22 research outputs found
Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
Mentions of new concepts appear regularly in texts and require automated
approaches to harvest and place them into Knowledge Bases (KB), e.g.,
ontologies and taxonomies. Existing datasets suffer from three issues, (i)
mostly assuming that a new concept is pre-discovered and cannot support
out-of-KB mention discovery; (ii) only using the concept label as the input
along with the KB and thus lacking the contexts of a concept label; and (iii)
mostly focusing on concept placement w.r.t a taxonomy of atomic concepts,
instead of complex concepts, i.e., with logical operators. To address these
issues, we propose a new benchmark, adapting MedMentions dataset (PubMed
abstracts) with SNOMED CT versions in 2014 and 2017 under the Diseases
sub-category and the broader categories of Clinical finding, Procedure, and
Pharmaceutical / biologic product. We provide usage on the evaluation with the
dataset for out-of-KB mention discovery and concept placement, adapting recent
Large Language Model based methods.Comment: 5 pages, 1 figure, accepted for CIKM 2023. The dataset, data
construction scripts, and baseline implementation are available at
https://zenodo.org/record/8228005 (Zenodo) and
https://github.com/KRR-Oxford/OET (GitHub
Longtonotes: OntoNotes with Longer Coreference Chains
Ontonotes has served as the most important benchmark for coreference
resolution. However, for ease of annotation, several long documents in
Ontonotes were split into smaller parts. In this work, we build a corpus of
coreference-annotated documents of significantly longer length than what is
currently available. We do so by providing an accurate, manually-curated,
merging of annotations from documents that were split into multiple parts in
the original Ontonotes annotation process. The resulting corpus, which we call
LongtoNotes contains documents in multiple genres of the English language with
varying lengths, the longest of which are up to 8x the length of documents in
Ontonotes, and 2x those in Litbank. We evaluate state-of-the-art neural
coreference systems on this new corpus, analyze the relationships between model
architectures/hyperparameters and document length on performance and efficiency
of the models, and demonstrate areas of improvement in long-document
coreference modeling revealed by our new corpus. Our data and code is available
at: https://github.com/kumar-shridhar/LongtoNotes
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing
Cross-lingual semantic parsing transfers parsing capability from a
high-resource language (e.g., English) to low-resource languages with scarce
training data. Previous work has primarily considered silver-standard data
augmentation or zero-shot methods, however, exploiting few-shot gold data is
comparatively unexplored. We propose a new approach to cross-lingual semantic
parsing by explicitly minimizing cross-lingual divergence between probabilistic
latent variables using Optimal Transport. We demonstrate how this direct
guidance improves parsing from natural languages using fewer examples and less
training. We evaluate our method on two datasets, MTOP and MultiATIS++SQL,
establishing state-of-the-art results under a few-shot cross-lingual regime.
Ablation studies further reveal that our method improves performance even
without parallel input translations. In addition, we show that our model better
captures cross-lingual structure in the latent space to improve semantic
representation similarity.Comment: Accepted to TACL 2023. Pre-MIT Press publication. 17 pages, 3
figures, 6 table
Knowledge Graph Reasoning over Entities and Numerical Values
A complex logic query in a knowledge graph refers to a query expressed in
logic form that conveys a complex meaning, such as where did the Canadian
Turing award winner graduate from? Knowledge graph reasoning-based
applications, such as dialogue systems and interactive search engines, rely on
the ability to answer complex logic queries as a fundamental task. In most
knowledge graphs, edges are typically used to either describe the relationships
between entities or their associated attribute values. An attribute value can
be in categorical or numerical format, such as dates, years, sizes, etc.
However, existing complex query answering (CQA) methods simply treat numerical
values in the same way as they treat entities. This can lead to difficulties in
answering certain queries, such as which Australian Pulitzer award winner is
born before 1927, and which drug is a pain reliever and has fewer side effects
than Paracetamol. In this work, inspired by the recent advances in numerical
encoding and knowledge graph reasoning, we propose numerical complex query
answering. In this task, we introduce new numerical variables and operations to
describe queries involving numerical attribute values. To address the
difference between entities and numerical values, we also propose the framework
of Number Reasoning Network (NRN) for alternatively encoding entities and
numerical values into separate encoding structures. During the numerical
encoding process, NRN employs a parameterized density function to encode the
distribution of numerical values. During the entity encoding process, NRN uses
established query encoding methods for the original CQA problem. Experimental
results show that NRN consistently improves various query encoding methods on
three different knowledge graphs and achieves state-of-the-art results
Entities with quantities : extraction, search, and ranking
Quantities are more than numeric values. They denote measures of the worldâs entities such as heights of buildings, running times of athletes, energy efficiency of car models or energy production of power plants, all expressed in numbers with associated units. Entity-centric search and question answering (QA) are well supported by modern search engines. However, they do not work well when the queries involve quantity filters, such as searching for athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. State-of-the-art systems fail to understand the quantities, including the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.). QA systems based on structured knowledge bases (KBs) also fail as quantities are poorly covered by state-of-the-art KBs. In this dissertation, we developed new methods to advance the state-of-the-art on quantity knowledge extraction and search.Zahlen sind mehr als nur numerische Werte. Sie beschreiben MaĂe von EntitĂ€ten wie die Höhe von GebĂ€uden, die Laufzeit von Sportlern, die Energieeffizienz von Automodellen oder die Energieerzeugung von Kraftwerken - jeweils ausgedrĂŒckt durch Zahlen mit zugehörigen Einheiten. EntitĂ€tszentriete Anfragen und direktes Question-Answering werden von Suchmaschinen hĂ€ufig gut unterstĂŒtzt. Sie funktionieren jedoch nicht gut, wenn die Fragen Zahlenfilter beinhalten, wie z. B. die Suche nach Sportlern, die 200m unter 20 Sekunden gelaufen sind, oder nach Unternehmen mit einem Quartalsumsatz von ĂŒber 2 Milliarden US-Dollar. Selbst moderne Systeme schaffen es nicht, QuantitĂ€ten, einschlieĂlich der genannten Bedingungen (weniger als, ĂŒber, etc.), der MaĂeinheiten (Sekunden, Dollar, etc.) und des Kontexts (200-Meter-Rennen, Quartalsumsatz usw.), zu verstehen. Auch QA-Systeme, die auf strukturierten Wissensbanken (âKnowledge Basesâ, KBs) aufgebaut sind, versagen, da quantitative Eigenschaften von modernen KBs kaum erfasst werden. In dieser Dissertation werden neue Methoden entwickelt, um den Stand der Technik zur Wissensextraktion und -suche von QuantitĂ€ten voranzutreiben. Unsere HauptbeitrĂ€ge sind die folgenden: âą ZunĂ€chst prĂ€sentieren wir Qsearch [Ho et al., 2019, Ho et al., 2020] â ein System, das mit erweiterten Fragen mit QuantitĂ€tsfiltern umgehen kann, indem es Hinweise verwendet, die sowohl in der Frage als auch in den Textquellen vorhanden sind. Qsearch umfasst zwei HauptbeitrĂ€ge. Der erste Beitrag ist ein tiefes neuronales Netzwerkmodell, das fĂŒr die Extraktion quantitĂ€tszentrierter Tupel aus Textquellen entwickelt wurde. Der zweite Beitrag ist ein neuartiges Query-Matching-Modell zum Finden und zur Reihung passender Tupel. âą Zweitens, um beim Vorgang heterogene Tabellen einzubinden, stellen wir QuTE [Ho et al., 2021a, Ho et al., 2021b] vor â ein System zum Extrahieren von QuantitĂ€tsinformationen aus Webquellen, insbesondere Ad-hoc Webtabellen in HTML-Seiten. Der Beitrag von QuTE umfasst eine Methode zur VerknĂŒpfung von QuantitĂ€ts- und EntitĂ€tsspalten, fĂŒr die externe Textquellen genutzt werden. Zur Beantwortung von Fragen kontextualisieren wir die extrahierten EntitĂ€ts-QuantitĂ€ts-Paare mit informativen Hinweisen aus der Tabelle und stellen eine neue Methode zur Konsolidierung und verbesserteer Reihung von Antwortkandidaten durch Inter-Fakten-Konsistenz vor. âą Drittens stellen wir QL [Ho et al., 2022] vor â eine Recall-orientierte Methode zur Anreicherung von Knowledge Bases (KBs) mit quantitativen Fakten. Moderne KBs wie Wikidata oder YAGO decken viele EntitĂ€ten und ihre relevanten Informationen ab, ĂŒbersehen aber oft wichtige quantitative Eigenschaften. QL ist frage-gesteuert und basiert auf iterativem Lernen mit zwei HauptbeitrĂ€gen, um die KB-Abdeckung zu verbessern. Der erste Beitrag ist eine Methode zur Expansion von Fragen, um einen gröĂeren Pool an Faktenkandidaten zu erfassen. Der zweite Beitrag ist eine Technik zur Selbstkonsistenz durch BerĂŒcksichtigung der Werteverteilungen von QuantitĂ€ten
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
The emergence of natural language processing has revolutionized the way users
interact with tabular data, enabling a shift from traditional query languages
and manual plotting to more intuitive, language-based interfaces. The rise of
large language models (LLMs) such as ChatGPT and its successors has further
advanced this field, opening new avenues for natural language processing
techniques. This survey presents a comprehensive overview of natural language
interfaces for tabular data querying and visualization, which allow users to
interact with data using natural language queries. We introduce the fundamental
concepts and techniques underlying these interfaces with a particular emphasis
on semantic parsing, the key technology facilitating the translation from
natural language to SQL queries or data visualization commands. We then delve
into the recent advancements in Text-to-SQL and Text-to-Vis problems from the
perspectives of datasets, methodologies, metrics, and system designs. This
includes a deep dive into the influence of LLMs, highlighting their strengths,
limitations, and potential for future improvements. Through this survey, we aim
to provide a roadmap for researchers and practitioners interested in developing
and applying natural language interfaces for data interaction in the era of
large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves
in the field of natural language processing and artificial intelligence, due to
their emergent ability and generalizability. However, LLMs are black-box
models, which often fall short of capturing and accessing factual knowledge. In
contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are
structured knowledge models that explicitly store rich factual knowledge. KGs
can enhance LLMs by providing external knowledge for inference and
interpretability. Meanwhile, KGs are difficult to construct and evolving by
nature, which challenges the existing methods in KGs to generate new facts and
represent unseen knowledge. Therefore, it is complementary to unify LLMs and
KGs together and simultaneously leverage their advantages. In this article, we
present a forward-looking roadmap for the unification of LLMs and KGs. Our
roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs,
which incorporate KGs during the pre-training and inference phases of LLMs, or
for the purpose of enhancing understanding of the knowledge learned by LLMs; 2)
LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding,
completion, construction, graph-to-text generation, and question answering; and
3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a
mutually beneficial way to enhance both LLMs and KGs for bidirectional
reasoning driven by both data and knowledge. We review and summarize existing
efforts within these three frameworks in our roadmap and pinpoint their future
research directions.Comment: 29 pages, 25 figure
Joint Approaches for Learning Word Representations from Text Corpora and Knowledge Bases
The work presented in this thesis is directed at investigating the possibility of combining text corpora and Knowledge Bases (KBs) for learning word representations. More speciïŹcally, the aim was to propose joint approaches that leverage the two types of resources for the purpose of enhancing the word meaning representations. The main research question to be answered was âIs it possible to enhance the word representations by jointly incorporating text corpora and KBs into the word representations learning process? If so, what are the aspects of word meaning that can be enhanced by combining those two types of resources? â. The primary contribution of the thesis is three main joint approaches for learning word representations: (i) Joint Representation Learning for Additional Evidence (JointReps), (ii) Joint Hierarchical Word Representation (HWR) and (iii) Sense-Aware Word Representations (SAWR). The JointReps was founded to improve the overall semantic representation of words. To this end, it sought additional evidence from a KB to the co-occurrence statistics in the corpus. In particular, JointReps enforced two words that are in a particular semantic relationship in the KB to have similar word representations. The HWR approach was then proposed to learn word representations in a speciïŹc order to encode the hierarchical information in a KB in the learnt representations. The HWR considered not only the hypernym relations that exist between words in a KB, but also contextual information in a text corpus. SpeciïŹcally, given a training corpus and a KB, HWR learnt word representations that simultaneously encoded the hierarchical structure in the KB as well as the co-occurrence statistics between pairs of words in the corpus. A particularly novel aspect of the HWR approach was that it exploits the full hierarchical path of words existing in the KB. The SAWR approach was then introduced to consider not only word representations but also the diïŹerent senses (diïŹerent meanings) associated with each word. The SAWR required the learnt representations to predict the word and the senses accurately. It learnt the sense-aware word representations jointly using both unlabelled and sense-labelled text corpora. The approaches were comprehensively analysed and evaluated in various standard and newly-proposed tasks using a wide range of benchmark datasets. The evaluation was conducted to compare the quality of the learnt word representations by the proposed approaches with word representations learnt by sole-resource baselines and previously proposed joint approaches in the literature. All the proposed joint approaches have proven to be eïŹective for enhancing the learnt word representations. More speciïŹcally, the proposed joint approaches were found to report signiïŹcant improvements over the approaches that use only one type of resources and the previously proposed joint approaches