(Internship/master Thesis) Knowledge Graph
Vor 2 Tagen
The amount of generated and available information is growing exponentially in most enterprises, but this data is often unstructured and fragmented into various sources. Consolidation and linking into structured knowledge bases are critical to ensure efficient access to relevant information. Knowledge graphs are a collection of interlinked descriptions of concepts, entities, relationships, and events. This knowledge modeling approach puts data in context via linking and semantic metadata and thus provides an ideal framework for data integration, unification, and analytics.
Challenges:
The harmonization and consolidation of multiple data sources into a unique knowledge base requires thought-out workflows, as most of the information is quite fragmented and only loosely structured.
The resulting structure should possess a high degree of expressivity to allow for a fluent and flexible representation of ELCA data, while maintaining good performances for the management and the exploration of the graph. The definition of clear formal semantics should lead to interpretable data and the possibility to infer new facts.
What you will learn:
You will be a Junior Data Scientist for the ELCA Data Science team in Lausanne, developing your skills in the NLP domain and in Machine Learning in general, under the guidance of experienced data scientists. You will tackle a concrete problem, with real-world enterprise data, helping ELCA in modeling and taking advantage of its knowledge bases.
Keywords:
Machine Learning, Knowledge Graphs, NLP, Information Extraction, Entity & Relationship Extraction, Ontologies
**In this role**:**ACTIVITIES**
You will explore and evaluate an approach to construct a Knowledge Graph from a subset of ELCA knowledge bases (project documentation, CRM data, CVs, external sources,). To do so, you will explore NLP methods (entity and relation extraction/resolution, ontology learning), a well as methods to extract and link meaningful information from non-textual data (such as.pptx files, tabular data,).
**OBJECTIVES**
- Define and harmonize collection and information extraction procedures from a variety of sources and for different data formats
- Propose a semisupervised approach for the construction of a knowledge graph from the sources, learning and extracting entities and relationships.
- Evaluate the structure and performance of the knowledge graph on information retrieval and inference tasks
**What we offer**:
- A dynamic work and collaborative environment with a highly motivated multi-cultural and multiples international sites team
- Personal development through training and coaching
- A flat hierarchy and a culture of collaboration across all disciplines
- The chance to make a difference in peoples’ life by building innovative solutions
- High innovation and research backed up by collaboration with universities like EPFL
- Various internal coding events (Hackathon, Brownbags), see our technical blog
- Monthly After-Works organized per locations
- Good life balance (41 working hours per week and possibility to work 2 days per week from home)
**About your profile**:**Knowledge**:
- Machine Learning
- Natural Language Processing
- Data engineering
**Skills**:
- Solid Python skills, good software engineering practices
- ML/DL frameworks
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