69 research outputs found
Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrieval
Information retrieval (IR) methods for KGQA consist of two stages: subgraph
extraction and answer reasoning. We argue current subgraph extraction methods
underestimate the importance of structural dependencies among evidence facts.
We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural
dependencies during subgraph extraction. We implement EPR by indexing the
atomic adjacency pattern of resource pairs. Given a question, we perform dense
retrieval to obtain atomic patterns formed by resource pairs. We then enumerate
their combinations to construct candidate evidence patterns. These evidence
patterns are scored using a neural model, and the best one is selected to
extract a subgraph for downstream answer reasoning. Experimental results
demonstrate that the EPR-based approach has significantly improved the F1
scores of IR-KGQA methods by over 10 points on ComplexWebQuestions and achieves
competitive performance on WebQuestionsSP.Comment: Accepted to TheWebConf'24 (WWW 2024). This is a preprint version; the
CR version will include more details. Github:
https://github.com/nju-websoft/EPR-KGQ
Automatic Rule Generation for Time Expression Normalization
The understanding of time expressions includes two sub-tasks: recognition and
normalization. In recent years, significant progress has been made in the
recognition of time expressions while research on normalization has lagged
behind. Existing SOTA normalization methods highly rely on rules or grammars
designed by experts, which limits their performance on emerging corpora, such
as social media texts. In this paper, we model time expression normalization as
a sequence of operations to construct the normalized temporal value, and we
present a novel method called ARTime, which can automatically generate
normalization rules from training data without expert interventions.
Specifically, ARTime automatically captures possible operation sequences from
annotated data and generates normalization rules on time expressions with
common surface forms. The experimental results show that ARTime can
significantly surpass SOTA methods on the Tweets benchmark, and achieves
competitive results with existing expert-engineered rule methods on the
TempEval-3 benchmark.Comment: Accepted to Findings of EMNLP 202
PaTeCon: A Pattern-Based Temporal Constraint Mining Method for Conflict Detection on Knowledge Graphs
Temporal facts, the facts for characterizing events that hold in specific
time periods, are attracting rising attention in the knowledge graph (KG)
research communities. In terms of quality management, the introduction of time
restrictions brings new challenges to maintaining the temporal consistency of
KGs and detecting potential temporal conflicts. Previous studies rely on
manually enumerated temporal constraints to detect conflicts, which are
labor-intensive and may have granularity issues. We start from the common
pattern of temporal facts and constraints and propose a pattern-based temporal
constraint mining method, PaTeCon. PaTeCon uses automatically determined graph
patterns and their relevant statistical information over the given KG instead
of human experts to generate time constraints. Specifically, PaTeCon
dynamically attaches class restriction to candidate constraints according to
their measuring scores.We evaluate PaTeCon on two large-scale datasets based on
Wikidata and Freebase respectively. The experimental results show that
pattern-based automatic constraint mining is powerful in generating valuable
temporal constraints.Comment: Accepted by AAAI2
Question Decomposition Tree for Answering Complex Questions over Knowledge Bases
Knowledge base question answering (KBQA) has attracted a lot of interest in
recent years, especially for complex questions which require multiple facts to
answer. Question decomposition is a promising way to answer complex questions.
Existing decomposition methods split the question into sub-questions according
to a single compositionality type, which is not sufficient for questions
involving multiple compositionality types. In this paper, we propose Question
Decomposition Tree (QDT) to represent the structure of complex questions.
Inspired by recent advances in natural language generation (NLG), we present a
two-staged method called Clue-Decipher to generate QDT. It can leverage the
strong ability of NLG model and simultaneously preserve the original questions.
To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA
system called QDTQA. Extensive experiments show that QDTQA outperforms previous
state-of-the-art methods on ComplexWebQuestions dataset. Besides, our
decomposition method improves an existing KBQA system by 12% and sets a new
state-of-the-art on LC-QuAD 1.0.Comment: Accepted by AAAI202
Summarizing Entity Descriptions for Effective and Efficient Human-centered Entity Linking
Entity linking connects the Web of documents with knowl-edge bases. It is the task of linking an entity mention in text to its corresponding entity in a knowledge base. Where-as a large body of work has been devoted to automatically generating candidate entities, or ranking and choosing from them, manual efforts are still needed, e.g., for defining gold-standard links for evaluating automatic approaches, and for improving the quality of links in crowdsourcing approaches. However, structured descriptions of entities in knowledge bases are sometimes very long. To avoid overloading hu-man users with too much information and help them more efficiently choose an entity from candidates, we aim to sub-stitute entire entity descriptions with compact, equally effec-tive structured summaries that are automatically generated. To achieve it, our approach analyzes entity descriptions in the knowledge base and the context of entity mention from multiple perspectives, including characterizing and differen-tiating power, information overlap, and relevance to contex-t. Extrinsic evaluation (where human users carry out entity linking tasks) and intrinsic evaluation (where human user-s rate summaries) demonstrate that summaries generated by our approach help human users carry out entity linking tasks more efficiently (22–23 % faster), without significant-ly affecting the quality of links obtained; and our approach outperforms existing approaches to summarizing entity de-scriptions
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