Mayesha Tasnim

Mayesha Tasnim

PhD Student at Civic AI Lab, University of Amsterdam

Posts by Collection

portfolio

publications

Synthesizing a Knowledge Graph of Data Scientist Job Offers with MINTE+

Published in International Semantic Web Conference, 2018

Data Scientist is one of the most sought-after jobs of this decade. In order to analyze the job market in this domain, interested institutions have to integrate numerous job advertising coming from heterogeneous Web sources eg, job portals, company websites, professional community platforms such as StackOverflow, GitHub, etc. In this demo, we show the application of the RDF Molecule-Based Integration Framework MINTE+ in the domain-specific application of job market analysis. The use of RDF molecules for knowledge representation is a core element of the framework gives MINTE+ enough flexibility to integrate job advertising from different web resources and countries. Attendees will observe how exploration and analysis of the data science job market in Europe can be facilitated by synthesizing at query time a consolidated knowledge graph of job advertising.

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Summarizing Entity Temporal Evolution in Knowledge Graphs

Published in Companion Proceedings of The World Wide Web Conference, 2019

Knowledge graphs are dynamic in nature, new facts about an entity are added or removed over time. Therefore, multiple versions of the same knowledge graph exist, each of which represents a snapshot of the knowledge graph at some point in time. Entities within the knowledge graph undergo evolution as new facts are added or removed. The problem of automatically generating a summary out of different versions of a knowledge graph is a long-studied problem. However, most of the existing approaches are limited to a pairwise version comparison. This limitation makes it difficult to capture a complete evolution out of several versions of the same knowledge graph. To overcome this limitation, we envision an approach to create a summary graph capturing temporal evolution of entities across different versions of a knowledge graph. The entity summary graphs may then be used for documentation generation.

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COMET: A Contextualized Molecule-Based Matching Technique

Published in International Conference on Database and Expert Systems Applications, 2019

Context-specific description of entities –expressed in RDF– poses challenges during data-driven tasks, e.g., data integration, and context-aware entity matching represents a building-block for these tasks. However, existing approaches only consider inter-schema mapping of data sources, and are not able to manage several contexts during entity matching. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and context-based similarity metrics to match contextually equivalent entities. COMET executes a novel 1-1 perfect matching algorithm for matching contextually equivalent entities based on the combined scores of semantic similarity and context similarity. COMET employs the Formal Concept Analysis algorithm in order to compute the context similarity of RDF entities.

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talks

teaching

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.