by FinScience

A brief presentation of who we are

FinScience is an Italian data-driven fintech company founded in 2017 in Milan by Google’s former senior managers and Alternative Data experts, who have combined their digital and financial expertise. FinScience, thus, originates from this merger of the world of Finance and the world of Data Science.
The company leverages founders’ experiences concerning Data Governance, Data Modeling and Data Platforms solutions. These are further enriched through the tech role in the European Consortium SSIX (Horizon 2020 program) focused on the building of a Social Sentiment for financial purposes. FinScience applies proprietary AI-based technologies to combine financial data/insights with alternative data in order to generate new investment ideas, ESG scores and non-conventional lists of companies that can be included in investment products by financial operators.

The FinScience’s data analysis pipeline is strongly grounded on the DBpedia ontology: the greatest value, according to our experience, is given by the possibility to connect knowledge in different languages, to query automatically-extracted structured information and to have rather frequently updated models.

Products and solutions

FinScience daily retrieves content from the web. About 1.5 million web pages are visited every day on about 35.000 different domains. The content of these pages is extracted, interpreted and analysed via Natural Language Processing techniques to identify valuable information and sources. Thanks to the structured information based on the DBpedia ontology, we can apply our proprietary AI algorithms to suggest to our customers the right investment opportunities.Our products are mainly based on the integration of this purely digital data – we call it “alternative data”- with traditional sources coming from the world of finance and sustainability. We describe these products briefly:

• FinScience Platform for traders​: it leverages the power of machine learning to help traders monitor specific companies, spot new trends in the financial market, give access to an high added-value selection of companies and themes.
• ESG scoring​: we provide an assessment of corporate ESG performance, by combining internal data (traditional, self-disclosed data) with external ‘alternative’ data (stakeholder-generated data) in order to measure the gap between what the companies communicate and what is stakeholder perception related to corporate sustainability commitments.
• Thematic selections of listed companies​ : we create Trend-Driven selections oriented towards innovative themes: our data, together with the analysis of financial specialists, contribute to the selection of a set of listed companies related to trending themes such as the Green New Deal, the 5G technology or new medtech applications.

FinScience and DBpedia

As mentioned before, FinScience is strongly grounded in the DBpedia ontology, since we employ Spotlight to perform Named Entity Recognition (NER), namely automatic annotation of entities in a text. The NER task is performed with a two step procedure. The first step consists in annotating the named entity of a text using ​ DBpedia Spotlight​. In particular, Spotlight links a mention in the text (that is identified by its name and its context within the text) to the DBpedia entity that maximizes the joint probability of occurrence of both. The model is pre-trained on texts extracted from Wikipedia. Note that each entity is represented by a link to a DBpedia page (see, e.g. ​ http://dbpedia.org/page/Eni​ ), a DBpedia type indicating the type of the entity according to ​ this​ ontology and other information.

Another interesting feature of this approach is that we have a one to one mapping of the italian and english entities (and in general any language supported by DBpedia), allowing us to have a unified representation of an entity in the two languages. We are able to obtain this kind of information by exploiting the potential of ​ DBpedia Virtuoso​, which allows us to access DBpedia dataset via SPARQL. By identifying the entities mentioned in the online content, we can understand which topics are mentioned and thus identify companies and trends that are spreading in the digital ecosystem as well as analyzing how they are related to each other.

Challenges and next steps

One of the toughest challenges for us is to find an optimal way to update the models used by DBpedia Spotlight. Every day new entities and concepts arise and we are willing to recognise them in the news we analyze. And that is not all. In addition to recognizing new concepts, we need to be able to track an entity through all the updated versions of the model. In this way, we will not only be able to identify entities, but we will also have evidence of when some concepts were first generated. And we will know how they have changed over time, regardless of the names that have been used to identify them.

We are strongly involved in the DBpedia community and we try to contribute with our know-how. Particularly FinScience will contribute on infrastructure and Dockerfiles as well as on finding issues on the new released project (for instance, ​wikistats-extractor​).

A big thank you to FinSciene for presenting their products, challenges and contribution to DBpedia.  

Yours,

DBpedia Association

The post FinScience: leveraging DBpedia tools for fintech applications appeared first on DBpedia Association.

 by Irene Martínez and Susana López from GNOSS

GNOSS (https://www.gnoss.com/) is a Spanish technology manufacturing company that has developed its own platform for the construction and exploitation of knowledge graphs. GNOSS technology operates within the framework of the set of technologies that concur in the Artificial Intelligence Program semantically interpreted: NLU (Natural Language Understanding); identification, extraction, disambiguation and linking of entities; as well as the construction of interrogation and knowledge discovery systems based on inferences and on systems that emulate the forms of natural reasoning.

How is our business focus

The GNOSS project is positioned in the emerging market for Deep AI (Deep Understanding AI). By Deep AI we mean the convergence of symbolic AI and sub-symbolic AI.

GNOSS is the leading company in Spain in the construction of solutions aimed at the construction of knowledge ecosystems interpretable and queryable (interrogable) by machines and people, which integrate heterogeneous and distributed data represented by technical vocabularies and ontologies written in programming languages (OWL-RDF ) interpretable by machines, which are consolidated and exploited through knowledge graphs

The technology developed by GNOSS facilitates the construction, within the framework of the aforementioned ecosystems, of intelligent interrogation and search systems, information enrichment and context generation systems, advanced recommendation systems, predictive Business Intelligence systems based on dynamic visualizations and NLP/NLU systems.

GNOSS works in the cloud and is offered as a service. We have a complex and robust technological infrastructure designed to compute intelligent data in a framework that offers the maximum guarantee of security and best practices in technology services.

Products and Solutions

PRODUCTS

GNOSS Knowledge Graph Builder is a development platform upon which third parties can deploy their web projects, with a complete suite of components to build Knowledge Graphs and deploy an intelligent web semantically aware in record time. The platform enables the interrogation of a Knowledge Graph by both machines and people. The main modules of the platform are 1) Metadata and Knowledge Graph Construction and Management; 2)Discovery, reasoning and analysis through Knowledge Graphs; 3) Semantic Content Management. It also includes some configurable characteristics and functions for fast, agile and flexible adaptation and evolution of intelligent digital ecosystems

SOLUTIONS

Thanks to GNOSS Knowledge Graph Builder and GNOSS Sherlock Services, we have developed a suite of transversal solutions and some sectorial solutions based on the creation and exploitation of Knowledge Graphs.

The transversal solutions are: GNOSS Metadata Management Solution (for the integration of heterogeneous and distributed information into semantic data layer consolidating information into a knowledge graph), GNOSS Sherlock NLP-NLU Service (Intelligent software services for machines to understand us, based on natural language processing and on entity recognition and linking; and dynamic graphic visualizations), GNOSS Search Cloud (which includes intelligent information search, interrogation and retrieval systems; inferences; recommendations and generation of significant contexts), GNOSS Semantic BI&Analytics (expressive and dynamic Business Intelligence based on Smart Data).

We have developed sectorial solutions in Education and University, Tourism, Culture and Museums, Healthcare, Communication and MK, Banking, Public Administration; Catalogs and support to supply chain.

What significance does DBpedia for us

We think that the foundations for the construction of the great European Project of Symbolic AI are being created thanks to DBpedia and other Linked Open Data projects, by turning the internet into a Universal Knowledge Base, which works according to the principles and standards of Linked Open Data and Semantic Web. This knowledge base, as the brain of the internet, would be the basis of the IA of the future. In this context, we consider that DBpedia plays a central role as an open general knowledge base and, therefore, as the core of the European Project of Symbolic AI.

Currently, some projects developed with GNOSS platform are already using DBpedia to access a large amount of structured and ontologically-represented information, in order to link entities, enrich information and offer contextual information. Two examples of this are the ‘Augmented Reading’ of Museo del Prado in the descriptions of the artworks of the Museum Prado, and the Graph of related entities in Didactalia.net.

The ‘Augmented Reading’ of Museo del Prado in the descriptions of the artworks of the Museum (see for instance ‘The Family of Carlos IV’, by Francisco de Goya) recognizes and extracts the entities contained in them, thereby providing additional and contextual information about them, so that anyone who can read them without giving up understanding them in depth.

In Didactalia.net, for a given educational resource, its Graph of related entities works as a conceptual map of the resource to support the teacher and the student in the teaching-learning process (see for instance this resource about Descartes).

How do we envision our future work with DBpedia

GNOSS can contribute to DBpedia at different levels, from making suggestions for further development to participating in strategy and commercialization.

We could collaborate with DBpedia contributing to tests of the releases of DBpedia and giving our feedback of the use of DBpedia in projects applied to public and private organizations developed with GNOSS. Based on this, we could make suggestions for future work considering our experience and customer needs in this context.

We could participate in the strategy and commercialization, in order to gain more presence in sectors in which we work, such as healthcare, education, culture or communication, and to achieve that the private companies can appreciate and benefit from the great value that DBpedia can offer them.

A big thank you to GNOSS for presenting their product and envisioning how they would like to work with DBpedia in the future.

Yours,

DBpedia Association

The post GNOSS – How do we envision our future work with DBpedia appeared first on DBpedia Association.

Prior to joining Aalborg University, Katja was a postdoc at the Max Planck Institute for Informatics in Saarbrücken. She received her doctoral degree in Computer Science from Ilmenau University of Technology in Germany.

Can you tell us something about your research focus?

The most important focus of my research has been querying the Web of Data, in particular, efficient query processing over distributed knowledge graphs and Linked Data. This includes indexing, source selection, and efficient query execution. Unfortunately, it happens all too often that the services needed to access remote knowledge graphs are temporarily not available, for instance, because a software component crashed. Hence, we are currently developing a decentralized architecture for knowledge sharing that will make access to knowledge graphs a reliable service, which I believe is the key to a wider acceptance and usage of this technology.

How do you personally contribute to the advancement of semantic technologies?

I contribute by doing research, advancing the state of the art, and applying semantic technologies to practical use cases.  The most important achievements so far have been our works on indexing and federated query processing, and we have only recently published our first work on a decentralized architecture for sharing and querying semantic data. I have also been using semantic technologies in other contexts, such as data warehousing, fact-checking, sustainability assessment, and rule mining over knowledge bases.

Overall, I believe the greatest ideas and advancements come when trying to apply semantic technologies to real-world use cases and problems, and that is what I will keep on doing.

Which trends and challenges do you see for linked data and the semantic web?

The goal and the idea behind Linked Data and the Semantic Web is the second-best invention after the Internet. But unlike the Internet, Linked Data and the Semantic Web are only slowly being adopted by a broader community and by industry.

I think part of the reason is that from a company’s point of view, there are not many incentives and added benefit of broadly sharing the achievements. Some companies are simply reluctant to openly share their results and experiences in the hope of retaining an advantage over their competitors. I believe that if these success stories were shared more openly, and this is the trend we are witnessing right now, more companies will see the potential for their own problems and find new exciting use cases.

Another particular challenge, which we will have to overcome, is that it is currently still far too difficult to obtain and maintain an overview of what data is available and formulate a query as a non-expert in SPARQL and the particular domain… and of course, there is the challenge that accessing these datasets is not always reliable.

As artificial intelligence becomes more and more important, what is your vision of AI?

AI and machine learning are indeed becoming more and more important. I do believe that these technologies will bring us a huge step ahead. The process has already begun. But we also need to be aware that we are currently in the middle of a big hype where everybody wants to use AI and machine learning – although many people actually do not truly understand what it is and if it is actually the best solution to their problems. It reminds me a bit of the old saying “if the only tool you have is a hammer, then every problem looks like a nail”. Only time will tell us which problems truly require machine learning, and I am very curious to find out which solutions will prevail.

However, the current state of the art is still very far away from the AI systems that we all know from Science Fiction. Existing systems operate like black boxes on well-defined problems and lack true intelligence and understanding of the meaning of the data. I believe that the key to making these systems trustworthy and truly intelligent will be their ability to explain their decisions and their interpretation of the data in a transparent way.

What are your expectations about Semantics 2019 in Karlsruhe?

First and foremost, I am looking forward to meeting a broad range of people interested in semantic technologies. In particular, I would like to get in touch with industry-based research and to be exposed 

The End

We like to thank Katje Hose for her insights and are happy to have her as one of our keynote speakers.

Visit SEMANTiCS 2019 in Karlsruhe, Sep 9-12 and get your tickets for our community meeting here. We are looking forward to meeting you during DBpedia Day.

Yours DBpedia Association

The post SEMANTiCS 2019 Interview: Katja Hose appeared first on DBpedia Association.

In this digital transformation era, success will be based on using analytics to discover the insights locked in the massive volume of data being generated today. Success with AI and ML depends on having the right infrastructure to process the data.[1]

The Value of Data Governance

One key element to facilitate ML and AI for the digital future of Europe, are ‘decentralized semantic data flows’, as stated by Sören Auer, a founding member of DBpedia and current director at TIB, during a meeting about the digital future in Germany at the Bundestag. He further commented that major AI breakthroughs were indeed facilitated by easily accessible datasets, whereas the Algorithms used were comparatively old.

In conclusion, Auer reasons that the actual value lies in data governance. Infact, in order to guarantee progress in  AI, the development of a common and transparent understanding of data is necessary. [2]

DBpedia Databus – Digital Factory Platform

The DBpedia Databus  – our digital factory Platform –  is one of many drivers that will help to build the much-needed data infrastructure for ML and AI to prosper.  With the DBpedia Databus, we create a hub that facilitates a ‘networked data-economy’ revolving around the publication of data. Upholding the motto, Unified and Global Access to Knowledge, the databus facilitates exchanging, curating and accessing data between multiple stakeholders  – always, anywhere. Publishing data on the Databus means connecting and comparing (your) data to the network. Check our current DBpedia releases via http://downloads.dbpedia.org/repo/dev/.

DBpediaDay – & AI for Smart Agriculture

Furthermore, you can learn about the DBpedia Databus during our 13th DBpedia Community meeting, co-located with LDK conference,  in Leipzig, May 2019. Additionally, as a special treat for you, we also offer an AI side-event on May 23rd, 2019.

May we present you the thinktank and hackathon  – “Artificial Intelligence for Smart Agriculture”. The goal of this event is to develop new ideas and small tools which can demonstrate the use of AI in the agricultural domain or the use of AI for a sustainable bio-economy. In that regard, a special focus will be on the use and the impact of linked data for AI components. 

In short, the two-part event, co-located with LSWT & DBpediaDay, comprises workshops, on-site team hacking as well as presentations of results. The activity is supported by the projects DataBio and Bridge2Era as well as CIAOTECH/PNO. All participating teams are invited to join and present their projects. Further Information are available here. Please submit your ideas and projects here.  

Finally, the DBpedia Association is looking forward to meeting you in Leipzig, home of our head office. Pay us a visit!

____

Resources:

[1] Zeus Kerravala; The Success of ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Requires an Architectural Approach to Infrastructure. ZK Research: A Division of Kerravala Consulting © 2018 ZK Research, available via http://bit.ly/2UwTJRo

[2] Sören Auer; Statement at the Bundestag during a meeting in AI, Summary is available via https://www.tib.eu/de/service/aktuelles/detail/tib-direktor-als-experte-zu-kuenstlicher-intelligenz-ki-im-deutschen-bundestag/

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The Conference

This new biennial conference series aims at bringing together researchers from across disciplines concerned with language data in data science and knowledge-based applications.

Keynote Speakers

We are happy, that Christian Bizer, a founding member of DBpedia, will be one of the three amazing keynote speakers that open the LDK conference. Apart from Christian, Christiane Fellbaum from Princeton University and  Eduart Werner, representative of Leipzig University will share their thoughts on current language data issues to start vital discussions revolving around language data.

Be part of this event in Leipzig and catch up with the latest research outcomes in the areas of acquisition, provenance, representation, maintenance, usability, quality as well as legal, organizational and infrastructure aspects of language data.  

DBpedia Community Meeting

To get the full Leipzig experience, we also like to invite you to our DBpedia Community meeting, which is colocated with LDK and will be held on May, 23rd 2019. Contributions are still welcome. Just in get in touch via dbpedia@infai.org .

We also offer an interesting side-event, the Thinktank and Hackathon “Artificial Intelligence for Smart Agriculture”. Visit our website for further information.

Join LDK conference 2019 and our DBpedia Community Meeting to catch up with the latest research and developments in the Semantic Web Community. 

Yours DBpedia Association

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