=Paper=
{{Paper
|id=Vol-3884/paper12
|storemode=property
|title=Content-Based Dense Retrieval of Open Datasets
|pdfUrl=https://ceur-ws.org/Vol-3884/paper12.pdf
|volume=Vol-3884
|authors=Qiaosheng Chen
|dblpUrl=https://dblp.org/rec/conf/semweb/Chen24
}}
==Content-Based Dense Retrieval of Open Datasets==
https://ceur-ws.org/Vol-3884/paper12.pdf
Content-Based Dense Retrieval of Open Datasets
Qiaosheng Chen1
1
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Abstract
The rapid growth of open data has intensified the need for effective dataset search capabilities. This
research proposal focuses on enhancing dataset search through content-based dense retrieval, addressing
the limitations of current metadata-dependent systems. This research aims to tackle the challenges
of dataset size, heterogeneity, and the creation of a comprehensive test collection for evaluation. The
proposed research methods include data summarization techniques for large datasets and a unified
representation of heterogeneous data, which are inspired by research related to the Semantic Web.
Additionally, the research will explore a coarse-to-fine tuning strategy for dense retrieval models,
leveraging data augmentation through distant supervision and self-training. The evaluation plan involves
constructing a content-based test collection and comparing retrieval performance between metadata-only
and content-enhanced approaches. The expected outcome is the development of effective content-based
dataset search solutions, ultimately improving data findability.
Keywords
Dataset Search, Dense Retrieval, Open Data
1. Introduction
The availability and significance of open data have led to a surge in interest and reliance on
dataset search within the field of information retrieval [1]. However, represented by Google
Dataset Search [2], existing approaches and systems predominantly rely on metadata (descriptive
text for dataset, such as title, description), which often suffers from low quality and limited
availability. These metadata-based approaches have posed shortage in accurately capturing the
relevance of datasets. For addressing the gap between usersβ real data needs and the quality of
dataset metadata, it necessitates a shift towards content-based approaches that can effectively
harness the richness of dataset content [3, 4].
On the other hand, dense retrieval models, which have become mainstream in the field
of document retrieval [5], have not yet been fully explored in the field of dataset search. In
particular, how to apply dense retrieval models to content-based dataset search problems still
faces many challenges. First, the large size of dataset content poses computational challenges,
especially when it exceeds the processing capacity of standard dense retrieval models which are
mainly based on pre-trained language models (PLMs). Additionally, the heterogeneity of dataset
content, spanning various data formats and domains [6], further complicates the development
of unified content-based search solutions.
Proceedings of the Doctoral Consortium at ISWC 2024, co-located with the 23rd International Semantic Web Conference
(ISWC 2024)
Envelope-Open qschen@smail.nju.edu.cn (Q. Chen)
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The proposed research aims to contribute towards the development of robust and effective
content-based solutions for dataset search, ultimately improving the findability and reusability of
open datasets. Users across various domains, including researchers, data scientists, policymakers,
and businesses, will benefit from content-based dataset search, while professional researchers
in fields such as information retrieval, natural language processing (NLP), and machine learning
are particularly invested in its advancement. Industries relying heavily on data-driven decision-
making, such as healthcare, finance, agriculture, and environmental science, should also care
about its development. Beyond the domain of information retrieval, this research involves
technologies relevant to the Semantic Web and Knowledge Graph (KG). RDF datasets represent
a significant part of open data. Moreover, employing ontologies or KGs as a framework can
aid in analyzing the content of open datasets and processing heterogeneou data from a unified
perspective. The advancement of dataset search also stands to catalyze the realization of findable,
accessible, interoperable, and reusable (FAIR) open data within the Semantic Web community.
2. Related Work
In this section, we review recent advancements in dataset search and dense retrieval, highlighting
limitations of current dataset search methods and examining strengths of dense retrieval
techniques.
2.1. Dataset Search
Dataset search has garnered increasing attention with the proliferation of diverse and volumi-
nous datasets, prompting the development of search approaches and systems [1, 7]. Notably,
Google Dataset Search [2] has paved the way as a pioneering dataset search engine, enabling
keyword retrieval over published metadata of Web datasets. However, its reliance on metadata
limits its effectiveness in supporting queries oriented towards dataset content. Moreover, exist-
ing dataset retrieval test collections [8, 9, 10] primarily depend on metadata annotations during
construction, resulting in a lack of evaluation benchmarks for content-based dataset search.
Recent studies have highlighted the importance of integrating considerations for dataset
content to enhance search effectiveness. Ota et al. [11] utilized value co-occurrence information
within tabular datasets to infer attribute domains, while Chen et al. [12] proposed a BERT-based
ranking model for table retrieval, focusing on selecting the most salient table items as represen-
tatives of the entire dataset. StruBERT [13] introduced a structure-aware BERT model to capture
both structural and textual information of tabular datasets. Moreover, existing tabular dataset
or RDF dataset search systems such as Auctus [14], LODAtlas [15], and CKGSE [16] leverage
dataset content to augment retrieval capabilities and enhance user search experiences. However,
these efforts primarily focus on single-format data, such as tabular or RDF data, overlooking the
challenges posed by multi-format datasets.
2.2. Dense Retrieval
Recent advancements in dense retrieval have been significantly influenced by the incorporation
of PLMs, which have demonstrated remarkable capabilities in capturing semantic nuances
within text [5]. This approach, often referred to as dense retrieval, leverages the dense vector
representations (embeddings) of text to facilitate semantic matching between queries and
documents. Notably, Karpukhin et al. [17] presented dense passage retrieval (DPR) for open-
domain question answering, highlighting the effectiveness of PLMs in this context. Their work
has been seminal in shaping subsequent research. The concept of using multiple representations
for improved text encoding has been explored by Humeau et al. [18] through their poly-encoder
architectures, which allow for richer semantic interactions between queries and texts. The
challenge of training efficient and robust dense retrievers has been addressed by various work.
For instance, Gao and Callan [19] introduced Condenser, a pre-training architecture specifically
designed to improve dense retrieval. Nogueira et al. [20] demonstrated the effectiveness of multi-
stage document ranking using BERT, showcasing how PLMs can be effectively integrated into
reranking stage. Furthermore, ColBERT [21] has provided insights into efficient and effective
passage search through contextualized late interaction over BERT. Most of the current dense
retrieval methods focus on retrieval of text documents or passages, whereas the structured content
of datasets requires new dense model structures or retrieval strategies. The large data size and
complex heterogeneity also make it difficult to directly treat the dataset content as plain text.
3. Problem Statement
In this section, we discuss the typical composition of datasets, outline the problem of content-
based dataset search, and formulate hypotheses and research questions derived from our
investigation.
To clarify the distinction between dataset search and general document search, we first
introduce the composition of a dataset, which consists of the following two parts:
β’ Metadata: This part includes descriptive fields provided by the dataset publisher, such as
title, description, publishing organization, and other useful information about the dataset.
β’ Data Files: A dataset consists of various data files, potentially in different formats. This
research only considers textual data files, including unstructured TXT, PDF, and DOC
files, as well as structured files such as graph data (RDF, OWL), tabular data (CSV, XLS),
and key-value pair data (JSON, XML). Images (JPEG, PNG), videos (AVI, MPG), audios
(WAV, MP3), and other non-textual formats are excluded from the scope of this research.
The research focuses on ad hoc dataset retrieval [8, 3], the foundational form of dataset
search. This process involves retrieving, from a collection π· of datasets, a ranked list of
datasets β¨π1 , π2 , β¦β© that are most relevant to a keyword query π. The relevance assessment
between query π and each dataset π β π· is conducted independently of other datasets π β² β π·,
where π β π β² . The primary objective is to compute the relevance score of each dataset π β π·
to a given keyword query π. The prevalent dense retrieval paradigm typically employs a PLM
as an encoder πΈ(β
) to encode a dataset π and a query π into vectors vπ and vπ respectively.
Subsequently, it computes the similarity score between these vectors to gauge the relevance
of π to π.
According to studies on metadata quality [22, 23], the metadata of open datasets on the Web
often lacks guaranteed quality and is underutilized by both publishers and users. Meanwhile,
dense retrieval models based on PLMs have exhibited increasingly powerful text understanding
capabilities with advancements in NLP [5]. Hence, the application of dense retrieval models in
dataset search becomes imperative. Based on these findings, we propose the following main
hypothesis and research question:
Hypothesis. Dataset metadata quality often varies and may not fully describe the content.
Users frequently seek information from the actual data files, and content-focused queries may
not align well with the available metadata.
RQ0. To what extent can content-based dense dataset retrieval methods outperform tradi-
tional metadata-centered approaches?
Building upon the hypothesis and RQ0, this research investigates the application of dense
models to content-based dataset retrieval. Nonetheless, representing and indexing complex
dataset content with PLM-based dense models presents substantial challenges. To address these
challenges, we decompose RQ0 into the following four specific research questions:
RQ1. How to overcome the challenge presented by the extensive size of dataset content,
especially when it exceeds the processing capacity of dense retrieval models?
RQ2. How to address the heterogeneity of dataset content, encompassing variations in
formats?
RQ3. How to develop a dataset retrieval test collection which considers the content of
datasets, rather than annotated solely relies on metadata?
RQ4. How to enhance the size and quality of existing public dataset retrieval test collections,
particularly in terms of providing sufficient training data for dense retrieval models?
4. Research Methods
In this section, we provide a detailed and systematic research methodology that outlines how
we address each research question (RQ1-RQ4) and validate our hypotheses [3, 24, 25, 26]. The
methodology is structured to ensure a comprehensive and coherent approach to solving the
challenges of content-based dense retrieval of open datasets.
RQ1. To overcome the challenge posed by the large size of dataset content that exceeds
the input capacity of PLMs, this research proposed an approach involving the extraction of
a data summary for each dataset. Starting with RDF datasets, we introduced a technique to
handle large RDF datasets by extracting a compact, representative subset of RDF triples [25].
This subset was selected to preserve the semantic integrity of the dataset and was used to
create a document representation that fits within the token limit of dense ranking models.
We employed two of the existing static RDF dataset summarization methods, IlluSnip [27, 28]
selecting top-ranked RDF triples covering the most frequent classes, properties, and entities,
and PCSG [29] extracting a connected subgraph from an RDF graph covering as many data
patterns as possible. Furthermore, we proposed a dynamic data summary extraction method
for dataset search, selecting compact data snippets of appropriate size that are relevant to
the user query [26]. By integrating these methods, one can create a compact, semantically
representative, and query-biased data summary of the original dataset. This enables the use of
PLMs for tasks such as dense dataset retrieval, where the models can process the summarized
data to understand and rank datasets based on their relevance to user queries without being
hindered by size limitations.
RQ2. We address the challenge of heterogeneity in dataset content by transforming data
from various formats into a unified representation. The method establishes mapping rules for
structured data, such as graph data, tabular data, and key-value pair data. These rules convert
the heterogeneous data into unified data chunks. Each data chunk is modeled as a set of data
triples, which consist of a subject, a predicate, and an object. This triple-structured format allows
for uniform processing of all datasets, regardless of their original format. Converting different
data formats into unified data chunks creates a consistent input for dense ranking models. This
approach allows for the exploitation of heterogeneous data in dataset ranking, overcoming the
limitations imposed by the diverse formats of open data. The summarized data chunks can then
be used to rank datasets based on their relevance to a given query, thus enhancing the search
accuracy and making the process more efficient. To ensure that the structural information is not
lost during the conversion process, the mapping rules we employed preserve the hierarchical and
relational aspects of the original data. For graph data, we maintain the relationships between
nodes and edges by representing them as triples. For tabular data, we preserve the row-column
structure by mapping rows to subjects and columns to predicates. For key-value pair data, we
maintain the key-value relationships by representing keys as predicates and values as objects.
This approach ensures that the structural integrity of the original data is preserved, which is
beneficial for accurate retrieval. Additionally, we conduct experiments to evaluate the impact
of content on retrieval performance, providing insights into the importance of preserving this
information during the conversion process.
RQ3. We released a content-based RDF dataset retrieval test collection ACORDAR [3], and
subsequently enhanced it to build ACORDAR 2.0 [24]. Constructing this content-based dataset
retrieval test collection began with the collection of RDF datasets from various open data portals,
ensuring a diverse and representative sample. Keyword queries were then formulated, either by
analyzing user needs or through crowd-sourcing, resulting in a set of search terms that reflected
actual information demands. To accommodate the complexity and size of datasets, a dashboard
was developed to assist annotators in browsing and understanding the content of datasets.
This tool was crucial for creating content-oriented queries and making informed relevance
judgments. Annotators used the dashboard to analyze datasets and generate queries that capture
the datasetβs essence. These queries were then used to pool potentially relevant datasets, which
were subsequently annotated for relevance. The pooling process was done using both sparse and
dense retrieval models to ensure a broad coverage of potential matches. Relevance judgments
were made on a graded scale, with annotators assessing the degree to which each dataset met
the queryβs requirements. To ensure quality, annotations involved multiple annotators and a
validation process. ACORDAR 2.0 was further enriched by transforming keyword queries into
question-style queries using a large language model (LLM), which increased the diversity of the
queries and simulates more natural information-seeking behavior. Our test collection provides
a benchmark for evaluating content-based dataset retrieval systems.
RQ4. To address the challenge of limited large labeled datasets necessary for training dense
retrieval models, we proposed a coarse-to-fine tuning strategy [25]. This strategy involved an
initial coarse-tuning phase with weak supervision obtained from a large set of automatically
generated queries and relevance labels. It incorporated two data augmentation methods: distant
supervision and self-training. In the distant supervision method, the title of each dataset served
as a query, and the metadata document was assumed to be relevant to this query, thereby
generating numerous labeled examples. Meanwhile, the self-training method employed dataset-
to-query generators trained on labeled data to generate queries from unlabeled data, further
expanding the datasets for training dense models.
This systematic methodology ensures that each research question is addressed with a clear
and structured approach, leading to the validation of our hypotheses and the development of
effective content-based dataset search solutions.
5. Evaluation
The evaluation plan for this research involves constructing content-based dataset retrieval
test collections [3, 24] following the methodology outlined in Section 4. Dataset retrieval
and reranking experiments will be conducted on these test collections, as well as on existing
public dataset retrieval test collections [8]. Performance will be assessed using commonly used
information retrieval metrics such as Recall, Normalized Discounted Cumulative Gain (NDCG),
and Mean Average Precision (MAP). The primary objectives of these experiments are as follows:
1. To compare the retrieval performance using solely metadata against retrieval using
metadata combined with content.
2. To assess the performance disparity between dense retrieval models and traditional sparse
retrieval models in the dataset search scenario.
3. To analyze the impact of various data summarization methods for representing data
content in dataset retrieval.
4. To investigate the effectiveness of different query types and characteristics in both
metadata-based and content-based retrieval methods.
Comparison of Metadata-Only vs. Content-Enhanced Retrieval. We will conduct
experiments to compare the retrieval performance of systems that use only metadata against
those that combine metadata with content. This analysis will assess the extent to which content-
based retrieval improves search accuracy and relevance. We will examine performance metrics
across various dataset types and query scenarios to identify specific cases where content-based
retrieval provides significant advantages.
Performance Disparity Between Dense and Sparse Retrieval Models. We will compare
the performance of dense retrieval models, which use PLMs, with traditional sparse retrieval
models like BM25, which rely on term frequency-based scoring. This evaluation will highlight
the strengths and limitations of dense retrieval models in dataset search. By analyzing their
performance across diverse query types and datasets, we aim to identify scenarios where dense
models excel, particularly in capturing semantic relevance, versus scenarios where sparse
models may be more effective.
Impact of Data Summarization Methods. The role of data summarization methods in
improving retrieval performance will be analyzed by testing both static techniques, such as
IlluSnip and PCSG, and dynamic methods, which generate query-biased summaries. We will
evaluate how these summarization approaches influence the relevance and efficiency of dataset
retrieval. Additionally, we will explore the trade-offs between summarization quality and
computational cost, providing insights into balancing performance with resource demands.
Query Type and Characteristic Analysis. A detailed examination of different query types
and their characteristics will be conducted to understand their effectiveness in metadata-based
and content-based retrieval methods. We hypothesize that specific queries requiring detailed
content comprehension or precision may benefit more from content-based retrieval. On the
other hand, more general queries or those that can be effectively addressed with metadata alone
may exhibit similar performance across both approaches. This analysis will help refine retrieval
strategies based on query requirements.
In addition, given that the eventual deployment of this work is envisioned in real-world
dataset search applications, it is imperative to evaluate the time efficiency and additional
space requirements of modules such as data summarization and dense retrieval models when
processing real-world open datasets.
6. Conclusion and Future Work
The research proposal on content-based dense retrieval of open datasets is crucial in navigating
the vast landscape of available data resources. By shifting the focus from metadata to the actual
content of datasets, we can enhance search accuracy, ultimately facilitating more informed
decision-making in data discovery. The long-term value of this research lies in its potential to
streamline access to diverse datasets, empowering researchers, businesses, and policymakers
with valuable insights.
6.1. Limitations and Challenges
Content-based dataset retrieval systems face several limitations and challenges. Time efficiency
is a critical issue, as dense retrieval models and summarization techniques require significant
computational resources, particularly when processing large datasets. Storage requirements are
another concern, as pre-trained language models and their embeddings demand substantial space,
making deployment difficult in resource-constrained environments. The heterogeneity and
complexity of dataset formats further complicate retrieval, as it is challenging to develop unified
solutions that preserve both structural and semantic information. Evaluation is also problematic,
as constructing comprehensive and realistic test collections that reflect real-world scenarios is
complex yet crucial for assessing system performance. Finally, query understanding remains a
persistent challenge, particularly for complex queries that require detailed comprehension of
dataset content to map them effectively to relevant datasets.
6.2. Future Research Directions
Future research will focus on several directions to overcome these challenges and enhance
dataset retrieval systems. Integrating LLMs into dataset search pipelines offers the potential to
improve both accuracy and efficiency, with planned evaluations to quantify their impact on
performance metrics. Explainable data summarization techniques will be explored to provide
transparent insights into the generation of data summaries and the rationale behind dataset
rankings, fostering trust and usability. Methods for content pattern analysis will be developed
to identify and utilize patterns within dataset content, improving retrieval accuracy. Expanding
the scope to multi-modal retrieval will address the need to handle diverse data types, including
images, videos, and audio, efficiently and at scale. Additionally, real-world deployment of these
systems will be prioritized to evaluate scalability and gather user feedback, guiding further
refinements and optimizations.
Acknowledgments
The author would like to express his thanks to his supervisor Prof. Gong Cheng for providing
helpful suggestions and comments. This work was supported by the NSFC (62072224).
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