MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs.

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art

Querying knowledge graphs in natural language.

Knowledge graphs are a powerful concept for querying large amounts of data. These knowledge graphs are typically enormous and are often not easily accessible to end-users because they require specialized knowledge in query languages such as SPARQL. Moreover, end-users need a deep understanding of the structure of the underlying data models often based on the Resource Description Framework (RDF). This drawback has led to the development of Question-Answering (QA) systems that enable end-users to express their information needs in natural language. While existing systems simplify user access, there is still room for improvement in the accuracy of these systems. In this paper we propose a new QA system for translating natural language questions into SPARQL queries. The key idea is to break up the translation process into 5 smaller, more manageable sub-tasks and use ensemble machine learning methods as well as Tree-LSTM-based neural network models to automatically learn and translate a natural language question into a SPARQL query. The performance of our proposed QA system is empirically evaluated using the two renowned benchmarks-the 7th Question Answering over Linked Data Challenge (QALD-7) and the Large-Scale Complex Question Answering Dataset (LC-QuAD). Experimental results show that our QA system outperforms the state-of-art systems by 15% on the QALD-7 dataset and by 48% on the LC-QuAD dataset, respectively. In addition, we make our source code available

Systematic Literature Review on Ontology-based Indonesian Question Answering System

Question-Answering (QA) systems at the intersection of natural language processing, information retrieval, and knowledge representation aim to provide efficient responses to natural language queries. These systems have seen extensive development in English and languages like Indonesian present unique challenges and opportunities. This literature review paper delves into the state of ontology-based Indonesian QA systems, highlighting critical challenges. The first challenge lies in sentence understanding, variations, and complexity. Most systems rely on syntactic analysis and struggle to grasp sentence semantics. Complex sentences, especially in Indonesian, pose difficulties in parsing, semantic interpretation, and knowledge extraction. Addressing these linguistic intricacies is pivotal for accurate responses. Secondly, template-based SPARQL query construction, commonly used in Indonesian QA systems, suffers from semantic gaps and inflexibility. Advanced techniques like semantic matching algorithms and dynamic template generation can bridge these gaps and adapt to evolving ontologies. Thirdly, lexical gaps and ambiguity hinder QA systems. Bridging vocabulary mismatches between user queries and ontology labels remains a challenge. Strategies like synonym expansion, word embedding, and ontology enrichment must be explored further to overcome these challenges. Lastly, the review discusses the potential of developing multi-domain ontologies to broaden the knowledge coverage of QA systems. While this presents complex linguistic and ontological challenges, it offers the advantage of responding to various user queries across various domains. This literature review identifies crucial challenges in developing ontology-based Indonesian QA systems and suggests innovative approaches to address these challenges

Geospatial Question Answering on the YAGO2geo Knowledge Graph

Τα τελευταία χρόνια έχουν γίνει πολλές προσπάθειες για την ανάπτυξη συστημάτων που να μπορούν να επεξεργαστούν ερωτήσεις σε φυσική γλώσσα και να επιστρέψουν έυστοχες απαντήσεις ώστε να γίνει η πληροφορία διαθέσιμη σε όλους και όχι μόνο σε όσους μπορούν να γράψουν ερωτήματα σε βάσεις δεδομένων. Τέτοια συστήματα μπορούν να σχεδιαστούν έτσι ώστε να δουλεύουν για διάφορα είδη ερωτήσεων, από γεγονότα για ιστορικά πρόσωπα μέχρι επιστημονικά προβλήματα. Σε αυτή την πτυχιακή εργασία θα δουλέψουμε με γεωχωρικές ερωτήσεις. Χρησιμοποιούμε ένα ήδη υπάρχον σύστημα γεωχωρικών ερωτήσεων-απαντήσεων φυσικής γλώσσας που μέχρι τώρα χρησιμοποιεί τους γράφους γνώσης Dbpedia, GADM (Database of Global Administrative Areas) και OSM (Open Street Map) και το αλλάζουμε ώστε να χρησιμοποιεί το γράφο γνώσης YAGO2geo ο οποίος έχει επεκταθεί με δεδομένα από το Open Street Map, το Ordnance Survey και το GADM. Ο σκοπός της αλλαγής αυτής είναι η επίτευξη αποτελεσμάτων μεγαλύτερης ακρίβειας χρησιμοποιώντας τα γεωχωρικά δεδομένα του Open Street Map και του Ordnance και τον τεράστιο αριθμό κλάσεων που περιέχονται στο γράφο γνώσης YAGO2.In the recent years there have been many attempts to develop systems that can process natural language questions and return meaningful answers in order to make information available to everyone and not only to people who can write queries for databases. Such systems can be designed to work for different types of questions varying from facts about historical figures all the way to questions about science problems. In this thesis, we will be working with geospatial questions. We use an already existing geospatial natural language QA system (GeoQA system) that is currently using the DBpedia, GADM (Database of Global Administrative Areas) and OSM (Open Street Map) knowledge graphs and changing it to use the YAGO2geo knowledge graph which has been extended with Open Street Map, Ordnance Survey and GADM data. The purpose of this change is to achieve more accurate results using the geospatial information that is in Open Street Map and Ordnance Survey and the huge amount of classes that are included in the YAGO2 knowledge graph

Ontology-based approach to semantically enhanced question answering for closed domain: a review

Abstract: For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain