=Paper=
{{Paper
|id=Vol-2262/ekaw-demo-12
|storemode=property
|title=Unified Workbench for Knowledge Graph Management
|pdfUrl=https://ceur-ws.org/Vol-2262/ekaw-demo-12.pdf
|volume=Vol-2262
|authors=Ryutaro Ichise,Natthawut Kertkeidkachorn,Lihua Zhao,Esrat Farjana Rupu
|dblpUrl=https://dblp.org/rec/conf/ekaw/IchiseKZR18
}}
==Unified Workbench for Knowledge Graph Management==
https://ceur-ws.org/Vol-2262/ekaw-demo-12.pdf
Unified Workbench
for Knowledge Graph Management
Ryutaro Ichise1,2 , Natthawut Kertkeidkachorn2,1 ,
Lihua Zhao2,1 , and Esrat Farjana Rupu1
1
National Institute of Informatics, Tokyo 101-8430, Japan
{ichise,esrat farjana}@nii.ac.jp
2
National Institute of Advanced Industrial Science and Technology,
Tokyo 135-0064, Japan
{n.kertkeidkachorn, lihua.zhao}@aist.go.jp
Abstract. Knowledge Graphs become essential knowledge resources for
AI applications. Nevertheless, massive real-world knowledge is produced
every day. Ignoring new knowledge greatly affects the outcomes of an
application due to missing or inaccurate knowledge. To deal with the
new knowledge and its life cycle, the holistic framework for curating
and manipulating knowledge is needed. In this study, we present our so-
lution, namely Unified Workbench for Knowledge Graph Management
(UWKGM), that unifies several technologies to deal with knowledge cu-
ration and manipulation in a knowledge graph. Also, We demonstrate
some example cases of this framework in use.
1 Introduction
Many AI related tasks, e.g. question and answering systems, entity resolution
systems, and information retrieval systems, widely use knowledge graphs (KGs)
as knowledge resources. Consequently, KGs are increasingly in demand. However,
curating and manipulating KGs requires huge efforts. Based on the analysis on
KGs, we found that there are three major problems: 1) Adding New Knowledge,
2) Erroneous Knowledge Injection and 3) Inadequate Knowledge. Adding new
knowledge is the most important issue because new knowledge, by nature, is
produced every day; in consequence, it is beyond the human effort to deal with.
Indeed, the entities and relations contained in KGs are usually dynamically
added rather than static. Hence, allowing a new entity or a relation to be added to
KGs is required. Erroneous knowledge injection is another problem that degrades
the quality of KGs. This problem involves the injection of erroneous knowledge
into KG. It is impossible to completely eliminate erroneous knowledge in KGs
because erroneous knowledge may be included by accident, e.g. users attempt
to create new knowledge or an automated KGs population. Using low-quality
KGs may trigger the malfunction on AI applications. Verification and validation
on KGs should be considered. Inadequate knowledge is the result of incomplete
knowledge resources. It is impossible to prepare all the necessary information in
advance. Reasoning necessary information at the time becomes a key to perform
actual AI applications on incomplete KGs. Completing missing knowledge is
therefore also a key for manipulating KGs.
2 R. Ichise, et al.
Fig. 1. UWKGM architecture
As discussed those three major problems on KGs above, the holistic frame-
work for curating and manipulating knowledge is needed. In this paper, we
therefore introduce our on-going framework, Unified Workbench for Knowledge
Graph Management (UWKGM), which integrates many technologies in order to
address three problems above. Also, use-cases and achievements of UWKGM are
demonstrated.
2 Unified Workbench for Knowledge Graph Management
UWKGM consists of four main components: 1) Relation Extraction (RE), 2)
Ontology Integration (OI), 3) Knowledge Verification (KV) and 4) Knowledge
Completion (KC), as shown in Fig. 1. RE and OI aim to solve the adding new
knowledge problem. KV is to deal with the erroneous knowledge injection prob-
lem. KC copes with the inadequate knowledge problem. The details of compo-
nents and their implementation are as follows.
Relation Extraction is a component to retrieve relationships between enti-
ties as triples from unstructured data, particularly text. There are two modules:
OpenIE and Coreference Resolution. Open Information Extraction (OpenIE)
finds and extracts the relation between two entities as a triplet, while Corefer-
ence Resolution unifies surface forms of an entity into one representation. In ad-
dition, the framework allows two modules to employ bootstrapping data curated
by domain experts in order to enhance results when creating triples. Currently,
Stanford OpenIE and Coreference Resolution are implemented by Stanford Open
Information Extraction [1]. Our configuration follows T2KG [3].
Ontology Integration is a component to serialize text triples to RDF stan-
dard triples and also integrate RDF triples to existing KGs. There are two mod-
ules: ontology learning and ontology matching. Ontology learning is to learn
Unified Workbench for Knowledge Graph Management 3
and populate (extend) the ontology, while ontology matching is to reuse classes,
attributes, relations (T-Box) and individuals (A-Box) in existing KGs. When
the ontology used in the KGs is inapplicable, the module is capable of modi-
fying the ontology to accommodate the new triples. This component relies on
both T2KG [3] and FITON [6]. The preliminary results of UWKGM on the re-
lation extraction together with the ontology integration was reported in T2KG
framework [3]
As presented in T2KG [3], RE and OI can solve the adding new knowledge
problem. Concretely, not only non-existing facts in KGs can be discovered but
also a new entity and a new property (as a relation) can be populated.
Knowledge Verification is to verify and validate RDF triples before pub-
lishing to KGs. Two modules in this component are error detection and error
correction. The error detection module is to detect erroneous triples by using
constraints in the ontology or analyzing the patterns of triples, such as value
range. The error correction module is to correct erroneous triples by finding the
most suitable replacement entities. The error detection module utilizes the stud-
ies [5, 4], while the error correction module is implemented based on FIXRVE [4].
As shown in the studies [5, 4], the erroneous triples are detected and corrected;
in consequence, the erroneous knowledge injection can be avoid and fixed.
Knowledge Completion is to learn the embedding representations and
perform inference over the existing KGs to discover missing knowledge. In this
component, there are two modules: knowledge embedding and link prediction.
the knowledge embedding is to learn entities and relations in KGs as vector
representations in the low-dimensional space. Such representations can be used
in many applications, e.g. fact-checking. The link prediction module is to predict
the missing relationship between entities in the KGs. We implement these two
modules by TorusE [2]. In TorusE [2], the missing knowledge is discovered and
KGs is enriched; as a result, the inadequate knowledge problem is alleviated.
3 Use Cases and Demonstration
To demonstrate the capability of our UWKGM framework, four main use cases
are presented as follows. Also, some demonstration videos and supplementary
materials are available at http://ri-www.nii.ac.jp/UWKGM/ .
KG Construction: The first UWKGM use-case focuses on transforming
text data extracted from any domain into the KGs. In this use case, RE cre-
ates triples from texts. After that, OI integrates those text triples into KGs. A
successful example of this specific UWKGM use-case is described in T2KG [3].
To date, we have used T2KG to transform more than 100,000 unstructured text
articles into text triples and have integrated those triples into KGs using the
ontology learning module.
KG Population: The second UWKGM use-case deals with populating the
new knowledge to the KG. There are two minor use-cases: 1) external resource-
based population, and 2) internal resource-based population. In external resource-
based population, the ontology matching module in OI is used to merge new
knowledge to the KG. One example of the external resource-based population
4 R. Ichise, et al.
is T2KG, in which KG triples are integrated under DBpedia ontology [3]. In an
internal resource-based population, KC is employed to predict new knowledge
from the KG. A recent specific use-case of this type is TorusE, in which KG
triples are used to predict new relationships between entities [2].
KG Revision: The third use-case demonstrate how to alleviate errors by
revising the KG. In UWKGM, KV is the key module for handling such use-
cases. Currently, a well-performed example of this use-case is FIXRVE [4], in
which a pre-defined ontology is used to find and to resolve incorrect triples by
using entity profiles.
KG Embedding: The fourth use-case is to learn embedding representations
of entities and relations in KGs. In the knowledge embedding module of KC, the
embedding representations are learned by the translation-based model, TorusE
[2]. We provide RESTful API to retrieve vector representations of the entities
and the relations in KGs for using other applications, e.g. link prediction in the
study [2].
4 Conclusion
In this paper, we discussed the problem on curating and manipulating KGs.
Based on our analysis on the problem, we then proposed our on-going framework,
called Unified Workbench for Knowledge Graph Management (UWKGM), and
illustrated its use-cases and achievements. We are currently planning to release
the framework to the public in the future. Our current demo video is available
on http://ri-www.nii.ac.jp/UWKGM/.
Acknowledgment
This work was partially supported by the New Energy and Industrial Technology
Development Organization (NEDO).
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