A Visual Semantic Framework for Innovation Analytics
Authors: Walid Shalaby, Kripa Rajshekhar, Wlodek Zadrozny
AAAI 2016 | Venue 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 EMAIL Kripa Rajshekhar Metonymy Labs Chicago Metropolitan Area EMAIL Wlodek Zadrozny Computer Science Department University of North Carolina at Charlotte EMAIL |
| 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. |