Syntactic Search Research Articles

Syntactic Code Search with Sequence-to-Tree Matching: Supporting Syntactic Search with Incomplete Code Fragments

Syntactic Code Search with Sequence-to-Tree Matching: Supporting Syntactic Search with Incomplete Code Fragments

Model of Explicit Knowledge Management in Organizational and Technical Systems

The technical component of knowledge management in organizational and technical systems in various fields, as well as in military formations and law enforcement agencies has not been investigated. In order to find the key characteristics of a comprehensive knowledge management model in organizational and technical systems, the main actions with knowledge and directions of implementation, realizing the recently developed knowledge management models, have been examined. There is provided to add a technical component to the model of explicit knowledge management in organizational and technical systems, implemented by the information and telecommunication system, the server of which is located, for example, in the research institution, the modeller or the educational institution. All components of the model of explicit knowledge management in organizational and technical systems accomplish acquiring of explicit knowledge from the knowledge management system in the organizational and technical systems or, if necessary, providing of explicit and implicit knowledge to the knowledge management system in organizational and technical systems. Implementation of the proposed explicit knowledge management model in organizational and technical systems is suggested to be implemented using the ontological approach of knowledge formalization and consideration. Thus, a combination of syntactic and semantic search is used, that is, the search is performed in instances of ontology, taking into account their semantic characteristic and connections.

Semantics-guided synthesis

This paper develops a new framework for program synthesis, called semantics-guided synthesis (SemGuS), that allows a user to provide both the syntax and the semantics for the constructs in the language. SemGuS accepts a recursively defined big-step semantics, which allows it, for example, to be used to specify and solve synthesis problems over an imperative programming language that may contain loops with unbounded behavior. The customizable nature of SemGuS also allows synthesis problems to be defined over a non-standard semantics, such as an abstract semantics. In addition to the SemGuS framework, we develop an algorithm for solving SemGuS problems that is capable of both synthesizing programs and proving unrealizability, by encoding a SemGuS problem as a proof search over Constrained Horn Clauses: in particular, our approach is the first that we are aware of that can prove unrealizabilty for synthesis problems that involve imperative programs with unbounded loops, over an infinite syntactic search space. We implemented the technique in a tool called MESSY, and applied it to SyGuS problems (i.e., over expressions), synthesis problems over an imperative programming language, and synthesis problems over regular expressions.

A recursive pattern recognition approach to selection web services in cloud environment

There are optimization problems in the Cloud for the selection of web services, due to the large number of services available by different cloud providers and the diversity of quality of service parameters of each of them. This work proposes the adaptation of a pattern recognition model based on the systematic functioning of the brain called Ar2p for the selection of web services in composition activities in Cloud environments. The web serice are represented as patterns to be recognized by Ar2p, which determines the necessary and sufficient web services that constitute the composition of services that meet its functional and non-functional requirements. The services composition and activity selection have been formalized through a logical-mathematical model of web service recognition mechanisms in two steps, one that describes the syntactic search of the service and the second, which offers filtering through quality of service parameters. An adaptive implementation of the final model allows its recognition modules to be provided with any desired optimization strategy.

Enhancing the Performance of Semantic Search in Bengali using Neural Net and other Classification Techniques

To know the information from the internet searching is one of the most important part for any user. In case of ‘Syntactic Search’ keyword based matching technique is used. Search accuracy is improved applying the filter like location, preference, user-history etc. However, it can happen that the user query or question and the best available answer or result in the internet domain has no terms in common or ignorable number of terms is common. In such case syntactic search cannot give the desired output. The role of ‘Semantic Search’ becomes prevalent in this scenario. The execution of semantic search faces challenge due to unavailability of resources like WordNet, Ontology, Annotation etc. An end to end algorithm is described to improve the accuracy of the semantic search in this work. Four classification techniques are used. They are ANN, Decision Tree, SVM and Naïve Bayes. Dataset is provided from the TDIL project of the Ministry of Electronics and IT, Govt. of India. The repository contains 86 categories of text having more than a million sentences. After getting the impressive result for the Bengali language test run was done for other Indian languages and a very good result is achieved. This research is extremely useful for the automatic question answering system, semantic similarity analysis, e-governance and m- governance.

Retrieval Interference in Syntactic Processing: The Case of Reflexive Binding in English.

It has been proposed that in online sentence comprehension the dependency between a reflexive pronoun such as himself/herself and its antecedent is resolved using exclusively syntactic constraints. Under this strictly syntactic search account, Principle A of the binding theory—which requires that the antecedent c-command the reflexive within the same clause that the reflexive occurs in—constrains the parser's search for an antecedent. The parser thus ignores candidate antecedents that might match agreement features of the reflexive (e.g., gender) but are ineligible as potential antecedents because they are in structurally illicit positions. An alternative possibility accords no special status to structural constraints: in addition to using Principle A, the parser also uses non-structural cues such as gender to access the antecedent. According to cue-based retrieval theories of memory (e.g., Lewis and Vasishth, 2005), the use of non-structural cues should result in increased retrieval times and occasional errors when candidates partially match the cues, even if the candidates are in structurally illicit positions. In this paper, we first show how the retrieval processes that underlie the reflexive binding are naturally realized in the Lewis and Vasishth (2005) model. We present the predictions of the model under the assumption that both structural and non-structural cues are used during retrieval, and provide a critical analysis of previous empirical studies that failed to find evidence for the use of non-structural cues, suggesting that these failures may be Type II errors. We use this analysis and the results of further modeling to motivate a new empirical design that we use in an eye tracking study. The results of this study confirm the key predictions of the model concerning the use of non-structural cues, and are inconsistent with the strictly syntactic search account. These results present a challenge for theories advocating the infallibility of the human parser in the case of reflexive resolution, and provide support for the inclusion of agreement features such as gender in the set of retrieval cues.

Ontology-Based Search of Genomic Metadata.

The Encyclopedia of DNA Elements (ENCODE) is a huge and still expanding public repository of more than 4,000 experiments and 25,000 data files, assembled by a large international consortium since 2007; unknown biological knowledge can be extracted from these huge and largely unexplored data, leading to data-driven genomic, transcriptomic, and epigenomic discoveries. Yet, search of relevant datasets for knowledge discovery is limitedly supported: metadata describing ENCODE datasets are quite simple and incomplete, and not described by a coherent underlying ontology. Here, we show how to overcome this limitation, by adopting an ENCODE metadata searching approach which uses high-quality ontological knowledge and state-of-the-art indexing technologies. Specifically, we developed S.O.S. GeM (http://www.bioinformatics.deib.polimi.it/SOSGeM/), a system supporting effective semantic search and retrieval of ENCODE datasets. First, we constructed a Semantic Knowledge Base by starting with concepts extracted from ENCODE metadata, matched to and expanded on biomedical ontologies integrated in the well-established Unified Medical Language System. We prove that this inference method is sound and complete. Then, we leveraged the Semantic Knowledge Base to semantically search ENCODE data from arbitrary biologists' queries. This allows correctly finding more datasets than those extracted by a purely syntactic search, as supported by the other available systems. We empirically show the relevance of found datasets to the biologists' queries.

Conceptual Search Based on Semantic Relatedness

Traditional search engines based on syntactic search are unable to solve key issues like synonymy and polysemy. Solving these issues leads to the invention of the semantic web. The semantic search engines indeed overcome these issues. Nowadays the most important part of the data remains unstructured documents. It is consequently very time consuming to annotate such big data. Concept based retrieval systems intend to manage directly unstructured documents. Semantic relationships are their main feature to extend syntactic search. In most of the methods implemented so far, concepts are used for both indexing and searching. Words remain the smallest unit to process semantic relatedness. The differences persist in the way that concepts are represented, mapped to each other, and managed for the sake of indexing and/or searching. Our approach is based on Wikipedia concepts. Concepts are represented as an undirected graph. Their semantic relatedness are computed with a distance derived from a semantic similarity measure. The same distance is used to calculate both semantic relatedness and query matching. DOI: http://dx.doi.org/10.11591/telkomnika.v12i8.5143

Discovering Latent Informaion By Spreading Activation Algorithm for Document Retrieval

Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do not contain the keywords but contain information related to the query are not retrieved. Spreading activation is an algorithm for finding latent information in a query by exploiting relations between nodes in an associative network or semantic network. However, the classical spreading activation algorithm uses all relations of a node in the network that will add unsuitable information into the query. In this paper, we propose a novel approach for semantic text search, called query-oriented-constrained spreading activation that only uses relations relating to the content of the query to find really related information. Experiments on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8% respectively better than the syntactic search and the search using the classical constrained spreading activation.

An improved semantic information searching scheme based multi-agent system and an innovative similarity measure

The key task for the web, namely, web searches, is evolving towards some novel form of semantic web search. In fact, most information retrieval systems are based on static vectors representations. Two major difficulties when a researcher uses current information retrieval systems are how to filter out irrelevant documents, and how to discover latest or more significant documents. Recently a very promising approach to semantic web search is based on combining standard web pages and search queries with ontological background knowledge. In this perspective we will describe a model to hold a document’s noise, and incompleteness. For this, we merge a syntactic keyword search with purely semantic search based domain ontology and a multi-agent system to solve such distributed problems. Then we perform a ranking algorithm on returned documents, and we propose a new semantic similarity measure between concepts based on the WordNet taxonomy structure.

Agent-based web search personalization approach using dynamic user profile

The World Wide Web has become the largest library through the history of the humanity. Having such a huge library made the search process more complex as the syntactic search engines offer an overwhelming amount of search results. Vocabulary problems like polysemy and synonymy can make the search results of traditional search engines irrelevant to users. Such problems trigger a strong need for personalizing the web search results based on user preferences. In this paper, we propose a new multi-agent system based approach for personalizing the web search results. The proposed approach introduces a model to build a user profile from initial and basic information, and maintain it through implicit user feedback to establish a complete, dynamic and up-to-date user profile. In the web search process, the model semantically optimizes the user query in two steps: query optimization using user profile preferences and query optimization using the WordNet ontology. The model builds on the advantages of the current search engines by utilizing them for retrieving the web search results. We present a detailed case study and simulation results evaluation to illustrate how the proposed model works and its expected value in increasing the precision of the traditional search engines and solving the vocabulary problems.

Semantic Discovery of Web Services through Social Learning

The increasing numbers of web services impose automatic discovery process in Service Oriented Architecture (SOA). But the existing SOA enables only syntactic discovery which produces coarse irrelevant results or sometimes no results. Different researches challenge this problem by introducing semantic discovery process in SOA to enable relevant and desired search results. These research outcomes cannot discover services effciently which are created independently with different knowledge bases. To overcome these problems, a new architecture of SOA is proposed which incorporates a new adaptive technique called social learning that improves service provider's domain ontology from service consumer's concept contributions and thus eventually makes the service more semantically discoverable. The proposed architecture contains new similarity measure and automatic merging algorithms on weighted ontology. From mathematical reasoning it is induced that the proposed architecture reduces overlapping concepts and thus more relevant discovery results are ensured. To test the proposed architecture's performance, a prototype of Universal Description Discovery and Integration (UDDI) is implemented and a simulation is conducted with real data set of OWL-S Technical Chart (OWLS-TC). About 67% noise responses from syntactic search (N-Gram String Distance algorithm) are reduced in the proposed architecture. The results also illustrate the proposed architecture's capabilities of concept learning and so about significant improvement in service discovery after a few social concept contributions.

An ontology-based search engine for protein-protein interactions.

BackgroundKeyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database.ResultsWe have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions.ConclusionRepresenting the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.