In this paper, we study the problem of analyzing the relationship between data given in a tabular format and a target knowledge graph, e.g., Wikidata. It is most important to find the label that indicates the correct meaning in Wikidata where data and values are annotated with each label. It is a very difficult task for a machine to correctly understand or infer its meaning. For this to happen, data must be accurately tagged. Wikidata has a label for each document. In addition, it has the characteristic of being linked to another document through these documents. These connected data can be represented as graphs. In this paper, a method is proposed to create a graph based on related elements and infer the relationship of other elements using advanced Wikidata SPARQL queries. Above all, this approach helps in interpreting clear inference relationships and provides a very suitable approach in an environment where data changes frequently such as an open access database.
Annotating data is one of the important tasks in tabular data. Because other information
can be inferred without requiring a lot of information due to accurate annotation.
Therefore, putting an appropriate annotation can be considered as knowing the semantics. In
that sense, it is very important to find out about the meaning in a tabular knowledge
graph. Because fallacy reasoning can lead to another fallacy reasoning in a data
processing pipeline. Eventually, fallacy inferences from one can spread throughout. The
data we used were based on Wikidata. Wikidata is composed of several facts consisting
of subject (S), predicate (P) and object (O). Each element is marked with the label in
Wikidata. This makes it possible to identify the semantics in Wikidata [
In the SemTab challenge, three tasks were given: CTA, CEA, and CPA [
CTA
CEA
CPA CTA : Assigning a semanticFitgy.p1e. tCohaallceonlguems nin Tabular Data
CEA : Matching a cell to a KG entity
CPA : Assigning a KG property to the relationship between two columns We have made the following assumptions to solve the problem. ─ Assumption 1. Every target must have a correct answer. ─ Assumption 2. First column must be a key value (S) of row for making conceptual graph. 1 ─ Assumption 3. Typo cases occur only in the first column. ─ Assumption 4. CTA is linked only to property called “instance of”. ─ Assumption 5. Lower number labeling item represent wider range of class. We have established Assumption 1 and assumed that there is always an answer because it is impossible to reason accurately without a clear answer. We try to approach the problem from a graph perspective and find the key part of the graph using Assumption 2. We set out the rest of the assumptions rather aggressively, considering the factors we gained empirically over the course of the round. 2 2.1
Given tabular data to perform the task, we approached the table from a matrix perspective and redefine it as follows (Table 1). The subject cell refers to the zero column in the target table. This cell means the title of the document in Wikidata, and it always has a document label, not a literal form. The object cell refers to the cells of all rows except the zero column in the target table. This cell means objects that exist in the document title in Wikidata, •anTdaurngliekte stuabbjelcet csetlrlsu, cthteureremay be a literal form that is not tagged with a label.
col0 ( , 0) . .
. ( , 0) . .
. ─ Object Cell : ( , ) ( i = 1, 2 …, m ), ( j = 1, 2 …, n ).
─ Header Row : (0, ) ( j = 0, 1, 2 …, n ). • C2.o2ncGeeneprattiungaSlubsgruapbhgraph
If information about the target is given as follows, CTA with column id 0, CPA with head column id and tail column id 0 and 1, and CEA of 0th and 1st column cells, We can find the values in Table 2 and generate a conceptual subgraph as shown in Figure 2. • object cell : ( , ) (i = 1, 2 … m), ( j = 1, 2 … n) 2 Society (Q8425) • Conceptual subgraph Tabular data can be expressed in the form of knowledge graphs in SPO relationships. Most of all, Wikidata can find all relationships in a document if it has a clear label, such as the title of a document defined as an item label. Blueline means the title of a Wikidata document. All relationships can be found in Wikidata document title label. Based on the document title, it can follow the list of targets on the right and carry out the task matching to the table. To the right of the standard, it is possible to confirm that other objects are connected to the CPA matching location in the administrative terminal entity. Similarly, it can check the CTA connected to the instance connected by Assumption 4 to the left. It is important to find Leesmuseum through these graphs. However, we have prioritized finding the labeling of Leesmuseum (Q2472824). Because if we know at least one subject, we can deduce the remaining factors or classify candidates close to the answer. 3
In this section, we present the architecture of our system, consisting of 4 stages, as illustrated in Figure 3: Stage 1. Candidate Extraction, Stage 2. Node Selection, Stage 3. Subject Crawling, Stage 4. Element Inference. Each stage is described next in more detail in a separate subsection. ─ Stage 2. Select 'subject' with the high probability value among the Candidates and updated graph. ─ Stage 3. Crawl the relevant subject which can’t be found, and then repeat Stage 1-2. ─ Stage 4. Infer to other people using updated graph, and then repeat stage 1-2. 3.1
In Stage 1, the advanced SPARQL query that is a rule-based model helps us make the
choice of appropriate queries for each data type, such as a constant pattern value (e.g.
a date type), a numerical value and text is used to find the values in the tabular table
from Wikidata. Since the tabular file can be converted to utf-8 code, preprocessing for
the language type was not performed. For each table, according to the appropriate data
type, a mix of query features were applied [
answer CTA
CEA
CPA Selecting all the candidates using a query, we proceed with a simple preprocessing. As mentioned earlier, numbers can be either text or literal, so we use a query with two things in mind. When such a query is applied, the contents unrelated to the annotation are extracted to the predicate candidate, so the work of removing the candidates with it is proceeded. After preprocessing, the subject with the highest probability of appearance is selected among all candidates. If an equivalent probability is found, all processes for the candidates are performed the same and one is chosen randomly for the final process. When a subject is determined, then objects related to the subject are matched. However, the work of finding the predicate must continue until the rest of the Wikidata page titles are found. Through the determined subject and objects, the work of filling in the nodes in the graph was in progress (Fig. 4). 3.3
If there are no errors in the tabular data, the whole process is probably done in the previous step. However, in the assignment, there were many typos and errors in the data. There were many types of data containing incorrect values, such as misspellings, incorrect spacing, and omission of other special symbols or numbers. In order to solve the typo error, the problem was approached by crawling through a search engine. The crawl was performed through the Google search engine, but during the crawling process, several cases were classified and prioritized to perform the crawl. When using the Google search engine, Google sometimes automatically corrects typos and recommends related search terms. Crawling was performed on the top page, and the order was related to the Wikidata title, automatically correcting the typo, and finally the case related to the Wikipedia title. 3.4
After correcting the typo, we repeat the process in Stages 1-2. Then, the system can find a subject that is highly relevant to the subject indicated in yellow on the graph (Fig. 5a). And like subject, the candidate with the highest probability value is selected from the predicate list that has been kept so far. When an equivalent probability value comes out, we create and maintain a predicate list of only candidates with equivalent values. For CTA work, only ‘instance of (P31)’ is used by Assumption 4. After this process, inferring through the remaining elements in the graph is performed to find the elements corresponding to orange (Fig. 5b). If there is a predicate list composed of equivalent values, the remaining predicates are selected through the inferred elements and the final work is completed.
associative subject answer CTA CTA inference target instance of (P31) answer
CEA
CPA (a) (b) CEA
CPA
CEA CEA
Fig. 5. Update Graph Process. The graph on the upper is the graph after Stage 3.
We continued to update our system as we performed round by round. In Round 1 and Round 2, results were good and had few problems. However, in Round 3, the CPA results were particularly bad, including all rounds in Table 4. This result came as the number of rows in the table decreased and the number of errors increased. It was confirmed that if the number of rows in the table is large, incorrect reasoning rarely occurs, but in the opposite case, many errors occur. AF1-Score A-Precision F1-Score
Conclusion This system presents a method of approaching the semantic table annotation tasks by creating SPRAQL queries and graphs. Accessing Wikidata using queries is very simple and much lighter than downloading a database dump directly. Especially, in the case of small sized data, this advantage is clear. In addition, this approach is well suited to the nature of Wikidata, which has the potential to modify data at any time. There are several improvements to this system. It is a method that can only be applied within the closed world called Wikidata. Additionally, if terms with many comprehensive meanings exist, it takes a lot of time to work. Although many assumptions were set up to solve the problem above, if data problems occur in other cells, a more advanced system is needed rather than a crawl method. This problem can show better performance if we apply learning about pattern sequence in characters.