Search results

The purpose of this paper is to better understand three main aspects of semantic web search engines of Google Knowledge Graph and Bing Satori. The authors investigated: coverage of entity types, the extent of their support for list search services and the capabilities of their natural language query interfaces.

The authors manually submitted selected queries to these two semantic web search engines and evaluated the returned results. To test the coverage of entity types, the authors selected the entity types from Freebase database. To test the capabilities of natural language query interfaces, the authors used a manually developed query data set about US geography.

The results indicate that both semantic search engines cover only the very common entity types. In addition, the list search service is provided for a small percentage of entity types. Moreover, both search engines support queries with very limited complexity and with limited set of recognised terms.

Both companies are continually working to improve their semantic web search engines. Therefore, the findings show their capabilities at the time of conducting this research.

The results show that in the near future the authors can expect both semantic search engines to expand their entity databases and improve their natural language interfaces.

As far as the authors know, this is the first study evaluating any aspect of newly developing semantic web search engines. It shows the current capabilities and limitations of these semantic web search engines. It provides directions to researchers by pointing out the main problems for semantic web search engines.

Many mobile devices today are equipped with diversified sensors that enable the acquisition of rich user context (e.g. GPS location, phone activity) for application utilization. With the growing usage of mobile devices in daily life, the problem of conveniently and promptly searching a piece of content that a user has viewed on his/her device before becomes more and more crucial. This paper aims to propose a context‐based query processing framework called UCQP that supports unstructured queries for content search in a user's access history.

Beyond the keywords related to the content properties, a context query in the framework is specified with freeform phrases that describe high‐level mobile contexts of the user at a previous time when the user viewed the searched content.

Experimental results on a prototype system of the framework illustrate its good accuracy and small response time.

To tolerate the incompleteness and inaccuracy in user query texts caused by fading human memory, the authors develop several semantic query parsers that are tailored for different types of contexts using natural language processing and information retrieval techniques. The authors further propose a similarity model to rank the multiple result contents of a query by comparing context entities specified in the query and historical context values associated with each result.

There is a huge amount of information and data stored in publicly available online databases that consist of large text files accessed by Boolean search techniques. It is widely held that less use is made of these databases than could or should be the case, and that one reason for this is that potential users find it difficult to identify which databases to search, to use the various command languages of the hosts and to construct the Boolean search statements required. This reasoning has stimulated a considerable amount of exploration and development work on the construction of search interfaces, to aid the inexperienced user to gain effective access to these databases. The aim of our paper is to review aspects of the design of such interfaces: to indicate the requirements that must be met if maximum aid is to be offered to the inexperienced searcher; to spell out the knowledge that must be incorporated in an interface if such aid is to be given; to describe some of the solutions that have been implemented in experimental and operational interfaces; and to discuss some of the problems encountered. The paper closes with an extensive bibliography of references relevant to online search aids, going well beyond the items explicitly mentioned in the text. An index to software appears after the bibliography at the end of the paper.

This paper introduces an approach to minimize the total designer effort for building XML data integration systems. Since fully automatic schema mapping generation is infeasible, in our view such an approach can be used as a semi‐automatic tool for XML schemas mediation. A method is proposed to query XML documents through a mediation layer. Such a layer is introduced to describe the mappings between global XML schema and local heterogeneous XML schemas. It produces a uniform interface over the local XML data sources, and provides the required functionality to query these sources in a uniform way. It involves two important units: the XML Metadata Document (XMD) and the Query Translator. The XMD is an XML document containing metadata, in which the mappings between global and local schemas are defined. The XML Query Translator which is an integral part of the system is introduced to translate a global user query into local queries by using the mappings that are defined in the XMD.

With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG.

The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method.

The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches.

Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.

When accessing structured contents in XML form, information requests are formulated in the form of special query languages such as NEXI, Xquery, etc. However, it is not easy for end users to compose such information requests using these special queries because of their complexities. Hence, the purpose of this paper is to automate the construction of such queries from common query like keywords or form-based queries.

In this paper, the authors address the problem of constructing queries for XML retrieval by proposing a semantic-syntax query model that can be used to construct different types of structured queries. First, a generic query structure known as semantic query structure is designed to store query contents given by user. Then, generation of a target language is carried out by mapping the contents in semantic query structure to query syntax templates stored in knowledge base.

Evaluations were carried out based on how well information needs are captured and transformed into a target query language. In summary, the proposed model is able to express information needs specified using query like NEXI. Xquery records a lower percentage because of its language complexity. The authors also achieve satisfactory query construction rate with an example-based method, i.e. 86 per cent (for NEXI IMDB topics) and 87 per cent (NEXI Wiki topics), respectively, compare to benchmark of 78 per cent by Sumita and Iida in language translation.

The proposed semantic-syntax query model allows flexibility of accommodating new query language by separating the semantic of query from its syntax.

With the rapid development of materials informatics and the Semantic Web, the semantic-driven solution has emerged to improve traditional query technology, which is hard to discover implicit knowledge from materials data. However, it is a nontrivial thing for materials scientists to construct a semantic query, and the query results are usually presented in RDF/XML format which is not convenient for users to understand. This paper aims to propose an approach to construct semantic query and visualize the query results for metallic materials domain.

The authors design a query builder to generate SPARQL query statements automatically based on domain ontology and query conditions inputted by users. Moreover, a semantic visualization model is defined based on the materials science tetrahedron to support the visualization of query results in an intuitive, dynamic and interactive way.

Based on the Semantic Web technology, the authors design an automatic semantic query builder to help domain experts write the normative semantic query statements quickly and simply, as well as a prototype (named MatViz) is developed to visually show query results, which could help experts discover implicit knowledge from materials data. Moreover, the experiments demonstrate that the proposed system in this paper can rapidly and effectively return visualized query results over the metallic materials data set.

This paper mainly discusses an approach to support semantic query and visualization of metallic materials data. The implementation of MatViz will be a meaningful work for the research of metal materials data integration.

In this paper we give a synoptic view of the growth of the text processing technology of information extraction (IE) whose function is to extract information about a pre‐specified set of entities, relations or events from natural language texts and to record this information in structured representations called templates. Here we describe the nature of the IE task, review the history of the area from its origins in AI work in the 1960s and 70s till the present, discuss the techniques being used to carry out the task, describe application areas where IE systems are or are about to be at work, and conclude with a discussion of the challenges facing the area. What emerges is a picture of an exciting new text processing technology with a host of new applications, both on its own and in conjunction with other technologies, such as information retrieval, machine translation and data mining.

This study aims to develop a Web-based application system called Infomediary of Taiwanese Indigenous Peoples (ITIP) that can help individuals comprehend the society and culture of indigenous people. The ITIP is based on the use of Semantic Web technologies to integrate a number of data sources, particularly including the bibliographic records of a museum. Moreover, an ontology model was developed to help users search cultural collections by topic concepts.

Two issues were identified that needed to be addressed: the integration of heterogeneous data sources and semantic-based information retrieval. Two corresponding methods were proposed: SPARQL federated queries were designed for data integration across the Web and ontology-driven queries were designed to semantically search by knowledge inference. Furthermore, to help users perform searches easily, three searching interfaces, namely, ethnicity, region and topic, were developed to take full advantage of the content available on the Web.

Most open government data provides structured but non-resource description framework data, Semantic Web consumers, therefore, require additional data conversion before the data can be used. On the other hand, although the library, archive and museum (LAM) community has produced some emerging linked data, very few data sets are released to the general public as open data. The Semantic Web’s vision of “web of data” remains challenging.

This study developed data integration from various institutions, including those of the LAM community. The development was conducted based on the mode of non-institution members (i.e. institutional outsiders). The challenges encountered included uncertain data quality and the absence of institutional participation.

The purpose of this paper is to show how previous studies have demonstrated that non‐professional users prefer using event‐based conceptual descriptions, such as “a woman wearing a hat”, to describe and search images. In many art image archives, these conceptual descriptions are manually annotated in free‐text fields. This study aims to explore technologies to automate event‐based knowledge extractions from these free‐text image descriptions.

This study presents an approach based on semantic role labeling technologies for automatically extracting event‐based knowledge, including subject, verb, object, location and temporal information from free‐text image descriptions. A query expansion module is applied to further improve the retrieval recall. The effectiveness of the proposed approach is evaluated by measuring the retrieval precision and recall capabilities for experiments with real life art image collections in museums.

Evaluations results indicate that the proposed method can achieve a substantially higher retrieval precision than conventional keyword‐based approaches. The proposed methodology is highly applicable for large‐scale collections where the image retrieval precision is more critical than the recall.

The study provides the first attempt in literature for automating the extraction of event‐based knowledge from free‐text image descriptions. The effectiveness and ease of implementation of the proposed approach make it feasible for practical applications.