share the serialized datasets such that the NOW portal
re-import the dataset from the files shared by MyPeg.ca. This is
also possible since both organizations use the same Ontology
to describe the indicator data. This means that no modification is
required by the City to take that new information into account,
they only have to import and update their local datasets. This is
the beauty of ontologies.
Conclusion
The new NOW portal is a great service for citizens of Winnipeg
City. It is also a really good example of a web portal that
leverages fully structured, integrated and linked data. To me, the
NOW portal is a really good example of the features that should go
along with a municipal data portal.
2016-08-16, DCMI is pleased to announce that Mike Lauruhn has
accepted an appointment to the DCMI Governing
Board as an Independent Member. Mike is Technology Research
Director at Elsevier Labs
and has been a longstanding participant in DCMI, serving as a
member of the Education and Outreach Committee's "Linked Data for
Professional Education (LD4PE) initiative" and as co-Program Chair
for the DCMI annual conference in 2010. Before joining Elsevier
Labs in 2010, he was a consultant with Taxonomy Strategies LLC
working with numerous private companies, nonprofits, and government
agencies to help define and implement taxonomies and metadata
schemas. He began his library career cataloging at the California
Newspaper Project at the Center for Bibliographic Studies &
Research at the University of California, Riverside. Mike's three
year term will begin at the close of DC-2016 in Copenhagen. For
additional information, visit the Governing Board page at http://dublincore.org/about/oversight/#lauruhn.
The
12th edition of the
SEMANTiCS, which is a well known platform for professionals and
researchers who make semantic computing work, will be held in the
city of Leipzig from September 12th till 15th. We are proud to
announce the final program of the SEMANTiCS conference. The
program will cover 6 keynote speakers, 40 industry
presentations, 30 scientific paper presentations, 40 poster &
demo presentations and a huge number of satellite events.
Special talks will given by Thomas Vavra from IDC and Sören Auer, who will
feature the LEDS track.
On top of that there will be a fishbowl session ‘Knowledge Graphs –
A Status Update’ with lightning talks from Hans Uszkoreit (DFKI)
and Andreas Blumenauer (SWC). This week, the set of our
distinguished keynote speakers
has been fixed and we are quite excited to have them at this years’
edition of SEMANTiCS. Please join us to listen to talks from
representatives from IBM, Siemens, Springer Nature, Wikidata,
International Data Corporation (IDC), Fraunhofer IAIS, Oxford
University Press and the Hasso-Plattner-Institut, who will share
their latest insights on applications of Semantic technologies with
us. To register and be part of the SEMANTiCS 2016 in Leipzig,
please go to: http://2016.semantics.cc/registration.
Share your ideas, tools and ontologies, last minute
submissions Meetup: Big Data & Linked Data – The Best of Both
Worlds
On the first eve of the SEMANTiCS conference we will
discuss how Big Data & Linked Data technologies could become a
perfect match. This meetup gathers experts on Big and Linked Data
to discuss the future agenda on research and implementation of a
joint technology development.
If you are interested to present your idea, approach
or project which links Semantic technologies with Big Data in an
ad-hoc lightning talk, please get in touch with Thomas Thurner
(t.thurner@semantic-web.at).
WORKSHOPS/TUTORIALS
This year’s SEMANTiCS is starting on September 12th
with a full day of exciting and interesting satellite events. In
6 parallel
tracks scientific and industrial workshops and tutorials are
scheduled to provide a forum for groups of researchers and
practitioners to discuss and learn about hot topics in Semantic Web
research.
How to find users and feedback for your
vocabulary or ontology?
The Vocabulary Carnival is a unique opportunity for
vocabulary publishers to showcase and share their work in form of a
poster and a short presentation, meet the growing community of
vocabulary publishers and users to build useful semantic, technical
and social links. You can join the Carnival Minute Madness on the
13th of September.
The European
Linked Data Contest awards prizes to stories, products,
projects or persons presenting novel and innovative projects,
products and industry implementations involving linked data. The
ELDC is more than yet another competition. We envisage to build a
directory of the best European projects in the domain of Linked
Data and the Semantic Web. This year the ELDC is awarded in the
categories Linked Enterprise Data and Linked Open Data, with
€1.500,- for each of the winners. Submission deadline is August 31,
2016.
Co-located with SEMANTiCS, the next DBpedia meeting
will be held at Leipzig on September 15th. Experts will speak about
topics such as Wikidata: bringing structured data to Wikipedia with
16.000 volunteers. The 7th edition of this event covers a DBpedia
showcase session, breakout sessions and a DBpedia Association
meeting where we will discuss new strategies and which direction is
important for DBpedia. If you like to become part of the DBpedia
community and present your ideas, please
submit your proposal or check our meeting website: http://wiki.dbpedia.org/meetings/Leipzig2016
Sponsorship opportunities
We would be delighted to welcome new sponsors for
SEMANTiCS 2016. You will find a number of sponsorship packages with
an indication of benefits and prices here: http://semantics.cc/sponsorship-packages.
Special offer: You can buy a special SEMANTiCS
industry ticket for €400 which includes a poster presentation at
our marketplace. So take the opportunity to increase the visibility
of your company, organisation or project among an international and
high impact community. If you are interested, please contact us via
email to semantics2016@fu-confirm.de.
Things, not
Strings Entity-centric views on enterprise information and
all kinds of data sources provide means to get a more meaningful
picture about all sorts of business objects. This method of
information processing is as relevant to customers, citizens, or
patients as it is to knowledge workers like lawyers, doctors, or
researchers. People actually do not search for documents, but
rather for facts and other chunks of information to bundle them up
to provide answers to concrete questions.
Strings, or names for things are not the same as the things they
refer to. Still, those two aspects of an entity get mixed up
regularly to nurture the Babylonian language confusion. Any search
term can refer to different things, therefore also Google has
rolled out its own knowledge graph to help organizing information
on the web at a large scale.
Semantic graphs can build the backbone of any information
architecture, not only on the web. They can enable entity-centric
views also on enterprise information and data. Such graphs of
things contain information about business objects (such as
products, suppliers, employees, locations, research topics, …),
their different names, and relations to each other. Information
about entities can be found in structured (relational databases),
semi-structured (XML), and unstructured (text) data objects.
Nevertheless, people are not interested in containers but in
entities themselves, so they need to be extracted and organized in
a reasonable way.
Machines and algorithms make use of semantic graphs to retrieve
not only simply the objects themselves but also the relations that
can be found between the business objects, even if they are not
explicitly stated. As a result, ‘knowledge lenses’ are delivered
that help users to better understand the underlying meaning of
business objects when put into a specific context.
Personalization of information The ability to take a
view on entities or business objects in different ways when put
into various contexts is key for many knowledge workers. For
example, drugs have regulatory aspects, a therapeutical character,
and some other meaning to product managers or sales people. One can
benefit quickly when only confronted with those aspects of an
entity that are really relevant in a given situation. This rather
personalized information processing has heavy demand for a semantic
layer on top of the data layer, especially when information is
stored in various forms and when scattered around different
repositories.
Understanding and modelling the meaning of content assets and of
interest profiles of users are based on the very same methodology.
In both cases, semantic graphs are used, and also the linking of
various types of business objects works the same way.
Recommender engines based on semantic graphs can link similar
contents or documents that are related to each other in a highly
precise manner. The same algorithms help to link users to content
assets or products. This approach is the basis for ‘push-services’
that try to ‘understand’ users’ needs in a highly sophisticated
way.
‘Not only MetaData’ Architecture Together with the
data and content layer and its corresponding metadata, this
approach unfolds into a four-layered information architecture as
depicted here.
Following the NoSQL paradigm, which is about
‘Not only SQL’, one could call this content
architecture ‘Not only Metadata’, thus
‘NoMeDa’ architecture. It stresses the importance of the
semantic layer on top of all kinds of data. Semantics is no longer
buried in data silos but rather linked to the metadata of the
underlying data assets. Therefore it helps to ‘harmonize’ different
metadata schemes and various vocabularies. It makes the semantics
of metadata, and of data in general, explicitly available. While
metadata most often is stored per data source, and
therefore not linked to each other, the semantic layer is no longer
embedded in databases. It reflects the common sense of a certain
domain and through its graph-like structure it can serve directly
to fulfill several complex tasks in information management:
Knowledge discovery, search and analytics
Information and data linking
Recommendation and personalization of information
Data visualization
Graph-based Data Modelling Graph-based semantic
models resemble the way how human beings tend to build their
own models of the world. Any person, not only subject matter
experts, organize information by at least the following six
principles:
Draw a distinction between all kinds of things: ‘This thing is
not that thing’
Give things names: ‘This thing is my dog Goofy’ (some
might call it Dippy Dawg, but it’s still the same
thing)
Categorize things: ‘This thing is a dog but not a
cat’
Create general facts and relate categories to each other: ‘Dogs
don’t like cats’
Create specific facts and relate things to each other: ‘Goofy
is a friend of Donald’, ‘Donald is the uncle of
Huey, Dewey, and Louie’, etc.
Use various languages for this; e.g. the above mentioned fact
in German is ‘Donald ist der Onkel von Tick, Trick und Track’
(remember: the thing called ‘Huey’ is the same thing as the thing
called ‘Tick’ – it’s just that the name or label for this
thing that is different in different languages).
These fundamental principles for the organization of information
are well reflected by semantic knowledge graphs. The same
information could be stored as XML, or in a relational database,
but it’s more efficient to use graph databases instead for the
following reasons:
The way people think fits well with information that is
modelled and stored when using graphs; little or no translation is
necessary.
Graphs serve as a universal meta-language to link information
from structured and unstructured data.
Graphs open up doors to a better aligned data management
throughout larger organizations.
Graph-based semantic models can also be understood by subject
matter experts, who are actually the experts in a certain
domain.
The search capabilities provided by graphs let you find out
unknown linkages or even non-obvious patterns to give you new
insights into your data.
For semantic graph databases, there is a standardized query
language called SPARQL that allows you to explore data.
In contrast to traditional ways to query databases where
knowledge about the database schema/content is necessary, SPARQL
allows you to ask “tell me what is there”.
Standards-based Semantics Making the semantics of
data and metadata explicit is even more powerful when based on
standards. A framework for this purpose has evolved over the past
15 years at W3C, the World Wide Web Consortium. Initially designed
to be used on the World Wide Web, many enterprises have been
adopting this stack of standards for Enterprise Information
Management. They now benefit from being able to integrate and link
data from internal and external sources with relatively low
costs.
At the base of all those standards, the Resource Description
Framework (RDF) serves as a ‘lingua franca’ to express all kinds of
facts that can involve virtually any kind of category or entity,
and also all kinds of relations. RDF can be used to describe the
semantics of unstructured text, XML documents, or even relational
databases. The Simple Knowledge Organization System (SKOS) is based
on RDF. SKOS is widely used to describe taxonomies and other types
of controlled vocabularies. SPARQL can be used to traverse and make
queries over graphs based on RDF or standard schemes like SKOS.
With SPARQL, far more complex queries can be executed than with
most other database query languages. For instance, hierarchies can
be traversed and aggregated recursively: a geographical taxonomy
can then be used to find all documents containing places in a
certain region although the region itself is not mentioned
explicitly.
Standards-based semantics also helps to make use of already
existing knowledge graphs. Many government organisations have made
available high-quality taxonomies and semantic graphs by using
semantic web standards. These can be picked up easily to extend
them with own data and specific knowledge.
Semantic Knowledge Graphs will grow with your
needs! Standards-based semantics provide yet another
advantage: it is becoming increasingly simpler to hire skilled
people who have been working with standards like RDF, SKOS or
SPARQL before. Even so, experienced knowledge engineers and data
scientists are a comparatively rare species. Therefore it’s crucial
to grow graphs and modelling skills over time. Starting with SKOS
and extending an enterprise knowledge graph over time by
introducing more schemes and by mapping to other vocabularies and
datasets over time is a well established agile procedure model.
A graph-based semantic layer in enterprises can be expanded
step-by-step, just like any other network. Analogous to a street
network, start first with the main roads, introduce more and more
connecting roads, classify streets, places, and intersections by a
more and more distinguished classification system. It all comes
down to an evolving semantic graph that will serve more and more as
a map of your data, content and knowledge assets.
Semantic Knowledge Graphs and your Content
Architecture It’s a matter of fact that semantics serves
as a kind of glue between unstructured and structured information
and as a foundation layer for data integration efforts. But even
for enterprises dealing mainly with documents and text-based
assets, semantic knowledge graphs will do a great job.
Semantic graphs extend the functionality of a traditional search
index. They don’t simply annotate documents and store occurrences
of terms and phrases, they introduce concept-based indexing in
contrast to term based approaches. Remember: semantics helps to
identify the things behind the strings. The same applies to
concept-based search over content repositories: documents get
linked to the semantic layer, and therefore the knowledge graph can
be used not only for typical retrieval but to classify, aggregate,
filter, and traverse the content of documents.
PoolParty combines Machine Learning with Human
Intelligence
Semantic knowledge graphs have the potential to innovate data
and information management in any organisation. Besides questions
around integrability, it is crucial to develop strategies to create
and sustain the semantic layer efficiently.
Looking at the broad spectrum of semantic technologies that can
be used for this endeavour, they range from manual to fully
automated approaches. The promise to derive high-quality semantic
graphs from documents fully automatically has not been fulfilled to
date. On the other side, handcrafted semantics is error-prone,
incomplete, and too expensive. The best solution often lies in a
combination of different approaches. PoolParty combines Machine
Learning with Human Intelligence: extensive corpus analysis and
corpus learning support taxonomists, knowledge engineers and
subject matter experts with the maintenance and quality assurance
of semantic knowledge graphs and controlled vocabularies. As a
result, enterprise knowledge graphs are more complete, up to date,
and consistently used.
“An Enterprise without a Semantic Layer is like a Country
without a Map.
On the 15th of
August at 3 PM, Michael Röder will present the paper “RDF Query
Relaxation Strategies Based on Failure Causes” of Fokou et al. in
P702.
Abstract
Recent advances in Web-information extraction have led to the
creation of several large Knowledge Bases (KBs). Querying these KBs
often results in empty answers that do not serve the users’ needs.
Relaxation of the failing queries is one of the cooperative
techniques used to retrieve alternative results. Most of the
previous work on RDF query relaxation compute a set of relaxed
queries and execute them in a similarity-based ranking order. Thus,
these approaches relax an RDF query without knowing its failure
causes (FCs). In this paper, we study the idea of identifying these
FCs to speed up the query relaxation process. We propose three
relaxation strategies based on various information levels about the
FCs of the user query and of its relaxed queries as well. A set of
experiments conducted on the LUBM benchmark show the impact of our
proposal in comparison with a state-of-the-art algorithm.
This event is part of a series of events about Semantic Web
technology. Please see http://wiki.aksw.org/Colloquium
for further information about previous and future events. As
always, Bachelor and Master students are able to get points for
attendance and there is complimentary coffee and cake after the
session.
Schema.org 3.1 has
been released! Many
thanks to everyone in the community who has
contributed to this update, which includes substantial new
vocabulary for describing hotels and accommodation,
some improvements around dataset description, as well as the usual
collection of new examples, bugfixes, usability, infrastructural,
standards compatibility and conceptual consistency
improvements.
This release builds upon the recent 3.0 release. In
version 3.0 we created a health-lifesci extension as
a new home for the extensive collection of medical/health terms
that were
introduced back in 2012. Publishers and webmasters do not need
to update their markup for this change, it is best considered an
improvement to the structure of our documentation. Our extension system allows
us to provide deeper coverage of specialist topics without
cluttering the core project pages. Version 3.0 also included some
improvements from
the FIBO
project, improving our representation of various financial
products.
We have also introduced a special extension called "pending", which provides a place
for newly proposed schema.org terms to be documented, tested and
revised. We hope that this will help schema proposals get wider
visibility and review, supporting greater participation from
non-developer collaborators. You should not need to be a computer
programmer to be part of our project, and "pending" is one step
towards making work-in-progress schema proposals more visible
without requiring knowledge of highly technical systems like
GitHub. We
have linked each term in pending.schema.org to the
technical discussions at Github, but also to a simple feedback
form. We anticipate updating the "pending" area relatively
frequently, in between formal releases.
The site also features a new "how we work" document,
oriented towards the Web standards community and toolmakers,
explaining the evolving process we have adopted towards creating
new and improved schemas. See also commentary on this in the UK
government technology blog post about
making job adverts more open with schema.org.
Many people were involved in these updates, but particular thanks
are due to Martin Hepp for leading the hotels/accommodation design,
and to Marc Twagirumukiza for chairing the "schemed" W3C community
group that led the creation of our new health-lifesci
extension.
Finally, we would like to dedicate this release to Peter Mika, who
has served on our steering group since the
early days. Peter has stepped down as Yahoo's representative,
passing his duties to Nicolas Torzec. Thanks, Peter! Welcome,
Nicolas...
For more details on version 3.1 of schema.org, check out the
release
notes
PoolParty Academy
offers three E-Learning tracks that enable customers, partners
and individual professionals to learn Semantic Web
technologies and PoolParty Semantic Suite in particular.
You can pre-register for the PoolParty Academy training tracks
at the academy’s website or join our live class-room at the biggest
European industrial Semantic Web conference – SEMANTiCS 2016.
In this system paper, we describe the DL-Learner framework,
which supports supervised machine learning using OWL and RDF for
background knowledge representation. It can be beneficial in
various data and schema analysis tasks with applications in
different standard machine learning scenarios, e.g. in the
life sciences, as well as Semantic Web specific applications such
as ontology learning and enrichment. Since its creation in 2007, it
has become the main OWL and RDF-based software framework for
supervised structured machine learning and includes several
algorithm implementations, usage examples and has applications
building on top of the framework. The article gives an overview of
the framework with a focus on algorithms and use cases.
2016-08-01, DCMI is pleased to announce the publication of the
Preliminary Program for
DC-2016 at http://dcevents.dublincore.org/IntConf/dc-2016/schedConf/program.
The program includes 28 Full Papers, Project
Reports, and Presentations on Metadata as well as 14
Posters. Six
Special Sessions on significant metadata topics as well as 6
half- and full-day Workshops
round out the program. The keynote will be delivered by Bradley P.
Allen, Chief Architect at Elsevier, the world's leading scientific
publisher. The Final Program including the authors and abstracts of
Papers, Project Reports, Posters, and
Presentations on Metadata will be available on 15 August.
Registration is now open at http://dcevents.dublincore.org/IntConf/index/pages/view/reg16
with an early registration rate available through 2 September
2016.
On Monday 18.07.2016, Kleanthi
Georgala will give her Colloquium presentation for her paper
“An Efficient Approach for the Generation of Allen
Relations”, that was accepted at the European Conference on
Artificial Intelligence (ECAI) 2016.
Abstract
Event data is increasingly being represented according to the
Linked Data principles. The need for large-scale machine learning
on data represented in this format has thus led to the need for
efficient approaches to compute RDF links between resources based
on their temporal properties. Time-efficient approaches for
computing links between RDF resources have been developed over the
last years. However, dedicated approaches for linking resources
based on temporal relations have been paid little attention to. In
this paper, we address this research gap by presenting
AEGLE, a novel approach for the efficient
computation of links between events according to Allen’s interval
algebra. We study Allen’s relations and show that we can reduce all
thirteen relations to eight simpler relations. We then present an
efficient algorithm with a complexity of O(n log n) for computing
these eight relations. Our evaluation of the runtime of our
algorithms shows that we outperform the state of the art by up to 4
orders of magnitude while maintaining a precision and a recall of
1.
Afterwards,
Tommaso
Soru will present a paper considered the latest chapter of the
Everything-2-Vec saga, which encompasses outstanding works such as
Word2Vec and Doc2Vec. The paper title is
“node2vec: Scalable Feature Learning for
Networks” [PDF]
by Aditya Grover and Jure Leskovec, accepted for publication at the
International Conference on Knowledge Discovery and Data Mining
(KDD), 2016 edition.
2016-07-11, The submission deadline of 15 July is rapidly
approaching for the Presentations and Best Practice
Posters and Demos tracks at
DC-2016. Both presentations and posters/demos provide the
opportunity to practitioners and researchers specializing in
metadata design, implementation, and use to present their work at
the International Conference on Dublin Core and Metadata
Applications in Copenhagen. No paper is required for presentations
or posters/demos. Accepted submissions in the Presentations track
will have approximately 20-25 minutes to present and 5-10 minutes
for questions and discussion. Proposal abstracts will be reviewed
for selection by the Program Committee. The presentation slide
decks and the poster images will be openly available as part of the
permanent record of the DC-2016 conference. If you are interested
in presenting at DC-2016, please submit a proposal abstract through
the DC-2016 submission system before the 15 July deadline at
http://dcevents.dublincore.org/index.php/IntConf/dc-2016/schedConf/cfp.
For a fuller description of the Presentations track, see
http://dcevents.dublincore.org/IntConf/index/pages/view/pre16.
Posted at 23:59
Copyright of the postings is owned by the original blog authors. Contact us.
since 2005.
The
On the 15th of
August at 3 PM, Michael Röder will present the paper “RDF Query
Relaxation Strategies Based on Failure Causes” of Fokou et al. in
P702.
PoolParty Academy
offers three E-Learning tracks that enable customers, partners
and individual professionals to learn Semantic Web
technologies and PoolParty Semantic Suite in particular.
On Monday 18.07.2016, 