Christoph Lange, Ph.D.: Research Statement and Vision: A Social Semantic Web for Science, Technology, Engineering, and Mathematics

My medium to long-term research goal is to establish a Social Semantic Web for STEM, in which knowledge from these fields is easily accessible and comprehensible to agents and humans. This vision has two facets, and my mission is to bridge them:

Contributing STEM Knowledge to the Web of Data means applying existing Social Semantic Web and Linked Data technology to contribute existing or new STEM knowledge to the Web of Data. That makes STEM knowledge accessible, retrievable, reusable, and comprehensible to intelligent software agents, and thus to their human end-users. It also enables linking pure mathematical knowledge to relevant related knowledge about applications, projects, people, and their social networks. Expected benefits include: a better utilization of mainstream mathematical knowledge (e.g. from social websites such as Wikipedia) and of scientific publications (e.g. opening digital libraries such as arXiv.org to semantic search engines), more flexible and reliable ways of collecting and interpreting public sector information, and better reproducibility of e-science experiments.
Ways to contribute knowledge include developing tools that enable scientists, engineers and mathematicians to collaboratively create new content on the Web of Data, but also automatic translation from existing knowledge repositories to Linked Data (possibly guided by human hints) – and designing suitable representation languages, ontologies, and metadata vocabularies for both. There is also a huge potential in enriching existing Linked Open Datasets, e.g. from e-science or e-government, by extending their coverage to the mathematical foundations of their respective domains.
Improving the Design of the Social Semantic Web by Taking Advantage of the State of the Art in STEM Knowledge Management: STEM knowledge is usually communicated via documents that use an advanced combination of natural language interspersed with a context-sensitive symbolic formula language (with context dimensions such as one's native language, level of expertise, application background, community of practice, or simply individual preference). STEM language, particularly mathematical language, has an extensible vocabulary, whose semantics is often not expressible in just one simple subset of first order logic. Concretely, I am working on contributing two achievements of STEM Knowledge Management research to the Semantic Web:
- expressive document markup languages that mix symbolic and natural language, and authoring tools optimized for them. Thus, we obtain (1) a complete representation that can be formally validated, and (2) the possibility to produce a human-comprehensible documentation at the same time.
- high-quality publishing of complex knowledge structures for screen or print media

Research questions in the intersection of “applying Social Semantic Web technology in the STEM domain” and “evolving the Social Semantic Web with some more mathematical spirit” include the following. With my research I aim at answering them.

What paradigms for human readers interacting with documents and with each other (and groups of readers interacting with document collections) are required to enable ordinary humans (students, engineers, etc.) to cope with the inherent structural complexity of STEM knowledge?
How can existing knowledge representation formalisms (such as ontology languages) be enhanced in order to capture STEM knowledge in its full complexity, while still allowing for efficient computer processing (e.g. reasoning)?
How can a mathematical notion of computation be integrated into logically lightweight representations of static structures of mathematical knowledge and into Linked Data query languages?

Areas of Interest

Linked Data
Mathematical Knowledge Management
Ontology Languages
Scientific Publishing
Semantic Wikis

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