Semantic Query Expansion Research Articles

MKBQA: Question Answering over Knowledge Graph Based on Semantic Analysis and Priority Marking Method

In the field of question answering-based knowledge graphs, due to the complexity of the construction of knowledge graphs, a domain-specific knowledge graph often cannot contain some common-sense knowledge, which makes it impossible to answer questions that involve common-sense and domain knowledge at the same time. Therefore, this study proposes a knowledge graph-based question answering method in the computer science domain, which facilitates obtaining complete answers in this domain. In order to solve the problem of natural language problems being difficult to match with structured knowledge, a series of logic rules are first designed to convert natural language into triples of the question. Then, a semantic query expansion strategy based on WordNet is proposed and a priority marking algorithm is proposed to mark the order of triples of the question. Finally, when a question triple corresponds to multiple triples in the knowledge graph, it can be solved by the proposed SimCSE-based similarity method. The designed logic rules can deal with each type of question in a targeted manner according to the different question words and can effectively transform the question text into question triples. In addition, the proposed priority marking algorithm can effectively mark the order in the triple of the question. MKBQA can answer not only computer science-related questions but also extended open domain questions. In practical applications, answering a domain question often cannot rely solely on one knowledge graph. It is necessary to combine domain knowledge and common-sense knowledge. The MKBQA method provides a new idea and can be easily migrated from the field of computer science to other fields. Experiment results on real-world data sets show that, as compared to baselines, our method achieves significant improvements to question answering and can combine common-sense and domain-specific knowledge graphs to give a more complete answer.

Semantic query expansion method based on pay-as-yougo fashion for graph model

The search requirements of users are usually vague and unclear, and the traditional keyword query method is difficult to obtain satisfactory query results. Therefore, the semantic query expansion technology uses semantic information to modify and extend the initial search requirements of users, to obtain more comprehensive and accurate query results. However, the construction cost of the extended query graph is high, and the existing research is difficult to adjust and optimize the user’s query demand in time, resulting in a poor query effect. To solve the above problems, this paper proposes a semantic expansion method based on pay-as-you-go fashion for graph model: Firstly, the initial query graph is constructed according to the user’s search requirements, and the semantic similarities between the search requirements and knowledge bases are calculated; Then, the extended sets of attribute-values and edges are generate by sorting the similarity value in descending order; Finally, all or part of the elements in the extended set are combined to generate the extended query graph. This method can dynamically adjust and optimize the initial query based on the relevant semantic information in time, and improve the query efficiency and effect.

Semantic approaches for query expansion

To increase the Information Retrieval System’s efficiency, there is a requirement to expand the native user query. There are many approaches to enhance the user query in which the primary method consists of semantic-based query expansion (QE). In a semantic-based QE approach, relevant documents are retrieved by considering all the similar terms of a given user query. The semantic-based QE approach helps in dealing with the limitation of low recall and low precision value of the Information retrieval system and deals with ambiguity and vagueness. Query Expansion techniques contain the semantic concepts that are relevant to semantic computing, computational intelligence, and information retrieval area. Computational intelligence technique is required during automatic query expansion for advanced information processing. This paper presents a comprehensive survey of the methods proposed by several researchers considering semantic-based query expansion. It also discusses the merits and demerits of each technique in a detailed manner. This paper presents the view of several semantic query expansion core techniques. This paper also discusses the various keyholes present in today’s era.

A fuzzy ontology framework in information retrieval using semantic query expansion

World Wide Web (WWW) constitutes fuzzy information and requires soft computing techniques to deal context of the query. It works on the principle of keyword matching yielding low precision and recall. Semantic web, an extension WWW improves the information retrieval process. Query expansion is utmost importance in information retrieval to retrieve relevant results. To overcome the weaknesses of current web system and to utilize the strengths query expansion a novel framework based on fuzzy ontology is proposed for information retrieval. In the proposed framework, domain specific knowledge is utilized for ontology construction. In framework pre-defined domain ontologies and Global ontology, ConceptNet is used to construct a fuzzy ontology. Based on constructed fuzzy ontology most semantically related words for a query are identified and query is expanded. A fuzzy membership function is defined for different semantic relationships present among the Global ontology ConceptNet.Based on the proposed framework queries are expanded (Semantic query expansion) and evaluated on four popular search engines namely Google, Yahoo, Bing and Exalead. The performance metrics used are Precision, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), R-precision and Number of documents retrieved. The Web search engines are precision oriented. Based on the proposed framework all the metrics are improved approx. by 10%. Precision before the query expansion lies between 0.75-0.81 whereas after the query expansion lies between 0.85-0.89 on various search engines. The number of documents retrieved is almost improved 1/1000 after the query expansion.

Tweet Contextualization Approach Using a Semantic Query Expansion

Twitter is a communication medium and a collaboration system that allows broadcasting short messages called tweets. In contrast to traditional blogs, media-sharing and social networks services, microblogs (tweets) are textual messages submitted in real-time to report an idea, an actual interest, or an opinion. The size of these messages may be limited by a maximum number of characters. This constraint, related to the size of message, causes the use of a particular vocabulary. The aim is to exchange a maximum of information in as little characters as possible. In this respect, we will focus on the Tweet Contextualization task. The purpose of this task is to allow the reader a better understanding of the tweet. This paper deals with a new Tweet Contextualization approach using a semantic query expansion. The main idea of our proposed method is to enhance queries (tweets) in order to produce more informative contexts. The effectiveness of our method is proved through an experimental study conducted on the INEX 2014 collection.

Ontology based Semantic Query Expansion for Searching Queries in Programming Domain

Information on the web is growing rapidly; learning programming languages has become one of the most widely searched topics. Programmers and people of several backgrounds are now using Web search engines to acquire programming information, including information about a specific language or topic. Nonetheless, due to a lack of programming knowledge, many laypeople have difficulties in forming appropriate queries to articulate their inquiries, so they used to write short queries, which leads to vocabulary problems and contextless query. Besides, semantics is almost neglected in traditional search query since it is just keyword-based searches, which deem their search to be imprecise with irrelevant results, due to the use of unclear keywords. A Semantic query expansion method is proposed for disambiguating queries in computer programming domain using ontology. The integration of Cosine similarity method into the proposed model improved the expanded query, and accordingly, the searching results. The proposed system has been tested with several ambiguous and misspelled queries and the generated extensions proved to retrieve more relevant results, when applied to a search engine. The quality of the retrieved results for the expanded queries, are much higher than that for the crude queries. The proposed technique was implemented, and then tested with external independent testers. They confirmed the mechanical test results and displayed its improvement with an average of precision @10 82.2% and 91.1% respectively. The results were promising and therefore open further research directions.

Semantic Query Expansion based on Entity Association in Medical Question Answering

Query expansion technology is a common method to solve the semantic deviation problem between questions and answers in the question answering system. In medical question answering field, the current semantic-based query expansion method has the following two shortcomings. (1) In the stage of obtaining candidate extended terms, only the medical entity’s concept in the query is used, but the medical entity’s association between questions and answers is ignored (2) During the extended terms filtering stage, the interference of negative medical entities to the mutual information is ignored. Given the above-mentioned deficiencies, this paper proposes a semantic query expansion method based on entity association in medical question answering. This method first combines the query intent with the inference association between medical entities to obtain candidate extended terms from the medical knowledge graph. Then filter candidate extended terms using the selection method based on negative medical entity recognition and mutual information and thus get the final extended terms according to the co-occurrence association between medical entities. Experimental results show that our proposed method outperforms the query expansion method based on the thesaurus which commonly used in the current medical question and answering system.

Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online

Abstract. Many geoportals such as ArcGIS Online are established with the goal of improving geospatial data reusability and achieving intelligent knowledge discovery. However, according to previous research, most of the existing geoportals adopt Lucene-based techniques to achieve their core search functionality, which has a limited ability to capture the user’s search intentions. To better understand a user’s search intention, query expansion can be used to enrich the user’s query by adding semantically similar terms. In the context of geoportals and geographic information retrieval, we advocate the idea of semantically enriching a user’s query from both geospatial and thematic perspectives. In the geospatial aspect, we propose to enrich a query by using both place partonomy and distance decay. In terms of the thematic aspect, concept expansion and embedding-based document similarity are used to infer the implicit information hidden in a user’s query. This semantic query expansion framework is implemented as a semantically-enriched search engine using ArcGIS Online as a case study. A benchmark dataset is constructed to evaluate the proposed framework. Our evaluation results show that the proposed semantic query expansion framework is very effective in capturing a user’s search intention and significantly outperforms a well-established baseline – Lucene’s practical scoring function – with more than 3.0 increments in DCG@K (K=3,5,10).

A Query Understanding Framework for Earth Data Discovery

One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user’s search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user’s query from multiple perspectives, with the goal of understanding the consumer’s quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user’s intent.

Query Expansion for Improving Web Search

The aim of this paper is to retrieve the most relevant expansion words for expanding the initial query of the user in order to enhance the outcomes of web search results. Query expansion plays a major role in reformulating a user’s initial query to a one more pertinent to the user’s intended meaning. The reformulated query is then used to obtain more appropriate outcomes from a large amount of information on the web. The proposed semantic query expansion technique uses Wikipedia and WordNet as data sources. Wikipedia is taken as a base for all query expansions because it is one of the most diversified and relevant databases available on the web. To further improve the proposed query expansion technique,WordNet—a lexical database—is used as the as another data source because the synonyms (synsets) of the query term provided by it can be quite useful for query expansion. The proposed expansion technique successfully combines the two data sources to retrieve the most relevant expansion terms from the data sources in response to the user’s original query. The proposed work has been divided into four phases: (1) extraction of relevant words from Wikipedia (2) extraction of relevant words from WordNet (3) merging of the expansion terms obtained from Wikipedia and WordNet, and (4) query formulation by combining the expansion terms using Boolean operators. This reformulated query is then fired on the web to find the desired result. The Experimental result shows a significant improvement in information retrieval using query expansion.

Performance evaluation of three semantic expansions to query PubMed.

PubMed is one of the most important basic tools to access medical literature. Semantic query expansion using synonyms can improve retrieval efficacy. The objective was to evaluate the performance of three semantic query expansion strategies. Queries were built for forty MeSH descriptors using three semantic expansion strategies (MeSH synonyms, UMLS mappings, and mappings created by the CISMeF team), then sent to PubMed. To evaluate expansion performances for each query, the first twenty citations were selected, and their relevance were judged by three independent evaluators based on the title and abstract. Queries built with the UMLS expansion provided new citations with a slightly higher mean precision (74.19%) than with the CISMeF expansion (70.28%), although the difference was not significant. Inter-rater agreement was 0.28. Results varied greatly depending on the descriptor selected. The number of citations retrieved by the three strategies and their precision varied greatly according to the descriptor. This heterogeneity could be explained by the quality of the synonyms. Optimal use of these different expansions would be through various combinations of UMLS and CISMeF intersections or unions. Information retrieval tools should propose different semantic expansions depending on the descriptor and the search objectives.

A Taxonomy and Survey of Semantic Approaches for Query Expansion

Conventional approaches to query expansion (QE) rely on the integration of an unstructured corpus and probabilistic rules for the extraction of candidate expansion terms. These methods do not consider search query semantics, thereby resulting in ineffective retrieval of information. The semantic approaches for QE overcome this limitation, whereby a search query is expanded with meaningful terms that accord with user information needs. This paper surveys recent approaches to semantic QE that employ different models and strategies and leverages various knowledge structures. We organize these approaches into a taxonomy that includes linguistic methods, ontology-based methods, and mixed-mode methods. We also discuss the strengths and limitations of each type of semantic QE method. In addition, we evaluate various semantic QE approaches in terms of knowledge structure utilization, corpus collection, baseline model adaptation, and retrieval performance. Finally, future directions in exploiting personalized social information and multiple ontologies for semantic QE are suggested.

A study of semantic integration across archaeological data and reports in different languages

This study investigates the semantic integration of data extracted from archaeological datasets with information extracted via natural language processing (NLP) across different languages. The investigation follows a broad theme relating to wooden objects and their dating via dendrochronological techniques, including types of wooden material, samples taken and wooden objects including shipwrecks. The outcomes are an integrated RDF dataset coupled with an associated interactive research demonstrator query builder application. The semantic framework combines the CIDOC Conceptual Reference Model (CRM) with the Getty Art and Architecture Thesaurus (AAT). The NLP, data cleansing and integration methods are described in detail together with illustrative scenarios from the web application Demonstrator. Reflections and recommendations from the study are discussed. The Demonstrator is a novel SPARQL web application, with CRM/AAT-based data integration. Functionality includes the combination of free text and semantic search with browsing on semantic links, hierarchical and associative relationship thesaurus query expansion. Queries concern wooden objects (e.g. samples of beech wood keels), optionally from a given date range, with automatic expansion over AAT hierarchies of wood types and specialised associative relationships. Following a ‘mapping pattern’ approach (via the STELETO tool) ensured validity and consistency of all RDF output. The user is shielded from the complexity of the underlying semantic framework by a query builder user interface. The study demonstrates the feasibility of connecting information extracted from datasets and grey literature reports in different languages and semantic cross-searching of the integrated information. The semantic linking of textual reports and datasets opens new possibilities for integrative research across diverse resources.

A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example

Discovering and accessing geospatial data presents a significant challenge for the Earth sciences community as massive amounts of data are being produced on a daily basis. In this article, we report a smart web-based geospatial data discovery system that mines and utilizes data relevancy from metadata user behavior. Specifically, (1) the system enables semantic query expansion and suggestion to assist users in finding more relevant data; (2) machine-learned ranking is utilized to provide the optimal search ranking based on a number of identified ranking features that can reflect users’ search preferences; (3) a hybrid recommendation module is designed to allow users to discover related data considering metadata attributes and user behavior; (4) an integrated graphic user interface design is developed to quickly and intuitively guide data consumers to the appropriate data resources. As a proof of concept, we focus on a well-defined domain-oceanography and use oceanographic data discovery as an example. Experiments and a search example show that the proposed system can improve the scientific community’s data search experience by providing query expansion, suggestion, better search ranking, and data recommendation via a user-friendly interface.

Semantic Sequential Query Expansion for Biomedical Article Search

The conventional sequential dependence model (SDM) has been proved to perform better than the bag of words model for biomedical article search because it pays attention to the sequence information within queries. Meanwhile, introducing lexical semantic relations into query expansion becomes a hot topic in IR research. However, a few research have been conducted on combining semantic and sequence information together. Hence, we propose the semantic sequential dependence model in this paper, which provides an innovative combination of semantic information and the conventional SDM. Specifically, our synonyms are obtained automatically through the word embeddings which are trained on the domain-specific corpus by selecting an appropriate language model. Then, these synonyms are utilized to generate possible sequences with the same semantics as the original query and these sequences are fed into SDM to obtain the final retrieval results. The proposed approach is evaluated on 2016 and 2017 BioASQ benchmark test sets and the experimental results show that our query expansion approach outperforms the baseline and other participants in the BioASQ competitions.

Document Retrieval in Self Directed E-Learning

Self Directed E-learning focuses on the independent learner one who engages in education at his own space free from curricular obligation. The browsing history of the E-learner is used to select the document of his or her interest along with the technique of semantic query expansion.

BiobankUniverse: automatic matchmaking between datasets for biobank data discovery and integration.

MotivationBiobanks are indispensable for large-scale genetic/epidemiological studies, yet it remains difficult for researchers to determine which biobanks contain data matching their research questions.ResultsTo overcome this, we developed a new matching algorithm that identifies pairs of related data elements between biobanks and research variables with high precision and recall. It integrates lexical comparison, Unified Medical Language System ontology tagging and semantic query expansion. The result is BiobankUniverse, a fast matchmaking service for biobanks and researchers. Biobankers upload their data elements and researchers their desired study variables, BiobankUniverse automatically shortlists matching attributes between them. Users can quickly explore matching potential and search for biobanks/data elements matching their research. They can also curate matches and define personalized data-universes.Availability and implementationBiobankUniverse is available at http://biobankuniverse.com or can be downloaded as part of the open source MOLGENIS suite at http://github.com/molgenis/molgenis.Supplementary information Supplementary data are available at Bioinformatics online.

Personalized Semantic Query Expansion Based on Dynamic User Query Profile and Spreading Activation Model

Personalized Semantic Query Expansion Based on Dynamic User Query Profile and Spreading Activation Model

Retrieving similar cases for construction project risk management using Natural Language Processing techniques

Abstract Case-based reasoning (CBR) is an important approach in construction project risk management. It emphasises that previous knowledge and experience of accidents and risks are highly valuable and could contribute to avoiding similar risks in new situations. In the CBR cycle, retrieving useful information is the first and the most important step. To facilitate the CBR for practical use, some researchers and organisations have established construction accident databases and their size is growing. However, as those documents are written in everyday language using different ways of expression, how information in similar cases is retrieved quickly and accurately from the database is still a huge challenge. In order to improve the efficiency and performance of risk case retrieval, this paper proposes an approach of combining the use of two Natural Language Processing (NLP) techniques, i.e. Vector Space Model (VSM) and semantic query expansion, and outlines a framework for this Risk Case Retrieval System. A prototype system is developed using the Python programming language to support the implementation of the proposed method. Preliminary test results show that the proposed system is capable of retrieving similar cases automatically and returning, for example, the top 10 similar cases.

An approach to semantic query expansion system based on Hepatitis ontology.

BackgroundOntology development, as an increasingly practical vehicle applied in various fields, plays a significant role in knowledge management. This paper, focusing on constructing and querying a hepatitis ontology, aims to provide a framework for ontology-based medical services. The paper is devoted to the algorithm of query expansion for the hepatitis ontology, including synonym expansion, hypernym/hyponym expansion and expansion of similar words. It applies semantic similarity calculation to judge the similarity of retrieval terms.ResultsThe paper proposes a new prototype system. The accuracy of query expansion is improved in both precision@40 and AP@40, which indicates that query expansion improves the accuracy of the query after using the method proposed in this paper.ConclusionsThe paper has adopted semantic similarity computing to improve retrieval performance. Experiments show that search precision of query expansion is higher based on domain concept relationship.