A Visual Semantic Framework for Innovation Analytics
Authors: Walid Shalaby, Kripa Rajshekhar, Wlodek Zadrozny
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this demo we present a Web-based semantic, visual, and interactive framework for innovation analytics. Initial results suggest a relatively high recall rate of relevant prior art using MSA. Figure 2: Top: Concept graph of Cognitive Analytics; explicit concepts are light blue nodes, and implicit concepts are red nodes. Bottom: Theme River plot showing patenting evolution of Cognitive Analytics and related technologies in its concept graph. |
| Researcher Affiliation | Collaboration | Walid Shalaby Computer Science Department University of North Carolina at Charlotte wshalaby@uncc.edu Kripa Rajshekhar Metonymy Labs Chicago Metropolitan Area kripa@metolabs.com Wlodek Zadrozny Computer Science Department University of North Carolina at Charlotte wzadrozn@uncc.edu |
| Pseudocode | No | The paper describes the steps of the concept graph construction pipeline but does not present them as structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions using Wikipedia and patent data, but does not provide concrete access information (link, DOI, specific citation with author/year, or repository name) for a specific dataset version or split used for training. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper describes the underlying approach (MSA) but does not list specific ancillary software components with version numbers needed for replication. |
| Experiment Setup | No | The paper describes the framework's pipeline and interactive features but does not provide specific experimental setup details such as hyperparameters or training configurations. |