Introduction:
Knowledge discovery from unstructured data has gained the attention of many practitioners over the past decades. In spite of major AI research focusing on data sources like news, web, and social media, its application to data in professional settings such as legal documents and financial filings, still present huge challenges.

In the financial services industry, in particular, vast analysis work requires knowledge discovery from various data sources, such as SEC filings, loan documents, and industry reports. The manual knowledge discovery and extraction process is usually low in efficiency, error-prone, and inconsistent. It is one of the key bottlenecks for financial services companies in improving their operating productivity. Furthermore, alternative data like social media feeds and news are gaining traction as promising knowledge sources for financial institutions as they provide additional perspectives when they make investment decisions. However, the valuable knowledge is always comingled with immense noise and the precision and recall requirements for extracted knowledge to be used in the business process are fastidious.

These challenges and issues call for the need of robust artificial intelligence (AI) algorithms and systems. The design and implementation of these AI techniques to meet financial business operations requires a joint effort between academic researchers and industry practitioners.

This one-day workshop will include invited speaks, paper presentations, and poster sessions to showcase research opportunities, novel solutions and systems, and success stories. We cordially welcome researchers, practitioners, and students from academic and industrial communities who are interested in the topics to participate and/or submit their original work.

Call for Papers:
We invite submissions of original contributions on methods, applications, and systems on artificial intelligence, machine learning, and data analytics, with a focus on knowledge discovery and extraction in the financial services domain. The scope of the workshop includes, but is not limited to, the following areas:

Knowledge representation;
Natural language processing and understanding for financial documents;
Search and question answering systems designed for financial corpora;
Named-entity recognition, disambiguation, relationship discovery, ontology learning and extraction from financial documents;
Knowledge alignment and integration from heterogeneous data;
AI assisted data tagging and labeling;
Data acquisition, augmentation, feature engineering, and analysis for investment and risk management;
Automatic knowledge extraction from financial filings and quality verification;
AI systems for relationship extraction and risk assessment from legal documents;
Event discovery from alternative data and impact on organization equity price.
We also encourage submissions of studies or applications pertinent to finance using other types of unstructured data such as financial transactions, sensors, mobile devices, satellites, social media.

Submissions:
We invite submissions of relevant work that be of interest to the workshop. All submissions must be original contributions, following the AAAI-20 formatting guidelines. We accept two types of submissions - full research paper no longer than 8 pages and short/poster paper with 2-4 pages. Submission will be accepted via EasyChair submission website: https://easychair.org/conferences/?conf=kdf2020.

Contact Information:
For general inquiries about the KDF workshop, participation, and submissions, please write to kdf2020@easychair.org.