Page 164 - Journal of Library Science in China, Vol.47, 2021

NIU Li, GAO Chenxiang, ZHANG Yufeng, YAN Shi, XU Yongjun & LI Anrunze / Discovering, reorganizing 163
and storytelling: paths and methods of archives research on the perspective of digital humanities

structure or different structures to form a knowledge network of a specific theme [27] . However,
the two-dimensional network knowledge organization form cannot realize a multi-dimensional
mutual verification of basic attributes such as the integrity and credibility of archive data, and it is
difficult to reveal the characteristics of archive resources from different angles. Therefore, “archive
reorganizing” from DH perspective needs to adopt a multi-dimensional organization model that
combines static association and dynamic aggregation.
(1) Static association. It is the main way to statically associate archive data by using knowledge
organization models such as domain ontology to describe, organize and standardize archival data,
forming associated data that can be exchanged, mapped and inter-operated on various platforms
of data of different structures under a specific framework. The archival data obtained from
the “discovering” is still relatively isolated memory entities, which need to be associated and
structured by the domain ontology. The people, events, times, places, physical entities contained
in the archival data and related sources should be regarded as meaningful memory entities, and
the semantic relationships between entities should be established in the form of triples through
the object properties of the ontology. Since the categories and properties of the domain ontology
are often constructed by domain experts, the structure and application method are relatively
stable once determined, and it is not easy for the entire framework to change disruptively, as it
follows the restrictions and specifications of the OWL language. Therefore, we call the association
structure generated by the organization of archival data through the domain ontology “static
association”, in order to express the transition of archival resources from “archival data” to “archival
associated data”, which makes it possible to conduct the unstructured storage, open organization
and associative publication of archival data.
(2) Dynamic aggregation. Compared with the static association of archival data relying on
the ontology model construction, dynamic aggregation focuses more on revealing the essential
attributes of archival resources from different dimensions, highlighting the difference between
archival data research and other types of data research from DH perspective. The “dynamic
aggregation” framework of archival data in this paper also requires the intervention of the
ontology model, but the framework needs to be built top-down and bottom-up. First, it is necessary
to build a top-down knowledge model, forming a multi-dimensional knowledge organization
structure by establishing a “dimension model”. The “dimensional model” here mainly refers to
the multi-dimensional classification system related to memory entities, which reorganizes the
discrete, qualitative attributes that describe an entity. For example, the concept of “time” can be
understood from different perspectives such as the era of the Common Era, feudal dynasties or
other calendars with different attribute expressions while examples of feudal dynasties are some
discrete, qualitative character values. Secondly, it is necessary to extract and fill the bottom-up
instance extraction and filling process, that is, extracting the different attributes of the “discovering”
memory entities in different dimensions and perspectives and filling them into the “dimension set”
directly related to a concept. The conceptual instances of these dimensions can be preferentially