Efficient indexing for semantic search

Lashkari, Fatemeh; Ensan, Faezeh; Bagheri, Ebrahim; Ghorbani, Ali A.

doi:10.1016/j.eswa.2016.12.033

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…However, its efficiency decreases significantly as the volume of the ontology increases. One of the challenges with a search engine that considers semantic information is that it requires index information beyond the stored traditional information, including entity mentions and type relationships [17]. Moreover, the performances of different and the same data structure types are discussed, the different indexes are described, and the appropriate index type for semantic search under different conditions is determined.…”

Section: Literature Reviewmentioning

confidence: 99%

A Method for Improving Reasoning and Realization Problem Solving in Descriptive Logic- Based and Ontology-Based Reasoners

Shokohinia¹,

Dideban²

2022

MJCS

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Recently, many methods have been developed for representing knowledge, reasoning, and result extraction extracting results based on the respective domain knowledge in question. Despite the ontological success in knowledge representation, the reasoning method has faces some challenges. The main challenge in ontology reasoning methods is the failure in solving realization problems in the reasoning process. Apart from the complexity of solving realization problems, this already daunting challenge is compounded by computational complexity the time complexity of the solving realization problem solving process problems is equal to that of NEXP TIME. This important issue problem is achieved solved by solving the subsumption and satisfiability problems. Thus, to solve the realization problem, we first partition the ontology or extract partitions related to the query. Then, the satisfiability problem is solved by extracting partitions, and all concepts related to the query are extracted. This study proposes a method to overcome this problem, where a new solution is proposed with an appropriate time position. Finally, the efficiency of the proposed method, is evaluated against other reasoning engines, and the results show optimized performance vis-a-vis previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

A Method for Improving Reasoning and Realization Problem Solving in Descriptive Logic- Based and Ontology-Based Reasoners

Shokohinia¹,

Dideban²

2022

MJCS

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“…In entity-oriented search, there is frequently a combination of corpora and knowledge bases, and a strong reliance on the integration of unstructured and structured data. Perhaps the most straightforward approach to tackle this problem is to store text in an inverted index and entities and their relations in a triplestore, and then separately compute and combine signals from each storage unit [92, §5.1.2] [129,308]. Despite the importance of the cross-referencing aspect, few attention has been given to the relations within and across corpora and knowledge bases in the design of representation models.…”

Section: --mentioning

confidence: 99%

Graph-based entity-oriented search

Devezas

2021

SIGIR Forum

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Entity-oriented search has revolutionized search engines. In the era of Google Knowledge Graph and Microsoft Satori, users demand an effortless process of search. Whether they express an information need through a keyword query, expecting documents and entities, or through a clicked entity, expecting related entities, there is an inherent need for the combination of corpora and knowledge bases to obtain an answer. Such integration frequently relies on independent signals extracted from inverted indexes, and from quad indexes indirectly accessed through queries to a triplestore. However, relying on two separate representation models inhibits the effective cross-referencing of information, discarding otherwise available relations that could lead to a better ranking. Moreover, different retrieval tasks often demand separate implementations, although the problem is, at its core, the same. With the goal of harnessing all available information to optimize retrieval, we explore joint representation models of documents and entities, while taking a step towards the definition of a more general retrieval approach. Specifically, we propose that graphs should be used to incorporate explicit and implicit information derived from the relations between text found in corpora and entities found in knowledge bases. We also take advantage of this framework to elaborate a general model for entity-oriented search, proposing a universal ranking function for the tasks of ad hoc document retrieval (leveraging entities), ad hoc entity retrieval, and entity list completion. At a conceptual stage, we begin by proposing the graph-of-entity, based on the relations between combinations of term and entity nodes. We introduce the entity weight as the corresponding ranking function, relying on the idea of seed nodes for representing the query, either directly through term nodes, or based on the expansion to adjacent entity nodes. The score is computed based on a series of geodesic distances to the remaining nodes, providing a ranking for the documents (or entities) in the graph. In order to improve on the low scalability of the graph-of-entity, we then redesigned this model in a way that reduced the number of edges in relation to the number of nodes, by relying on the hypergraph data structure. The resulting model, which we called hypergraph-of-entity, is the main contribution of this thesis. The obtained reduction was achieved by replacing binary edges with n -ary relations based on sets of nodes and entities (undirected document hyperedges), sets of entities (undirected hyperedges, either based on cooccurrence or a grouping by semantic subject), and pairs of a set of terms and a set of one entity (directed hyperedges, mapping text to an object). We introduce the random walk score as the corresponding ranking function, relying on the same idea of seed nodes, similar to the entity weight in the graph-of-entity. Scoring based on this function is highly reliant on the structure of the hypergraph, which we call representation-driven retrieval. As such, we explore several extensions of the hypergraph-of-entity, including relations of synonymy, or contextual similarity, as well as different weighting functions per node and hyperedge type. We also propose TF-bins as a discretization for representing term frequency in the hypergraph-of-entity. For the random walk score, we propose and explore several parameters, including length and repeats, with or without seed node expansion, direction, or weights, and with or without a certain degree of node and/or hyperedge fatigue, a concept that we also propose. For evaluation, we took advantage of TREC 2017 OpenSearch track, which relied on an online evaluation process based on the Living Labs API, and we also participated in TREC 2018 Common Core track, which was based on the newly introduced TREC Washington Post Corpus. Our main experiments were supported on the INEX 2009 Wikipedia collection, which proved to be a fundamental test collection for assessing retrieval effectiveness across multiple tasks. At first, our experiments solely focused on ad hoc document retrieval, ensuring that the model performed adequately for a classical task. We then expanded the work to cover all three entity-oriented search tasks. Results supported the viability of a general retrieval model, opening novel challenges in information retrieval, and proposing a new path towards generality in this area.

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“…The possibility of building indexing data structures for semantic search, which consists of the Keyword, Entity and Type Indices to support the process of retrieving both keyword-based and semantic information for annotated queries, was researched by [9]. In order to find an efficient indexing data structure, authors have explored both homogeneous and heterogeneous index types that use similar as well as dissimilar data structures for each of the index types, respectively.…”

Section: State Of the Artmentioning

confidence: 99%

LINDASearch: a faceted search system for linked open datasets

et al. 2019

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The importance of Linked Data lies on the fact that its practices and principles have been adopted by an increasing number of data providers, resulting in the creation of a data space on the Web containing billions of RDF Triples and accessible worldwide throughout the Internet. RDF datasets can be queried by tools and applications for searching and gathering information. However and due to the huge amount and types of dataset, the selection and reuse of data resources is not easy task. Therefore, a metasearch system for open Linked Data projects called LINDASearch (LINDASearch stands for Linked Data Search) is introduced. LINDASearch provides a middleware architecture in order to provide information about the most known Open Linked Data Projects such as DBpedia, The GeoNames geographical database, LinkedGeoData, FOAF profiles, Global Health Observatory, Linked Movie DataBase (LinkedMDB) and World Bank Linked Data. This paper describes the LINDASearch's architecture as well as its functionality through one case study divided in two scenarios in order to show the architecture's functionality and present the results obtained from each scenario.

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Efficient indexing for semantic search

Cited by 16 publications

References 23 publications

A Method for Improving Reasoning and Realization Problem Solving in Descriptive Logic- Based and Ontology-Based Reasoners

A Method for Improving Reasoning and Realization Problem Solving in Descriptive Logic- Based and Ontology-Based Reasoners

Graph-based entity-oriented search

LINDASearch: a faceted search system for linked open datasets

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