A Practice of Tourism Knowledge Graph Construction based on Heterogeneous Information (2020)

Abstract: The increasing amount of semi-structured and unstructured data on tourism websites brings a need for information extraction (IE) so as to construct a Tourism-domain Knowledge Graph (TKG), which is helpful to manage tourism information and develop downstream applications such as tourism search engine, recommendation and Q & A. However, the existing TKG is deficient, and there are few open methods to promote the construction and widespread application of TKG. In this paper, we present a systematic framework to build a TKG for Hainan, collecting data from popular tourism websites and structuring it into triples. The data is multi-source and heterogeneous, which raises a great challenge for processing it. So we develop two pipelines of processing methods for semi-structured data and unstructured data respectively. We refer to tourism InfoBox for semi-structured knowledge extraction and leverage deep learning algorithms to extract entities and relations from unstructured travel notes, which are colloquial and high-noise, and then we fuse the extracted knowledge from two sources. Finally, a TKG with 13 entity types and 46 relation types is established, which totally contains 34,079 entities and 441,371 triples. The systematic procedure proposed by this paper can construct a TKG from tourism websites, which can further applied to many scenarios and provide detailed reference for the construction of other domain-specific knowledge graphs.

## introduction tourism has become increasingly popular in people's daily life. before people set out to travel, they often need to make clear the travel guides and matters needing attention for their destinations. nowadays, with the development of the internet, many tourism websites have appeared and provide a variety of travel information, such as attractions, tickets, bus routes, travel guides, etc. however, there may be some errors in the miscellaneous information on the tourism websites, and information on different tourism websites may be inconsistent. as shown in screenshots of sina micro-blog users' blogs in figure 1 , there are still tourists who are worried about making travel strategies despite rich information on all kinds of tourismrelated search engines. how to collect and integrate valuable tourism knowledge on websites is a very important issue. recently, knowledge graph (kg) has received much attention and research interest in industry and academia. the kg utilizes a set of subject-predicate-object triplets to represent the diverse entities and their relations in real-world scenes, which are respectively represented as nodes and edges in the graph. the kg is a graph-based large-scale knowledge representation and integration method, which has been applied in various scenarios such as enterprise @xcite , medical @xcite and industry @xcite . naturally, we consider applying kg in the field of tourism to integrate and organize relevant knowledge, so as to provide tourists with easier tools to develop travel strategies. at present, several general knowledge graphs (gkgs) have been built both in chinese and english @xcite @xcite . the domain-specific figure 1 : screenshots of sina micro-blog users' blogs. in the blogs, people with tourism intentions complain that it is difficult to formulate travel strategies. knowledge graph (dkg) in which the stored knowledge is limited to a certain field has also been implemented and put into use in many domains @xcite . however, tourism-domain knowledge graph (tkg) is still deficient, which undoubtedly hinders the development of intelligent tourism system. in this paper, we propose a systematic framework to construct a tkg under the background of hainan tourism. we combine the semi-structured knowledge crawled from the encyclopedia pages of tourism websites with the unstructured travel notes shared by tourists on the websites as the data source. because of the lack of sufficient high-quality data and the difficulty of language processing, constructing a chinese-based tkg still faces several challenges as follows: travel notes are colloquial and high-noise. the writing style of travel notes is often arbitrary, and tourists tend to add various pictures, emoticons and special characters to travel notes, which will introduce much noise for unstructured data. the lack of datasets dedicated to tourism. there is a serious lack of normative datasets in the tourism field, which are basis of model training. are the general algorithms suitable for tourism? entity extraction and relation extraction are the key steps in knowledge graph construction. most of the existing algorithms for these two tasks are tested on the general datasets, we need to verify whether these algorithms are suitable for the tourism field. how to integrate data from different sources? data from different sources inevitably have some overlaps and ambiguities, which should be eliminated in the kg. facing this challenges, we put forward corresponding methods to deal with them. in detail, the contributions of our work are highlighted as follows: ## related work in recent years, the kg has been applied in many fields to complete knowledge storage, query, recommendation and other functions. in the tourism scene, experts and scholars have also begun to explore the application value of knowledge graphs. dbtravel @xcite is an english tourism-oriented knowledge graph generated from the collaborative travel site wikitravel. a chinese tkg was also constructed by @xcite , which extracted tourism-related knowledge from existing chinese c c l 2 0 2 0 computational linguistics general knowledge graph such as zhishi.me @xcite and cn-dbpedia @xcite . unlike their chinese tkg, we extensively obtain data and extract knowledge from popular tourism websites. in this way, the completeness of our knowledge graph does not depend on the existing knowledge graph, but on the amount of data we acquire. to construct the tkg, we need to extract triples form all kinds of information resources. the conversion process from semi-structured data to structured data is more standardized and has fewer errors, but semi-structured data often cannot contain all the knowledge. with the development of natural language processing (nlp), more and more knowledge graphs are constructed based on unstructured corpus, using named entity recognition (ner) and relation extraction (re) technologies. as a hot research direction in the field of nlp, many chinese ner models have been proposed over the years. the purpose of ner task is to identify mentions of named entities from text and match them to pre-defined categories. as a classic branch of ner models, the dictionary-based methods recognize named entities by constructing a dictionary and matching text with it. for example, cmel @xcite built a synonym dictionary for chinese entities from microblog and adopts improved svm to get textual similarity for entity disambiguation. another line of related work is to apply traditional machine learning techniques to complete the ner task, just like the conditional random fields(crfs)-based ner system proposed by @xcite . recently, neural network-based (nn-based) models have shown great future prospects in improving the performance of ner systems, including bidirectional long short-term memory (lstm) model ( he et al., 2019 to capture the most important information in a sentence, attention-based bidirectional long short-term memory networks(att-blstm) @xcite was come up and become a popular re algorithm. the above supervise learning algorithms are time-consuming and costly to label data. in order to solve these problems, some distant supervision algorithms have also been developed @xcite @xcite . because the tkg only contains knowledge in the field of tourism, the corpus for training is not large, so we do not consider using distant supervision algorithms. ## implementation in this paper, we crawl semi-structured and unstructured data related to hainan tourism from popular travel websites, and extract the structured knowledge from these two types of data in two pipelines. figure 2 shows the overview of our method. c c l 2 0 2 0 ## conclusions with the development of tourism, information management and utilization in the field of tourism is a very important task. we proposed a systematic approach to construct the chinese tourism knowledge graph, c c l 2 0 2 0 using the information on the tourism websites. we leveraged semi-structured data and unstructured data to extract entities and relations synchronously, and they can be combined to obtain more complete sets of entities and relations than only one of them. due to the lack of standardized datasets in the field of tourism, we first proposed a strategy for constructing datasets to facilitate the extraction of entities and relations from the complex network text data. in addition, we used several algorithms to complete the named entity recognition (ner) task and relation extraction (re) task on the datasets we created, and compare the results. we found that bert+bilstm-crf has the best performance for ner task and bilstm+cnn with entity type information introduced performs best on re task. we have implemented a relatively complete information extraction system on the tourism knowledge graph. in the future work, we want to solve the problem of how to update the knowledge in real time, because the knowledge on the tourism websites is always increasing and changing. in addition, we intend to explore some domain-adaptive techniques to make our model can be used widely.