The CausalityWorkbench project provides an environment to test causal discovery algorithms. Via a web portal, we provide a number of resources, including a repository of datasets, models, and software packages, and a virtual laboratory allowing users to benchmark causal discovery algorithms by performing virtual experiments to study artificial causal systems. We regularly organize competitions. In this paper, we explore the opportunities offered by development applications.

With the advent of RDFa and the at least partial support by major search engines, semantically structured data is more and more appearing on the Web. To enable high value use cases, links between entity descriptions need to be established. The linked data model suggests that links should be state explicitly by those who expose entity descriptions, but unlike on the normal web, incentives for doing so are unclear so that the model ultimately seems to fail in practice. In this position paper, we make the conjecture that explicit links are not needed for realizing the semantic web. We propose discuss how Record Linkage techniques are in general very well suited for the task but argue the need for a tool would allow data publishers to have an active role in producing entity descriptions that can then be linked automatically.

Research effort, with its emphasis on evaluation and measurable progress, things began to change. Instead SHRDLU (WIN72) is perhaps the canonical example. of systems whose architecture and vocabulary were The rapid growth of efforts to found the next generation of based on linguistic theory (in this case acoustic phonetics), systems on general-purpose knowledge representation languages new approaches based on statistical modelling and Bayesian (I'm thinking of several varieties of semantic nets, probability emerged and quickly spread. "Every time I fire a from plain to partitioned, as well as KRL, KL-ONE and linguist my system's performance improves" (Fred Jellinek, their successors, ending (not yet, of course) with CYC (See head of speech recognition at IBM, c. 1980, latterly repudiated (BRA08) for all these) stumbled to a halt once their failure by Fred but widely attested). As advanced from resolution theorem provers through a number more and more problems are re-conceived as instances of of stages to the current proliferation of a range of Description the noisy channel model, the empiricist paradigm continually Logic'reasoners'; Whereas in the 1970s and 1980s there grew, so did the need to manage the impact of change and was real energy and optimism at the interface between computational conflict: enter'truth maintenance', subsequently renamed and theoretical linguistics, the overwhelming success'reason maintenance'. While still using some of But outflanking these'normal science' advances of AI, the terminology of linguistic theory, computational linguistics the paradigm shifters were coming up fast on the outside: practioners are increasingly detached from theory itself, over the last ten years machine learning has spread from which has suffered a, perhaps connected, loss of energy and small specialist niches such as speech recognition to become sense of progress.

In this paper, we describe our work on discovering missing links in Wikipedia articles. This task is important for both readers and authors of Wikipedia. The readers will benefit from the increased article quality with better navigation support. On the other hand, the system can be employed to support the authors during editing. This study combines the strengths of different approaches previously applied for the task, and adds its own techniques to reach satisfactory results. Because of the subjectivity in the nature of the task; automatic evaluation is hard to apply. Comparing approaches seems to be the best method to evaluate new techniques, and we offer a semi-automatized method for evaluation of the results. The recall is calculated automatically using existing links in Wikipedia. The precision is calculated according to manual evaluations of human assessors. Comparative results for different techniques are presented, showing the success of our improvements. We employ Turkish Wikipedia, we are the first to study on it, to examine whether a small instance is scalable enough for such purposes.

In this position paper, we argue that the Linked Open Data (LoD) Cloud, in its current form, is only of limited value for furthering the Semantic Web vision. Being merely a weakly linked triple collection, it will only be of very limited benefit for the AI or Semantic Web communities. We describe the corresponding problems with the LoD Cloud and give directions for research to remedy the situation.

Painters write about their what artists say about their own work, using motivations; photographers, about details of the concept mapping as a conceptual capture tool (Eskridge et.

Browsing linked open data (LOD) is a promising, yet, often unsatisfactory experience today. User-support for the identification of relevant information within the fast-growing cloud of LOD is limited. This paper presents LENA-TR, a browser for LOD that highlights relevant information with respect to different knowledge aspects hidden in linked data. Its interpretation of faceted navigation facilitates the sense-making and browsing of LOD, solving many of the shortcomings experienced in LOD browsing today.

In this paper we describe the production and consumption of linked data in the scenario of the Italian news agency ANSA portal. The goal of the use-case is to provide viewers of a news item with background information and links to related news articles contained on the portal. This information enrichment process is entity-based: ANSA news archive is analyzed using Name Entity Recognition, and each detected entity is annotated with a unique identifier. These identifiers are obtained using the Entity Name Server developed within the scope of the OKKAM European project. Subsequently the news are published on the portal using RDFa and linked to a semantic search engine that provides background information harvested from sources such as DBpedia and links to additional news sources. The presented project has the potential to contribute to Linked Data by creating and publishing a large quantity of entities and assertions about them coming from the ANSA news archive.

Modern web sites provide easy access to large amounts of data via open application programming interfaces. Users interacting with these sites constantly change the underlying data sets, which can be represented in graph-structured form. Nodes in these dynamic graph structures exhibit dependencies over time. Analysing these dependencies is crucial for understanding and predicting the dynamics inherent to temporally changing graph structures on the web. When the graphs become large however, it is not feasible to take into account all properties of the graph and in general it is unclear how to choose the appropriate features. Moreover, comparing two nodes becomes difficult, if the nodes do not share exactly the same features. In this work we propose an algorithm that automatically learns the features that govern temporal dependencies between nodes in large dynamic graph structures. We present preliminary results of applying the algorithm to data collected from the web, discuss potential extensions of the framework and anticipate how a major problem in data mining, sparse data, could be tackled by leveraging Linked Data.

Several solutions exist for semantically describing Web Services (WSs) from the perspective of orchestration but little is known about how semantics benefit WS choreography. The most extreme example of a choreography problem occurs in peer-to-peer systems where shared semantics of data may need to be established via services interactions. We present a solution to this problem by sharing micro-data via interaction models. No pre-unified ontology is required in our approach so peers can make use of existing heterogeneous resources having been described in the RDF data model flexibly and compatibly. The experimental results indicate that our approach semantically enhances WS choreography in a lightweight way which complies with principles of Linked Data and republished Interaction Models (IMs) can further facilitate the progress of the Web of data as well as the formation of peer communities generated through peers' interactions.