Ontology learning and question answering (qa) systems

Başkurt, Meltem
Ontology Learning requires a deep specialization on Semantic Web, Knowledge Representation, Search Engines, Inductive Learning, Natural Language Processing, Information Storage, Extraction and Retrieval. Huge amount of domain specific, unstructured on-line data needs to be expressed in machine understandable and semantically searchable format. Currently users are often forced to search manually in the results returned by the keyword-based search services. They also want to use their native languages to express what they search. In this thesis we developed an ontology based question answering system that satisfies these needs by the research outputs of the areas stated above. The system allows users to enter a question about a restricted domain by means of natural language and returns exact answer of the questions. A set of questions are collected from the users in the domain. In addition to questions, their corresponding question templates were generated on the basis of the domain ontology. When the user asks a question and hits the search button, system chooses the suitable question template and builds a SPARQL query according to this template. System is also capable of answering questions required inference by using generic inference rules defined at a rule file. Our evaluation with ten users shows that the sytem is extremely simple to use without any training resulting in very good query performance.


Natural language query processing in ontology based multimedia databases
Aygül, Filiz Alaca; Çiçekli, Fehime Nihan; Department of Computer Engineering (2010)
In this thesis a natural language query interface is developed for semantic and spatio-temporal querying of MPEG-7 based domain ontologies. The underlying ontology is created by attaching domain ontologies to the core Rhizomik MPEG-7 ontology. The user can pose concept, complex concept (objects connected with an “AND” or “OR” connector), spatial (left, right . . . ), temporal (before, after, at least 10 minutes before, 5 minutes after . . . ), object trajectory and directional trajectory (east, west, southe...
Multi-resolution visualization of large scale protein networks enriched with gene ontology annotations
Yaşar, Sevgi; Can, Tolga; Department of Computer Engineering (2009)
Genome scale protein-protein interactions (PPIs) are interpreted as networks or graphs with thousands of nodes from the perspective of computer science. PPI networks represent various types of possible interactions among proteins or genes of a genome. PPI data is vital in protein function prediction since functions of the cells are performed by groups of proteins interacting with each other and main complexes of the cell are made of proteins interacting with each other. Recent increase in protein interactio...
A hybrid movie recommender using dynamic fuzzy clustering
Gürcan, Fatih; Birtürk, Ayşe Nur; Department of Computer Engineering (2010)
Recommender systems are information retrieval tools helping users in their information seeking tasks and guiding them in a large space of possible options. Many hybrid recommender systems are proposed so far to overcome shortcomings born of pure content-based (PCB) and pure collaborative fi ltering (PCF) systems. Most studies on recommender systems aim to improve the accuracy and efficiency of predictions. In this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on content boosted co...
Improvement of corpus-based semantic word similarity using vector space model
Esin, Yunus Emre; Alpaslan, Ferda Nur; Department of Computer Engineering (2009)
This study presents a new approach for finding semantically similar words from corpora using window based context methods. Previous studies mainly concentrate on either finding new combination of distance-weight measurement methods or proposing new context methods. The main di fference of this new approach is that this study reprocesses the outputs of the existing methods to update the representation of related word vectors used for measuring semantic distance between words, to improve the results further. ...
Fuzzy association rule mining from spatio-temporal data: an analysis of meteorological data in turkey
Ünal Çalargün, Seda; Yazıcı, Adnan; Department of Computer Engineering (2008)
Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal cont...
Citation Formats
M. Başkurt, “Ontology learning and question answering (qa) systems,” M.S. - Master of Science, Middle East Technical University, 2010.