Knowledge-Based Systems

Recommended Prerequisites

Though not-essential, knowledge on the following will be useful:

- Fundamentals of Artificial Intelligence

- Fundamentals of Machine Learning

- Web programming

- Java programming.

Teaching Methods

The course is structured around two main activities:

- Lectures

- Project

There will be two types of classes:

-Expositive, where the teacher will present the main concepts

-Project support, where the teacher will check the project development, guide the students, and assess progress.

The project consists of the development of an application that involves the representation, management and knowledge extraction for a domain, along several milestones, and described in a final report.

Learning Outcomes

The following skills will be acquired:

-Building and managing knowledge representation structures, namely ontologies.

-Using Semantic Web languages for knowledge representation (RDF, RDFs, OWL).

-Using triple stores and SPARQL.

-Reusing vocabularies and Linked Data.

-Using semantic search mechanisms.

-Specification and development of intelligent systems that explore knowledge structures for achieving their goals.

-Development of mechanisms for extracting knowledge from non-structured data (text) and their integration in knowledge bases.

Based on the previous, the following concepts will be acquired:

-Ontology, other structures of knowledge, and languages for representing them;

-Linked Data;

-Triple store, SPARQL;

-Information Retrieval;

-Semantic Search;

-Vector space model for Information Retrieval;

-Distributional Semantics;

-Information Extraction

-Natural Language Processing and related tasks.

Work Placement(s)

No

Syllabus

1. Knowledge Representation:

            a. Logic fundamentals (popositional logic, first-order logic)

            b. Languages RDF, RDF Schema, OWL

            c. Structures: taxonomies, thesauri, ontologies

            d. Linked Data and Vocabulary Reutilization

2. Information Retrieval and Semantic Search

            a. In structured data: triple stores, SPARQL, reasoning mechanisms

            b. Non-structured data (text)

            c. Performance of Information Retrieval Systems

3. From text to structured knowledge:

            a. Natural Language Processing

            b. Knowledge Extraction for Knowledge Base population

            c. Distributional Semantics.

Assessment Methods

Assessment
Exam: 35.0%
Project: 65.0%

Bibliography

-- Liyang Yu. A Developer's Guide to the Semantic Web, 3rd edition. Springer, 2015.

 -- ChengXiang Zhai & Sean Massung. Text Data Management and Analysis: a Practical Introduction to Information Retrieval and Text Mining. ACM and Morgan & Claypool, 2016.

 -- Dan Jurafsky & James H. Martin. Speech and Language Processing. Prentice Hall, 2009.