Template-Based Question Answering over Linked Data using Recursive Neural Networks

abstract: The Semantic Web contains large amounts of related information in the form of knowledge graphs such as DBpedia. These knowledge graphs are typically enormous and are not easily accessible for users as they need specialized knowledge in query languages (such as SPARQL) as well as deep familiarity of the ontologies used by these knowledge graphs. So, to make these knowledge graphs more accessible (even for non- experts) several question answering (QA) systems have been developed over the last decade. Due to the complexity of the task, several approaches have been undertaken that include techniques from natural language processing (NLP), information retrieval (IR), machine learning (ML) and the Semantic Web (SW). At a higher level, most question answering systems approach the question answering task as a conversion from the natural language question to its corresponding SPARQL query. These systems then utilize the query to retrieve the desired entities or literals. One approach to solve this problem, that is used by most systems today, is to apply deep syntactic and semantic analysis on the input question to derive the SPARQL query. This has resulted in the evolution of natural language processing pipelines that have common characteristics such as answer type detection, segmentation, phrase matching, part-of-speech-tagging, named entity recognition, named entity disambiguation, syntactic or dependency parsing, semantic role labeling, etc. This has lead to NLP pipeline architectures that integrate components that solve a specific aspect of the problem and pass on the results to subsequent components for further processing eg: DBpedia Spotlight for named entity recognition, RelMatch for relational mapping, etc. A major drawback in this approach is error propagation that is a common problem in NLP. This can occur due to mistakes early on in the pipeline that can adversely affect successive steps further down the pipeline. Another approach is to use query templates either manually generated or extracted from existing benchmark datasets such as Question Answering over Linked Data (QALD) to generate the SPARQL queries that is basically a set of predefined queries with various slots that need to be filled. This approach potentially shifts the question answering problem into a classification task where the system needs to match the input question to the appropriate template (class label). This thesis proposes a neural network approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination for the need of laborious feature engineering that can be cumbersome and error prone. The input question would be encoded into a vector representation. The model will be trained and evaluated on the LC-QuAD Dataset (Large-scale Complex Question Answering Dataset). The dataset was created explicitly for machine learning based QA approaches for learning complex SPARQL queries. The dataset consists of 5000 questions along with their corresponding SPARQL queries over the DBpedia dataset spanning 5042 entities and 615 predicates. These queries were annotated based on 38 unique templates that the model will attempt to classify. The resulting model will be evaluated against both the LC-QuAD dataset and the Question Answering Over Linked Data (QALD-7) dataset. The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset. After slot filling, the overall system achieves a macro F-score 0.419 on the LC- QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.Dissertation/ThesisMasters Thesis Software Engineering 201

Aspects of Coherence for Entity Analysis

Natural language understanding is an important topic in natural language proces- sing. Given a text, a computer program should, at the very least, be able to under- stand what the text is about, and ideally also situate it in its extra-textual context and understand what purpose it serves. What exactly it means to understand what a text is about is an open question, but it is generally accepted that, at a minimum, un- derstanding involves being able to answer questions like “Who did what to whom? Where? When? How? And Why?”. Entity analysis, the computational analysis of entities mentioned in a text, aims to support answering the questions “Who?” and “Whom?” by identifying entities mentioned in a text. If the answers to “Where?” and “When?” are specific, named locations and events, entity analysis can also pro- vide these answers. Entity analysis aims to answer these questions by performing entity linking, that is, linking mentions of entities to their corresponding entry in a knowledge base, coreference resolution, that is, identifying all mentions in a text that refer to the same entity, and entity typing, that is, assigning a label such as Person to mentions of entities. In this thesis, we study how different aspects of coherence can be exploited to improve entity analysis. Our main contribution is a method that allows exploiting knowledge-rich, specific aspects of coherence, namely geographic, temporal, and entity type coherence. Geographic coherence expresses the intuition that entities mentioned in a text tend to be geographically close. Similarly, temporal coherence captures the intuition that entities mentioned in a text tend to be close in the tem- poral dimension. Entity type coherence is based in the observation that in a text about a certain topic, such as sports, the entities mentioned in it tend to have the same or related entity types, such as sports team or athlete. We show how to integrate features modeling these aspects of coherence into entity linking systems and esta- blish their utility in extensive experiments covering different datasets and systems. Since entity linking often requires computationally expensive joint, global optimi- zation, we propose a simple, but effective rule-based approach that enjoys some of the benefits of joint, global approaches, while avoiding some of their drawbacks. To enable convenient error analysis for system developers, we introduce a tool for visual analysis of entity linking system output. Investigating another aspect of co- herence, namely the coherence between a predicate and its arguments, we devise a distributed model of selectional preferences and assess its impact on a neural core- ference resolution system. Our final contribution examines how multilingual entity typing can be improved by incorporating subword information. We train and make publicly available subword embeddings in 275 languages and show their utility in a multilingual entity typing tas

Few-shot entity linking of food names

Entity linking (EL), the task of automatically matching mentions in text to concepts in a target knowledge base, remains under-explored when it comes to the food domain, despite its many potential applications, e.g., finding the nutritional value of ingredients in databases. In this paper, we describe the creation of new resources supporting the development of EL methods applied to the food domain: the E.Care Knowledge Base (E.Care KB) which contains 664 food concepts and the E.Care dataset, a corpus of 468 cooking recipes where ingredient names have been manually linked to corresponding concepts in the E.Care KB. We developed and evaluated different methods for EL, namely, deep learning-based approaches underpinned by Siamese networks trained under a few-shot learning setting, traditional machine learning-based approaches underpinned by support vector machines (SVMs) and unsupervised approaches based on string matching algorithms. Combining the strengths of each of these approaches, we built a hybrid model for food EL that balances the trade-offs between performance and inference speed. Specifically, our hybrid model obtains 89.40% accuracy and links mentions at an average speed of 0.24 seconds per mention, whereas our best deep learning-based model, SVM model and unsupervised model obtain accuracies of 86.99%, 87.19% and 87.43% at inference speeds of 0.007, 0.66 and 0.02 seconds per mention, respectively

SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles

SocialLink is a project designed to match social media profiles on Twitter to corresponding entities in DBpedia. Built to bridge the vibrant Twitter social media world and the Linked Open Data cloud, SocialLink enables knowledge transfer between the two, both assisting Semantic Web practitioners in better harvesting the vast amounts of information available on Twitter and allowing leveraging of DBpedia data for social media analysis tasks. In this paper, we further extend the original SocialLink approach by exploiting graph-based features based on both DBpedia and Twitter, represented as graph embeddings learned from vast amounts of unlabeled data. The introduction of such new features required to redesign our deep neural network-based candidate selection algorithm and, as a result, we experimentally demonstrate a significant improvement of the performances of SocialLink

TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media

Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.Comment: Accepted to COLING 2022. Used to create the TempoWiC Shared Task for EvoNL