On Monday, August 18 ,13.30, Edgard Marx, will give a pre-presentation of his Semantics’ conference talk about the accepted paper Towards an Open Question Answering Architecture.About the AKSW Colloquium
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.
The 21st European Conference on Artificial Intelligence (ECAI) will be held in the city of Prague, Czech Republic from 18th to 22nd August 2014. Various excellent papers on artificial intellegence, logic, rule mining and many more topics will be presented.
AKSW member Ricardo Usbeck will present a poster of AGDISTIS – Agnostic Disambiguation of Named Entities using Linked Data. To the best of our knowledge AGDISTIS is able to outperform the state-of-the-art approaches in entity linking by up to 29% f-meassure. Come and visit him at ECAI 2014.
I will first present the formulas that have been used to perform the UMBEL analysis. To better understand this section, you will need some math knowledge, but there is nothing daunting here. You can probably safely skip that section if you desire.
The second section is the actual analysis of a Big Structure (UMBEL) using the graph measures I presented in the initial section. The results will be presented, analyzed and explained.
After reading this blog post, you should better understand how graph and network analysis techniques can be used to understand, use and leverage Big Structures to help integrate and interoperate disparate data sources.Big Structure: Big in Size
A Big Structure is a network (a graph) of inter-related concepts which is composed of thousands or even hundred of thousands of such concepts. One characteristic of a Big Structure is its size. By nature, a Big Structure is too big to manipulate by hand and it requires tools and techniques to understand and assess the nature and the quality of the structure. It is for this reason that we have to leverage graph and network measures to help us in manipulating these Big Structures.
In the case of UMBEL, the Big Structure is a scaffolding of reference concepts used to link external (unrelated) structures to help data integration and to help unrelated systems inter-operate. In a World where the Internet Of Things is the focus of big companies and where there are more than 400 standards, such techniques and technologies are increasingly important, otherwise it will end-up being the Internet of [Individual] Things. Such a Big Structure can also be used for other tasks such as helping machine learning techniques to categorize and disambiguate pieces of data by leveraging such a structure of types.UMBEL as a Graph
UMBEL is an RDF and OWL ontology of a bit more than 26 000 reference concepts. Because the structure is represented using RDF, it means that it is a directed graph. All of UMBEL’s vertices are classes, and all of the edges are properties.
The most important fact to keep in mind until the end of this blog post is that we are manipulating a directed graph. This means that all of the formulas used to analyze the UMBEL graph are formulas applicable to directed graphs only.
I will keep the normal graph analysis language that we use in the literature, however keep in mind that a vertice is a class or a named individual and that a edge is a property.
The UMBEL structure we are using is composed of the classes view and the individuals view of the ontology. That means that all the concepts are there, where some of them only have a class view, others have an individual view and others have both (because they got punned).Graph Analysis Metrics
In this section, I will present the graph measures that we will use to perform the initial UMBEL graph analysis. In the next section, we will make the analysis of UMBEL using these measures, and we will discuss the results.
This section uses math notations. It could be skipped, but I suggest to try to take time some time to understand each measure since it will help to understand the analysis.Some Notations
In this blog post, a graph is represented as where is the graph, is the set of all the vertices and is the set of all the edges of the same type that relates vertices.
The UMBEL analysis focuses on one of the following transitive properties:Â rdfs:subClassOf,Â umbel:superClassOf,Â skos:broaderTransitive, skos:narrowerTransitive and rdf:type. When we do perform the analysis, we are picking-up a subgraph that is composed of all the connections between the vertices that are linked by thisÂ edge (property).Density
The first basic measure is the density of a graph. The density measures how many edges are in set compared to the maximum possible number of edges between vertices in set . The density is measured with:
where is the density, is the number of properties (edges) and is the number of classes (vertices).
The density is a ratio of the number of edges that exists, and the number of edges that could exists in the graph. is the number of edges in the graph and is the number of possible maximum number of edges.
The maximum density is 1, and the minimum density is 0. The density of a graph gives us an idea about the number of connections that exists between the vertices.Average Degree
The degree of a vertex is the number of edges that connect that vertex to other vertices. The average degree of a graph is another measure of how many edges are in set compared to number of vertices in set .
where is the average degree, is the number of properties (edges) and is the number of classes (vertices).
This measure tells the average number of nodes to which any given node is connected.Diameter
The diameter of a graph is the longest shortest path between two vertices in the graph. This means that this is the longest path that excludes all detours, loops, etc. between two vertices.
Let be the length of the shortest path between and . And . The diameter of the graph is defined as:
This metric gives us an assessment of the size of the graph. It is useful to understand the kind of graph we are playing with. We will also relate it with the average path length to assess the span of the graph and the distribution of path lengths.Average Path Length
The average path length is the average of the shortest path length, averaged over all pairs of vertices. Let be the length of the shortest path between and . And .
where, is the number of vertices in the graph ; where, is the number of pairs of distinct vertices. Note that the number of pairs of distinct vertices is equal to the number of shortest paths between all pairs of vertices if we pick just one in case of a tie (two shortest paths with the same length).
In the context of ontology analysis, I would compare this metric as the speed of the ontology. What I mean by that is that one of the main tasks we do with an ontology is to infer new facts from known facts. Many inferring activities requires traversing the graph of an ontology. This means that the smaller the average path length between two classes, the more performant these inferencing activities should be.Average Local Clustering Coefficient
The local clustering coefficient quantifies how well connected the neighborhood vertices of a given vertex are. It is the ratio of the edges that exists between all of the neighborhood vertices of a given vertex and the maximum number of possible edges between these same neighborhood vertices.
where is the number of neighborhood vertices, is the maximum number of edges between the neighborhood vertices and is the set of all the neighborhood vertices for a given vertex .
The local clustering coefficient is represented by the sum of the clustering coefficient of all the vertices of a graph divided by the number of vertices in . It is given by:
Betweenness centrality is a measure of importance of a node in a graph. It is represented by the number of times a node participates in the shortest path between other nodes. If a node participates in the shortest path of multiple other nodes, then it means that it is more important than other nodes in the graph. It acts like a conduit.
where is the total number of shortest paths from node to node and is the number of those paths that pass through .
In the context of ontology analysis, the betweenness centrality will tell us which of the classes that participates the more often in the shortest paths of a given transitive property between other classes. This measure is interesting to help us understand how a subgraph is constructed. For example, if we take a transitive property such as rdfs:subClassOf, then the graphs generated by a subgraph composed of this relationship only should be more hierarchic by the semantic nature of the property. This means that the nodes (classes) with the highest betweenness centrality value should be classes that participate in the upper portion of the ontology (the more general classes). However, if we think about the foaf:knows transitive property between named individuals, then the results should be quite different and suggest a different kind of graph.Initial UMBEL Graph Analysis
Now that we have a good understanding of some core graph analysis measures, we will use them to analyze the graph of relationship between the UMBEL classes and reference concepts using the subgraphs generated by the properties: rdfs:subClassOf, umbel:superClassOf, skos:broaderTransitive, skos:narrowerTransitive and rdf:type.Density
The maximum number of edges in UMBEL is: which is about two thirds of a billion of edges. This is quite a lot of edges, but it is important to keep in mind since most of the following ratios are based on this maximum number of edges (connections) between the nodes of the UMBEL graph.
Here is the table that shows the density of each subgraph generated by each property:Class view Individual view Metric sub class of super class of broader narrower type Number of edges 39 410 116 792 36 016 36 322 271 810 Density 0.0000567 0.0001402 0.0000518 0.0000523 0.0001021
As you can see, the density of any of the UMBEL subgraphs is really low considering that the maximum density of the graph is 1. However this gives us a picture of the structure: Most of the concepts have no more than few connections between each node for any of the analyzed properties.
This makes sense, since a conceptual structure is meant to model relationships between concepts that represent concepts of the real world, and in the real world, the concepts that we created are far from being connected to every other concept.
This is actually what we want: we want a conceptual structure which has a really low density. This suggest that the concepts are unambiguously related and hierarchized.
Having a high density (let’s say, 0.01, which would mean that there are nearly 7 million connections between the 26 345 concepts for a given property) may suggest that the concepts are too highly connected which could suggest that using the UMBEL ontology for tagging, classifying and reasoning over its concepts won’t be an optimal choice because of the nature of the structure and its connectivity (and possible lack of hierarchy).Average Degree
The average degree shows the average number of UMBEL nodes that are connected to any other node for one of the given property.Class view Individual view Metric sub class of super class of broader narrower type Number of Edges 39406 97323 36016 36322 70921 Number of Vertices 26345 26345 26345 26345 26345 Average degree 1.4957 3.6941 1.3670 1.3787 2.6920
As you can see, the number are quite low: from 1.36 to 3.69. This is consistent with what we saw with the density measure above. This helps confirm our assumption that all of these properties create mostly hierarchical subgraphs.
However there seems to be one anomaly with these results, the average degree 3.69 of the umbel:superClassOf property. Intuitively, its degree should be near the one of the rdfs:subClassOf but it is far from this: it is more than twice its average degree. Looking at the OWL serialization of the UMBEL version 1.05 reveals that most umbel:RefConcept do have 3 triples:umbel:superClassOf skos:Collection , skos:ConceptScheme , skos:OrderedCollection .
This makes no sense that the umbel:RefConcept are super classes of these skos classes. I suspect that this got introduced via punning at some point in the history of UMBEL and got unnoticed until today. This issue will be fixed in a coming maintenance version of UMBEL.
If we check back the density measure of the graph, we notice that we have a density of 0.0001402 for the umbel:superClassOf property versus 0.0000567 for the rdfs:subClassOf property which has about the same ratio. So we could have noticed the same anomaly by taking a better look at the density measure.
But in any case, this shows how this kind of graph analysis can be used to find such issues in Big Structures (structures too big to find all these issues by scrolling the code only).Diameter
The diameter of the UMBEL graph is like the worse case scenario. It tells us what is the longest shortest path for a given property’s subgraph.Class view Individual view Metric sub class of super class of broader narrower type Diameter 19 19 19 19 4
In this case, this tell us that the longest shortest path between two given nodes for the rdfs:subClassOf property is 19. So in the worse case, if we infer something between these two nodes, then our algorithms will require maximum 19 steps (think in terms of a breath first search, etc).Average Path Length Distribution
The average path length distribution shows us the a number paths that have x in length. Because of the nature of UMBEL and its relationships, I think we should expect a normal distribution. An interesting observation we can do is the the average path length is situated around 6, which is the six degree of separation.
We saw that the “worse case scenario” was a shortest path of 19 for all the property except rdf:type. Now we know that the average is around 6.rdfs:SubClassOf
Here we can notice an anomaly in the expected normal distribution of the path lengths. Considering the other analysis we did, we can consider that the anomaly is related to the umbel:superClassOf issue we found. What we will have to re-check this metric once we fix the issue. I expect we will see a return to a normal distribution.skos:broaderTransitive
Average Local Clustering Coefficient
The average local clustering coefficient will tell us how clustered the UMBEL subgraphs are.Class view Individual view Metric sub class of super class of broader narrower type Average local clustering coefficient 0.0001201 0.00000388 0.03004554 0.00094251 0.77191429
As we can notice, UMBEL does not have the small-world effect with its small clustering coefficients depending on the properties we are looking at. It means that there is not a big number of hubs in the network and so that the number of steps from going from a class or a reference concept to another is higher than in other kinds of networks, like airport networks. This makes sense by looking at the average path length and the path length distributions we observed above.
At the same time, this is the nature of the UMBEL graph: it is meant to be a a well structured set of concepts with multiple different specificity layers.
To understand its nature, we could consider the robustness of the network. Normally, networks with high average clustering coefficients are known to be robust, which means that if a random node is removed, it shouldn’t impact the average clustering coefficient or the average path length of that network. However, in a network that doesn’t have the small-world effect, then they are considered less robust which means that if a node is removed, it could greatly impact the clustering coefficients (which would be lower) and the average path length (which would be higher).
This makes sense in such a conceptual network: if we remove a concept from the structure, it will most than likely impact the connectivity of the other concepts of the network.
One interesting thing to notice is the clustering coefficient of 0.03 for the skos:broaderTransitive property and 0.00012 for the rdfs:subClassOf property. I have no explanation for this discrepancy at the moment, but this should be investigated after the fix as well since intuitively these two coefficient should be close.Betweenness Centrality
As I said above, In the context of ontology analysis, the betweenness centrality tells us which of the classes participates more often in the shortest paths of a given transitive property between other classes. This measure is useful to help us understand how a subgraph is constructed.
If we check the results below, we can see that all the top nodes are nodes that we could easily classify as being part of the upper portion of the UMBEL ontology (the general concepts). Another interesting thing to notice is that the issue we found with the umbel:superClassOf property doesn’t seem to have any impact on the betweenness centrality of this subgraph.rdfs:subClassOf PartiallyTangible 0.1853398395065167 HomoSapiens 0.1184076250864128 EnduringThing_Localized 0.1081317879905902 SpatialThing_Localized 0.092787668995485 HomoGenus 0.07956810084399618
Now, let’s push the analysis further. Remember that I mentioned that all the properties we are analyzing in this blog post are transitive? Let’s perform exactly the same metrics analysis, but this time we will use the transitive closure of the subgraphs.
The transitivity characteristic of a property is simple to understand. Let’s consider this tiny graph where the property is a transitive property. Since is transitive, there is also a relationship .
Given we inferred using the transitive relation .
Now, let’s use the power of these transitive properties and let’s analyze the transitive closure of the subgraphs that we are using to compute the metrics. The transitive closure is simple to understand. From the input subgraph, we are generating a new graph where all these transitive relations are explicit.
Let’s illustrate that using this small graph: . The transitive clojure would create a new graph:
This is exactly what we will be doing with the sub-graphs created by the properties we are analyzing in this blog post. The end result is that we will be analyzing a graph with many more edges than we previously had with the non transitive closure versions of the subgraphs.
What we will analyze now is the impact of considering the transitive closure upon the ontology metrics analysis.Density
Remember that the maximum number of edges in UMBEL is: , which is about two thirds of a billion edges.Class view Individual view Metric sub class of super class of broader narrower type Number of Edges 789 814 922 328 674 061 661 629 76 074 Density 0.0011380 0.0013289 0.0009712 0.0009533 0.0001096
As we can see, we have many more edges now with the transitive closure. The density of the graph is higher as well since we inferred new relationships between nodes from the transitive nature of the properties. However it is still low considering the number of possible edges between all nodes of the UMBEL graph.Average Degree
We now see the impact of transitive closure on the average degree of the subgraphs. Now each node of the subgraphs are connected by 25 to 35 other nodes in average.Class view Individual view Metric sub class of super class of broader narrower type Number of Edges 789 814 922 328 674 061 661 629 76 074 Number of Vertices 26345 26345 26345 26345 26345 Average degree 29.97965 35.00960 25.58591 25.11402 2.887606
One interesting fact is that the anomaly disappears with this transitive closure subgraph for the umbel:superClassOf property. There is still a glitch with it, but I don’t think it would raise suspicion at first. This is important to note since we won’t have noticed this issue with the current version of the UMBEL ontology if we would have analyzed the transitive closure of the subgraph only.Diameter Class view Individual view Metric sub class of super class of broader narrower type Diameter 2 2 2 2 2
As expected, the diameter of any of the transitive closure subgraphs is 2. It is the case since we made explicit a fact (a edge) between two nodes that was not explicit at first. This is good, but this is no quite useful from the perspective of ontology analysis.
This would only be helpful if the number were not 2 which would suggest some errors in the way you computed the diameter of the graph.
However what we can see here is that the speed of the ontology (as defined in the Average Path Length section above) is greatly improved. Since we forward-chained the facts in the transitive closure sub-graphs, it means that knowing if a class A is a sub-class of a class B is much faster, since we have a single lookup to do instead of an average of 6 for the non transitive closure version of the subgraphs.Average Path Length Distribution
All of the path length distributions
will be the same as this one:
Some of the properties like the rdfs:subClassOf property shows a much stronger coefficient than with the non-transitive closure version. This is normal since all the nodes are connected to the other nodes down the paths. So, if a node in between disappears, then it won’t affect the connectivity of the subgraph since all the linkage that got inferred still remains.
This analysis also suggests that the transitive closure version of the subgraphs are much stronger (which makes sense too).
However I don’t think this metric is that important of a characteristic to check when we analyze reference ontologies since they do not need to be robust. They are not airport or telephonic networks that need to cope with disappearing nodes in the network.Betweenness Centrality
What the betweenness centrality measure does with the transitive closure of the subgraphs is that it highlight the real top concepts of the ontology like Thing, Class, Individual, etc. Like most of the other measures, it blurs the details of the structure (which is not necessarily a good thing).rdfs:subClassOf SuperType 0.03317996389023238 AbstractLevel 0.02810408526564482 Thing 0.02772171675862925 Individual 0.02747482318621853 TopicsCategories 0.02638342698407473
The interesting thing here is that this measure actual shows the actual concepts for which we discovered an issue with above.skos:Concept 0.02849203320293865 owl:Thing 0.02849094898994718 SuperType 0.02848878056396423 skos:ConceptScheme 0.02825567477079737 skos:OrderedCollection 0.02825567477079737
This blog post shows that simple graph analysis metrics applied to Big Structures can be quite helpful to understand their nature, how they have been constructed, what is their size, their impact on some algorithms that could use them, and to find potential issues in the structure.
One thing we found is that the correlation between the properties rdfs:subClassOf and skos:broaderTransitive are nearly identical. They nearly have the same values for each metrics. If you were new to the UMBEL ontology you wouldn’t have known this fact without doing this kind of analysis or by spending much time looking at the serialized OWL file. It doesn’t tell us anything about how similar the relations are, but it does tell us that they have the same impact on the ontology’s graph structure.
Performing this analysis also led us to discover a few anomalies with the umbel:superClassOf property, suggesting an issue with the current version of the ontology. This issue would have been hard to notice, and understand, without performing such a graph analysis to the structure.
However, I also had the intuition that the analysis of the transitive closure of the subgraphs would have led to more interesting results. At best that analysis did confirm a few things, but in most of the cases it only blurred the specificities of most of the metrics.
These analysis metrics will soon be made available as standard Web services, so that they may be applied against any arbitrary graph or ontology.
- Support new RDF 1.1 Turtle.
- Don’t write xsd:decimal literals to Turtle bare if they would not be read back with the same type.
- Fix possible crash in serd_writer_end_anon() when writing invalid lists.
- Generate blank names like _:b1 and _:B2 not _:genid1 _:docid2.
- Correctly handle posix_memalign failure.
- Fix const-correctness violation for reader input string.
- Add -lm to pkg-config libs.
- Update to waf 1.7.16.
- Towards Question Answering on Statistical Linked Data ( Konrad Höffner and Jens Lehmann)
Second, there is also another excellent and interesting series of workshops.Date Title Hosts Room Room Website ________________________________ ____________ 09:00 – 12:30 14:00 – 17:30 01.09.2014 Link Discovery of the Web of Data (Organized by GeoKnow & LinkingLOD) Axel Ngonga (Uni Leipzig) yes - Website 01.09.2014 GeoLD – Geospatial Linked Data (organised by the GeoKnow Project) Jens Lehmann (Uni Leipzig)
Daniel Hladky (Ontos)
Andreas Both (Unister)yes yes Website 01.09.2014 MLODE 2014 – Content Analysis and the Semantic Web, a LIDER Hackathon Bettina Klimek (Uni Leipzig), Philipp Cimiano (Uni Bielefeld) yes yes Website 02.09.2014 MLODE 2014 – Mulitlingual Linked Open Data for Enterprises, LIDER Roadmapping workshop Bettina Klimek (Uni Leipzig), Philipp Cimiano (Uni Bielefeld) yes yes Website 02.09.2014 MLODE 2014 – Community meetings and break-out sessions Bettina Klimek (Uni Leipzig), Philipp Cimiano (Uni Bielefeld) - yes Website Visit us from on the 1st to the 5th September in Leipzig, Germany and enjoy the talks. More information on these publications at http://aksw.org/Publications. Cheers, Ricardo on behalf of AKSW
- A distributed search framework for full-text, geo-spatial and semantic search (Andreas Both, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck, Christiane Lemke, Denis Lukovnikov and Maximilian Speicher)
- LD Viewer – Linked Data Presentation Framework (Denis Lukovnikov, Claus Stadler and Jens Lehmann)
- A Comparison of Supervised Learning Classifiers for Link Discovery (Tommaso Soru and Axel-Cyrille Ngonga Ngomo)
- Towards an Open Question Answering Architecture (Edgard Marx, Ricardo Usbeck, Axel-Cyrille Ngonga Ngomo, Konrad Höffner, Jens Lehmann and Sören Auer)
- DataID: Towards Semantically Rich Metadata For Complex Datasets (Martin Brümmer, Ciro Baron, Ivan Ermilov, Markus Freudenberg and Sebastian Hellmann)
Additionally, there are also interessting workshops. For example, Link Discovery of the Web of Data (Organized by GeoKnow & LinkingLOD) organized by Axel Ngonga-Ngomo.Visit us from on the 4th and 5th September in Leipzig, Germany and enjoy the talks. More information on these publications at http://aksw.org/Publications. Cheers, Ricardo on behalf of AKSW
The Shortest Path Web service is used to get the shortest path between two UMBEL reference concepts by following the path of a transitive property. The concepts that belong to that path will be returned by the server.
This web service is similar to the degree web service endpoint but the actual path is shown. This web service is (marginally more useful) than degree. So if you don’t need to know the actual concepts that participate in the shortest path between two concepts, then you should be using the degree web service endpoint instead.
The graph created by the UMBEL reference concepts ontology is a mostly an directed acyclic graph (DAG). This means that a given pair of concepts is not necessarily linked via all the properties. In these cases, the shortest path returns an error message rather than the path concepts.Intended Users
This new web service endpoint is intended for users that want to perform graph/network analysis tasks on the UMBEL web service endpoint.The Web Service Endpoint
This endpoint will return a vector (so the order of the results is important) of concepts that participate into the shortest path. For each concept, its URI and preferred label are returned.The Online Tool
We also provide an online shortest path tool that people can use to experience interacting with the web service.
The user first needs to select the two concepts for which he wants to find the shortest path between the two. Then he has to select the transitive property he want to use to find the path.
Once the user clicks the Get Shortest Path button, he will get list of concepts, and the order, that compose the path.
If no path exists between the two concepts for the selected property, an error message is displayed to the user.
Tagging Web Documents with the UMBEL Taggers
Another improvement included with this release is the enhancement of the UMBEL taggers12. It is now possible to tag any document accessible on the Web. The only thing you have to do is to provide a URL where the tagger will find the document to download and tag.
The user interface for the taggers also was modified to expose this new functionality. You now have the choice to give a text or a URL as input to the endpoints:
In middle July 2014 I attended the DCO summer school at Big Sky Resort, MT, with a 2-day field trip at Yellowstone National Park (YNP) – a nice experience – the venue is wonderful, and also the topics covered by the curriculum. But what impressed me the most is to see how the Web brings changes to geoscience works as well as geoscientists.
We have three excellent field trip guides, Lisa Morgan, Pat Shanks and Bill Inskeep. They prepared and distributed a 82-page YNP field trip guide! Of course they first shared it online through Dropbox. What also impressed me is that when I showed my golden spike information portal to Lisa, she also showed me a few APPs on her iPhone with state geologic map services – useful gadget for field work. But our field trip experience in YNP showed that a paper map is still necessary as it is bigger and provides a overview of a wider area, and it needs no battery.
The YNP itself has a virtual observatory website called Yellowstone Volcano Observatory, hosted by USGS and University of Utah. The portal provides “timely monitoring and hazard assessment of volcanic, hydrothermal, and earthquake activity in the Yellowstone Plateau region.” Featured information includes publications, online mapping services, and also images, videos and webcams about YNP.
I was happy to see that Katie Pratt and I are accompanied by many other summer school participants when we were tweeting on Twitter. Search the hashtag #DCOSS14 you will find how active the participants were on Twitter during the period of the summer school. I was even a little surprise to see that Donato Giovannelli @d_giovannelli helped answer a question about twitter impact on citation by pasting the link to a paper, a few seconds after I gave a short introduction to the Altmetric.com and its use in Nature Publishing Group, Springer and Wiley.
And my role at the summer school was two-fold: participant and lecturer. I gave a presentation titled ‘Why data science matters and what we can do with it‘, in which I addressed four sub-topics: data management and publication, interoperability of data, provenance of research, and era of Science 2.0. The slides are accessible on Slidershare [link].
Last week, a tweet from
ApacheCon brings together the open source community to learn about and collaborate on the technologies and projects driving the future of open source, big data and cloud computing. Apache projects have and continue to be hugely influential in the innovation and development of software development across a plethora of categories from content, databases and servers, to big data, cloud, mobile and virtual machine.
The developers, programmers, committers and users driving this innovation and utilising these tools will meet in Budapest on November 17-19, for collaboration, education and community building.
In the last years Linked Data has become an important topic in the Apache Software Foundation, with projects such as Jena, Marmotta, Stanbol, Clerezza and Any23. Redlink supports the event by co-chairing a dedicated track about Linked Data. The track aims to be a place where all these projects can meet to explore synergies across the different projects and developers. It is also particularly interesting for us to connect with other data-intensive projects to discuss their approaches with Semantic Web technologies.
- Alessandro Benedetti will talk about Multi Language Content Discovery Through Entity Driven Search that Zaizi uses on Sensefy. Tuesday, November 18, 4:50pm – 5:40pm.
- Thomas Kurz will present how Manage Linked Media With Apache Marmotta. Wednesday, November 19, 10:40am – 11:30am.
- Rupert Westenthaler will introduce the Open Annotation Support For Apache Stanbol. Wednesday, November 19, 2:00pm – 2:50pm.
- Andy Seaborne (Apache Jena) and myself will be discussing the path Towards a Commons RDF Library. Wednesday, November 19, 4:00pm – 4:50pm.
Looking forward to meet you in Budapest this coming November!
Press release, Salzburg, Austria – July 24, 2014
Redlink is now a supporter member of the Open Data Institute. As a an innovative startup in the enterprise linked data sector, Redlink brings the value of semantic processing and linked data services built on free and open-source software and delivered as a platform-as-a-service to a wider audience of developers, public institutions and IT integrators. This membership represents for Redlink a major step in promoting open data culture in Europe and an integral part of our ongoing work as technology enablers.
Founded by Sir Tim Berners-Lee and Professor Sir Nigel Shadbolt, and opened in December 2012, the ODI is an independent,
non-profit, non-partisan, limited by guarantee company. With a 5,000 sq ft convening space in the heart of London’s thriving Shoreditch area, and a global remit, ODI work to catalyse an open data culture to create economic, environmental, and social value. The ODI helps unlock supply, generates demand, creates and disseminates knowledge to address local and global issues.
Gavin Starks, ODI CEO: “In joining the ODI, Redlink is showing leadership in its sector, recognising the social, economic and environmental potential of open data. More than 70 pioneering member companies have now joined the ODI to deliver new products and services and create value for business, and society”.
Redlink was born in March 2013 from the core committers of Apache Marmotta and Apache Stanbol to democratise semantic technologies and to help organisations take full advantage of linked data made publicly available by governments for structuring any form of unstructured data.
John Pereira, Redlink CEO: “We have come a long way in understanding the importance of freeing data from legacy and proprietary formats, the results are clear with the many initiatives and available open datasets. Now we need to demonstrate the business value. At Redlink our contribution is to simplify the use of semantic processing and linked data technology to power the new generation of exciting linked data driven applications”
Redlink GmbH (http://redlink.co), headquartered in Austria, helps enterprises make sense of their data by semantically enriching, linking and searching the vast amounts of unstructured data. Redlink is the company behind the open source projects Apache Stanbol and Apache Marmotta, and is committed to the wide adoption of open source semantic technologies to support a broad set of mission-critical and real-time production uses.
+43 660 277 1228
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The ODI Communications Team
On Monday, July 28, in room P702 at 3.00 p.m., Edgard Marx proposes a question answering system. He has a computer science background (BSc. and MSc. in Computer Science/PUC-Rio) and is a member of AKSW (Agile Knowledge Engineering and Semantic Web). Edgard has been engaging in Semantic Web technology research since 2010 and is mainly working on evangelization and developing of conversion and mapping tools.Abstract
The use of Semantic Web technologies led to an increasing number
of structured data published on the Web.
Despite the advances on question answering systems retrieving and presenting the desired information from RDF structured sources is still substantial challenging.
In this talk we will present our proposal and working draft to address this challenges.
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.
Mike Bergman just published the second part1 of his series of blog posts that summarize the evolution of the Semantic Web in the last decade, and how our experience of the last 7 years of research in that field has led to these observations.
The second part of that series is: Big Structure: At The Nexus of Knowledge Bases, the Semantic Web and Artificial Intelligence.
He continues to outline some issues with the Semantic Web, but more importantly how it fits in a much broader ecosystem, namely KBAI (Knowledge Based AI). He explains the difference between data integration and data interoperability and how these problems could benefit leveraging a sub-set of the Artificial Intelligence domain related to data interoperability:
These two blog posts set the foundation and the direction where Structured Dynamics is heading in the coming years and where we will focus our research projects and how we will help our clients with their data integration and interoperability issues.
We welcome hearing from you!