Systematic review of question answering over knowledge bases

Over the years, a growing number of semantic data repositories have been made available on the web. However, this has created new challenges in exploiting these resources efficiently. Querying services require knowledge beyond the typical user’s expertise, which is a critical issue in adopting semantic information solutions. Several proposals to overcome this dif- ficulty have suggested using question answering (QA) systems to provide user‐friendly interfaces and allow natural language use. Because question answering over knowledge bases (KBQAs) is a very active research topic, a comprehensive view of the field is essential. The purpose of this study was to conduct a systematic review of methods and systems for KBQAs to identify their main advantages and limitations. The inclusion criteria rationale was English full‐text articles published since 2015 on methods and systems for KBQAs.info:eu-repo/semantics/publishedVersio

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

Towards Dynamic Composition of Question Answering Pipelines

Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction. DBpedia has been the most prominently used knowledge graph in this setting. QA systems implement a pipeline connecting a sequence of QA components for translating an input question into its corresponding formal query (e.g. SPARQL); this query will be executed over a knowledge graph in order to produce the answer of the question. Recent empirical studies have revealed that albeit overall effective, the performance of QA systems and QA components depends heavily on the features of input questions, and not even the combination of the best performing QA systems or individual QA components retrieves complete and correct answers. Furthermore, these QA systems cannot be easily reused, extended, and results cannot be easily reproduced since the systems are mostly implemented in a monolithic fashion, lack standardised interfaces and are often not open source or available as Web services. All these drawbacks of the state of the art that prevents many of these approaches to be employed in real-world applications. In this thesis, we tackle the problem of QA over knowledge graph and propose a generic approach to promote reusability and build question answering systems in a collaborative effort. Firstly, we define qa vocabulary and Qanary methodology to develop an abstraction level on existing QA systems and components. Qanary relies on qa vocabulary to establish guidelines for semantically describing the knowledge exchange between the components of a QA system. We implement a component-based modular framework called "Qanary Ecosystem" utilising the Qanary methodology to integrate several heterogeneous QA components in a single platform. We further present Qaestro framework that provides an approach to semantically describing question answering components and effectively enumerates QA pipelines based on a QA developer requirements. Qaestro provides all valid combinations of available QA components respecting the input-output requirement of each component to build QA pipelines. Finally, we address the scalability of QA components within a framework and propose a novel approach that chooses the best component per task to automatically build QA pipeline for each input question. We implement this model within FRANKENSTEIN, a framework able to select QA components and compose pipelines. FRANKENSTEIN extends Qanary ecosystem and utilises qa vocabulary for data exchange. It has 29 independent QA components implementing five QA tasks resulting 360 unique QA pipelines. Each approach proposed in this thesis (Qanary methodology, Qaestro, and FRANKENSTEIN) is supported by extensive evaluation to demonstrate their effectiveness. Our contributions target a broader research agenda of offering the QA community an efficient way of applying their research to a research field which is driven by many different fields, consequently requiring a collaborative approach to achieve significant progress in the domain of question answering

QAestro - semantic-based composition of question answering pipelines

The demand for interfaces that allow users to interact with computers in an intuitive, effective, and efficient way is increasing. Question Answering (QA) systems address this need by answering questions posed by humans using knowledge bases. In recent years, many QA systems and related components have been developed both by practitioners and the research community. Since QA involves a vast number of (partially overlapping) subtasks, existing QA components can be combined in various ways to build tailored QA systems that perform better in terms of scalability and accuracy in specific domains and use cases. However, to the best of our knowledge, no systematic way exists to formally describe and automatically compose such components. Thus, in this work, we introduce QAestro, a framework for semantically describing both QA components and developer requirements for QA component composition. QAestro relies on a controlled vocabulary and the Local-as-View (LAV) approach to model QA tasks and components, respectively. Furthermore, the problem of QA component composition is mapped to the problem of LAV query rewriting, and state-of-the-art SAT solvers are utilized to efficiently enumerate the solutions. We have formalized 51 existing QA components implemented in 20 QA systems using QAestro. Our empirical results suggest that QAestro enumerates the combinations of QA components that effectively implement QA developer requirements

QAestro – Semantic-based composition of question answering pipelines

The demand for interfaces that allow users to interact with computers in an intuitive, effective, and efficient way is increasing. Question Answering (QA) systems address this need by answering questions posed by humans using knowledge bases. In recent years, many QA systems and related components have been developed both by practitioners and the research community. Since QA involves a vast number of (partially overlapping) subtasks, existing QA components can be combined in various ways to build tailored QA systems that perform better in terms of scalability and accuracy in specific domains and use cases. However, to the best of our knowledge, no systematic way exists to formally describe and automatically compose such components. Thus, in this work, we introduce QAestro, a framework for semantically describing both QA components and developer requirements for QA component composition. QAestro relies on a controlled vocabulary and the Local-as-View (LAV) approach to model QA tasks and components, respectively. Furthermore, the problem of QA component composition is mapped to the problem of LAV query rewriting, and state-of-the-art SAT solvers are utilized to efficiently enumerate the solutions. We have formalized 51 existing QA components implemented in 20 QA systems using QAestro. Our empirical results suggest that QAestro enumerates the combinations of QA components that effectively implement QA developer requirements. © 2017, Springer International Publishing AG