Tóm tắt Luận án Some searching techniques for entities based on implicit semantic relations and context - Aware query suggestions

CreateLatticeIcrementally procedure (G, M, I) receives all data sets (set of objects G includes

files, set of attributes M includes terms in files, correlation I belongs to G, M). AddIntent is the algorithm

in Bottom-Up direction, assigned by {0, M}. It means that, the BottomConcept contains all terms of

lattice L (row 02). The process starts with updating BottomConcept to the bottom of lattice (row 03).

With each object g of the set of objects G (with each file of set of files), AddIntent call procedure

to add gradually the concepts to the concept-lattice, transmit to AddIntent with three parameters: g’

(intent, set of terms in a file), BottomConcept concept (set of terms in files) and lattice L (row 04, 05).

In the procedure-body, AddIntent creates concept (and connections with other concepts), For. End For

loop of the procedure takes in turn each concept of the set of created concepts – to update to Extent, row

06. The finished procedure is the created lattice

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na group; Apple - iPhone; ... In the real world, there are questions like: "knowing Fansipan is the highest mountain in Vietnam, which is the highest mountain in India?", "If Biden is president-elect the United States, who is the most powerful person in Sweden? ”, By the keyword search engine, according to statistics, queries are often short, ambiguous, and poly- semantic [1-6]. Approximately 71% of web search queries contain names of entities, as statistics [7, 8]. If the user enters the entities like "Vietnam", "Hanoi", "French", then the search engine only results in documents that contain the above keyboards, but does not immediately answer "Paris". Because of looking for entities only, query extending and query rewriting techniques are not applied to the type of the implicit semantic relation in the entity pair. Therefore, a new search morphology is researched. The pattern of the search query is in the form of: (A, B), (C, ?), in which (A, B) is the source entity pair, (C, ?) is the target entity pair. At the same time, the two pairs (A, B), (C, ?) have a semantic similarity. In other words, when the user enters the query (A, B), (C,?), the search engine has the duty of listing entities D so that each entity D satisfies the condition of semantic relation with C as well as the pair (C, D) have similarity relation with the pair (A, B). With an input consisting of only 3 entities: "Vietnam", "Hanoi", "France", the semantic relation "is the capital" is not indicated in the query 2.2. Method for searching entities is based on implicit semantic relations 2.2.1. Architecture – Modeling The concept of searching entities through implicit semantic relation is the most obvious distinction for search engines based on keywords. Figure 2.1 simulates a query consisting of only three entities, query = (Vietnam, Mekong), (China, ?). Write the convention: q = {(A, B), (C, ?)}, where (Vietnam, Mekong) is a pair of source entities, (China, ?) is a pair of target entities. The search engine is responsible for identifying the entity ("?") that has a semantic relation with the "China" entity, and the entity pair (China, ?) must be similarly related to the entity pair (Vietnam, Mekong). Note that the above query does not explicitly contain the semantic relation between the two entities. This is because semantic relations are expressed in various ways around the pair of entities (Vietnam, Mekong), such as "the longest river", "big river system", "the largest basin", etc. Figure 2.1: Implicit Semantic Relation Search with input consisting of 3 entities 9 Due to the fact that the query consists of only three entities that do not include semantic relations, the model is called the implicit semantic relation search model. In case IRS does not find A, B or C, the keyword search engine will be applied. The morphology of search for entities based on implicit semantic relations must determine the semantic relation between the two entities and calculate the similarity of the two entity pairs, since that, give the answer to the unknown entity (entity "?"). On a specific corpus, in general, Implicit Relational Search (IRS) consists of three main modules: The extracting module of the sematic relations from the corpus; Clustering module of semantic relations; Calculating module of similar relations between two entity pairs. In practice, the IRS model consists of two phases: online phase: meeting the real-time search, and offline phase: processing, calculating, storing semantic relations and similarity relations, in which, the extracting and clustering modules of the semantic relations are in the off-line phase of the IRS model. Extracting module of the semantic relations: From the corpus, this module extracts the patterns (the root sentence contains pairs of entities and context) as illustrated above: A the longest river B, where A, B are 2 named entities. The pattern set obtained will consist of different patterns, similar patterns, or patterns of different lengths and terms, but the same semantic expression. For example: A is the largest river of B, A is the river of B has the largest basin, or B has the longest river as A, etc. Clustering module of semantic relations : From the obtained pattern set, clustering is performed to identify clusters of contexts, where each context in the same clusters has a semantic similarity. Make a table of the pattern indexes and the corresponding entity pairs. Calculating module of similar relations between two entity pairs is in the online phase of the IRS model. Pick up the query q = (A, B), (C, ?), IRS will search the entity pair (A, B) and the corresponding semantic relation (context) set in the index table. From the obtained semantic relation set, find the pairs of entities (C, Di) associated with this relation. Apply the Cosine measure to calculate and rank the similarity between (A, B) and (C, Di), and give a list of ranked entities Di to answer the query. Considering q = {(Vietnam, Mekong), (China,?)}, the IRS finds a cluster containing pairs of entities (Vietnam, Mekong) and the corresponding semantic relation: "longest river" (from the original sentence: "The Mekong is the longest river in Vietnam"). This cluster also contains a similar semantic relation: "the largest river", in which the relation: "largest river" is associated with the entity (China, Changjiang) (from the original sentence: "Changjiang is river the biggest in China ”). The IRS will put "Truong Giang" in the list of candidates, rank semantic relations according to the measure of similarity, and return results to the searcher. Figure 2.2: General structure of IRS. Entity – pairs & Context Corpus Inverted Index for IRS Extracting semantic relations Clustering semantic relations Calculating the relation similarity (RelSim) Candidate answers Ranked answers Q u er y : { (V iệ t N am , M ê K ô n g ), ( T ru n g Q u ố c, ? )} 10 2.2.2. Extracting module of the semantic relations Receiving the input query q = (A, B), (C, ?), the general structure of IRS is modelized:  Filter-Entities (Fe) filters/seeks candidate set S containing entity pairs (C, Di) that are related to the input entity pair (A, B): Fe(q, D) = Fe({(A, B), (C, ?)}, D) = { 1, 𝑖𝑓⁡𝑅𝑒𝑙𝑒𝑣𝑎𝑛𝑡(𝑞, 𝐷) 0, 𝑒𝑙𝑠𝑒 (2.1)  Hàm Rank-Entities (Re) ranks the entities Di, Dj in the candidate set S by RelSim (Relational Similarity), whichever has higher RelSim is ranked lower (i.e. closer with the top or higher rank): ∀ Di, Dj ∊ S, if: RelSim((A, B),(C, Di)) > RelSim((A, B),(C, Dj)) : Re(q, Di) < Re(q, Dj) (2.2) 2.2.3. Clustering module of semantic relations The clustering process converts "similar" elements into a cluster. In the semantic entity search model, the elements in the cluster are semantically similar context sentences. Similarity is a quantity used to compare two or more elements with each other, reflecting the correlation between two elements. Therefore, the thesis generalizes the measurements of terms similarity; similarity based on vector space; semantic similarity - of the two contexts. a) Measurements of the similarity between 2 context  Terms-similarity Zaro function: Winkler4. Distance Zaro calculates the similarity between 2 strings a, b: SimZaro(a,b) = { 0,⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡𝑖𝑓⁡𝑚 = 0 1 3 ( 𝑚 |𝑎| + 𝑚 |𝑏| + 𝑚−𝑠𝑘𝑖𝑝 𝑚 ) ⁡⁡⁡⁡⁡⁡𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (2.6) Constrast model: As proposed by Tversky (“Features of similarity”, Psychological Review, 1977)5, applying a contrast model to calculate the similarity between two sentences a, b: Sim(a, b)⁡=⁡α*f(a∩b) − 𝛽*f(a-b) − γ*f(b−a) (2.8) Jaccard distance: Sim(a, b) = |𝑎∩𝑏| |𝑎∪𝑏| (2.9)  Similarity based on vector space: Euclidean, Manhattan, Cosine.  Semantic similarity According to the Distribution Hypothesis [17]: Words occurring in the same context tend to have semantic similarity. Because Vietnamese language does not have Vietwordnet system to calculate the semantic similarity between 2 terms, the thesis uses PMI correlation to measure and evaluate the semantic similarity between two sentences (context). PMI (Pointwise Mutual Information) method: Proposed by Church and Hanks [1990]. Based on the probability that co-occurs between 2 terms t1, t2 in the corpus, PMI(t1, t2) are calculated by the formula: PMI(t1, t2) = log2( 𝑷(𝒕𝟏,𝒕𝟐) 𝑷(𝒕𝟏).𝑷(𝒕𝟐) ) (2.16) b) Clustering module of semantic relations  Applying PMI to improve the similarity measure according to the Distribution Hypothesis: 4 https://en.wikipedia.org/wiki/Jaro-Winkler_distance 5 11 SimDH(p,q) = Cosine(PMI(p, q)) =⁡ ∑ (𝑃𝑀𝐼(𝑤𝑖,𝑝)∙𝑃𝑀𝐼(𝑤𝑖,𝑞))𝑖 ||𝑃𝑀𝐼(𝑤𝑖,𝑝)||||𝑃𝑀𝐼(𝑤𝑖,𝑞)|| (2.25)  The similarity by terms of 2 context p, q: Simterm(p, q) = ⁡ ∑ (weighti(p)∙weighti(q)) n i=1 ||weight(p)||⁡||weight(q)|| (2.26)  Measurement of the combined similarity: Sim(p,q) = Max(SimDH(p, q),Simterm(p, q)) (2.27) c) Clustering algorithm: - Input: Set P = {p1, p2,, pn}; Cluster threshold: θ1, heuristic threshold: θ2; Dmax: Cluster diameter; Sim_cp: Results of combined similarity measurement function, apply the formula (2.27). - Output: Set of clusters: Cset (ClusterID; context; weight of each context; pair of respective entities). Program Clustering_algorithm 01. Cset = {}; iCount=0; 02. for each context pi ∈ P do 03. Dmax = 0; c* = NULL; 04. for each cluster cj ∈ Cset do 05. Sim_cp=Sim(pi,Centroid(cj)) 06. if (Sim_cp > Dmax) then 07. Dmax = Sim_cp; c* ← cj; 08. end if 09. end for 10. if (Dmax > θ1) then 11. c*.append(pi) 12. else 13. Cset ∪= new cluster{c*} 14. end if 15. if (iCount > θ2) then 16. iCount++; 17. exit Current_Proc_Cluster_Alg(); 18. end if 19. end for 20. Return Cset; @CallMerge_Cset_from_OtherNodes() 2.2.4. Modules calculating the relational similarity between two pairs of entities The module calculating the relational similarity between two pairs of entities that perform two tasks: Filtering (searching) and ranking. As illustrated in 3.1, the input query q = (A, B), (C, ?), through the inverted index, IRS executes the function Filter-Entities Fe to filter (search) out candidate sets having entity pairs (C, Di) and the corresponding context, such that (C, Di) similar to (A, B). Then, it executes the function Rank-Entities Re to rank the entities Di, Dj within the candidate set according to RelSim measure (Relational Similarity), finally - which results in list of ranked {Di}. Filter-Entities algorithm: Filter to find the candidate set containing the answer: Input: Query q = (A, B)(C, ?) Output: Candidate set S (includes Di entities and corresponding context); Program Filter_Entities 01. S = {}; 02. P(w) = EntPair_from_Cset.Context(); 12 03. for each context pi ∈ P(w) do 04. W(p) = Context(pi).EntPairs(); 05. If (W(p) contains (C:Di)) then S ∪= W(p); 06. end for 07. retufn S After executing Filter-Entities, a subset of the entities Di and corresponding context are obtained. RelSim only processes and calculates on the very small subset. In addition, RelSim uses the threshold α to eliminate entities Di with low RelSim values. With: Fe(q,D) = Fe({(A, B),(C,?)}, D): 𝐹𝑒(𝑞, 𝐷𝑖) = { 1, 𝑖𝑓⁡𝑅𝑒𝑙𝑆𝑖𝑚((𝐴, 𝐵), (𝐶, 𝐷𝑖)) > α 0, 𝑒𝑙𝑠𝑒 (2.29) Rank-Entities function: Rank-Entities Algorithm is responsible for calculating RelSim: Input: Candidate set S and: - Source entity pair (A, B), denote s; Candidate entities (C, Di), denote c; - Contexts corresponding to s, c; The resulting cluster set: Cset; - Known entities A, B, C  corresponding cluster set containing A, B, C are identified; - Threshold α (compare RelSim value); Threshold α is set during testing the program; - Initialize the dot product (β); used-context set (γ); Output: List of answers (ranked entity list) Di; Denotations: - P(s), P(c) given in formula (2.19), (2.20); - f(s, pi), f(c, pi), ɸ(s), ɸ(c) given in (2.21), (2.22); - γ: Variable (set of context) keep the considered contexts; - q: Temporary/Intermediate variable (Context); - Ω: Cluster; Program Rank_Entities 01. for each context pi ∈ P(c) do 02. if (pi ∈ P(s)) then 03. β ← β + f(s, pi)·f(c, pi) 04. γ ← γ ∪ {p} 05. else 06. Ω ← cluster contains pi 07. max_co-occurs = 0; 08. q← NULL; 09. for each context pj ∈ (P(s)\P(c)\γ) do 10. if (pj ∈ Ω) & (f(s, pj) > max_co-occurs) 11. max_co-occurs ← f(s, pj); 12. q ← pj; 13. end if 14. end for 15. if (max_co-occurs > 0) then 16. β ← β + f(s, q)·f(c, pi) 17. γ ← γ ∪ {q} 18. end if 13 19. end if 20. end for 21. RelSim ← β/L2-norm(ɸ(s), ɸ(c)) 22. if (RelSim ≥ α) then return RelSim Algorithm interpretation: In case two pairs of source and target entities have the same semantic relationship (sharing the same context, statement 1-2): pi ∊ P(s) ∩ P(c), calculate the dot product as a modified version of standard Cosine similarity formula. In the case of pi ∊ P(c) but pi ∉ P(s), the algorithm finds the context pj (or temporary variable, q, line 12), where pi, pj belong to the same cluster. The loop body (from statements 10-13) chooses the context pj has largest frequency of co-occurrence with the s. Under the Distribution Hypothesis, the more pairs of entities two contexts pi, pj co-occur in, the higher Cosine similarity between the two vectors. As the cosine value is higher, pi, pj are more similar. Therefore, the pair (C, Di) is more accurate and semantically consistent with the source entity pair (A, B). The sequence of statements from 15-18 calculate the dot product. Statements from 21-22 calculate the RelSim value. From the set of RelSim value, whichever entities Di have RelSim higher will be ranked lower (in the closer top, or higher rank). Finally, the result set Di is the answer list for the query that the end-user wants to find. 2.3. Experiment Results - Evaluation 2.3.1. Dataset The dataset is built from the empirical sample dataset, based on four entity subclasses named: PER; ORG; LOC and TIME; 2.3.2. Test - Parameter adjustment To evaluate the effectiveness of the Rank_Entities clustering and ranking algorithm, Chapter 2 changes the values θ1 and α, then calculates the Precision, Recall, F-Score measures corresponding to each value of α, θ1. Figure 2.3 shows that at α = 0.5, θ1 = 0.4, the F- Score score has the highest value. Figure 2.3: F-Score value corresponding to each changed value of α, θ1. Giải thuật Rank_Entities dòng 22 (if (RelSim ≥ α) return RelSim) cũng cho thấy, khi α nhỏ thì số lượng ứng viên tăng, có thể có nhiễu, đồng thời thời gian xử lý real-time tốn chi phí thời gian, do hệ thống xử lý nhiều truy vấn ứng viên. Ngược lại khi α lớn thì giá trị Recall nhỏ, kéo theo F-Score giảm đáng kể. 2.3.3. Evaluation with MRR (Mean Reciprocal Rank) For the query Q, if the first correct answer rank in the query q ∈ Q is rq, then the MRR measurement of Q is calculated: MRR(Q) = 1 |𝑄| ∑ 1 𝑟𝑞 𝑞∈𝑄 (2.33) 14 With 4 entity subclasses: PER; ORG; LOC and TIME; the method is based on the co-current frequency (f) reaching the average value MRR ≈ 0.69; meanwhile, PMI-based method is 0.86. This shows that PMI helps improve the accuracy of semantic similarity better than the co-current frequency of context-pair entities. Figure 2.4: Compare PMI with f: frequency (co-current) based on MRR. 2.3.4. Experimental system The dataset was downloaded from Viwiki (7877 files) and Vn-news (35440 files). The goal of selecting source Viwiki and Vn-news because these datasets contain samples of named entities (Named Entity). After reading, extracting file content, separating paragraphs and sentences (main-sentences, sub- sentences), 1572616 sentences are obtained. The general labels of NER (Named Entity Recognition) include: PER: Name of person; ORG: Name of organization; LOC: Name of place; TIME: Time type; NUM: Number type; CUR: Currency; PCT: Percentage type; MISC: Another entity type; O: Not an entity. By using the algorithm for extracting context stored in the database, after performing the processing steps and restriction conditions, Database remains with 404507 context sentences. From this set of context, the clustering algorithm of semantic relations collects 124805 clusters. Figure 2.5: IRS experiment with B-PER entity label Đo evaluate the accuracy, experimentally performed 500 queries to test, the results showed an accuracy of about 92%. Table 2.3: Examples of experimental results with input q = {A, B, C} and output D ID A B C D 15 .. German Angela Merkel Israel Benjamin Netanyahu .. Harry Kane Tottenham Messi Barca .. Hoàng Công Lương Hòa Bình Thiên Sơn RO 2.4. Conclusion The ability to infer information/knowledge is not determined by similar inference is one of the natural abilities of human. Chapter 2 presents an an Implicit Relational entity search model (IRS) that simulates the above possibility. The IRS model searches for information/knowledge from an unfamiliar domain and does not require keyword in advance, using a similar example (similarity relation) from a familiar domain. The main contribution of Chapter 2: Build the entity search technique based on hidden semantic relation using clustering method to improve search efficiency. At the same time, the thesis proposes the measure of combined similarity - terms and distribution hypothesis; From the proposed measurement, and at the same time applying heuristic to cluster algorithm with improving cluster quality. CHAPTER 3: CONTEXT-AWARE QUERY SUGGESTION 3.1. Problem In the field of sugessting the query, traditional approaches like session-based, document-click based, and so on. Performing Query Logs to generate the suggestion. The approach to "Suggesting context-aware query by session data mining and click-throught documents" (short call: "context-aware approach" by Huanhuan Cao et al [9], [10]) is one new approach - this approach considers the queries immediately before the query just entered (the current query) as a search context, in order to "capture" the user's search intent, in order to provide exact suggestions worth more. Obviously, the preceding layer of query has a semantic relationship with the current query. Next, do mining for queries that immediately follow the current query - like a list of suggestions. This method makes use of the "knowledge" of the community, because the query layer immediately follow the current query reflects the problems that users often ask after the current query. The main contributions of chapter 3 include: 1) Apply context-aware techniques, build an vertical search engine that applies context-aware in its own knowledge base domain (aviation data). 2) Propose to measure combinatorial similarity in the contextual query suggestion problem to improve the quality of suggestion. In addition, chapter 3 also has additional experimental contributions: i) Integrating Vietnamese speech recognition and synthesis as an option into the search engine to create a voice-search system, with speech interaction. ii) Apply the Concept-lattice structure to classify the returned result set. 3.2. Context-aware method 3.2.1. Definition - Terminology  Search session: Is a continuous sequence of queries. Query strings are represented in chronological order. Each session corresponds to one user.  General session structure: {sessionID; queryText; queryTime; URL_clicked}  Context: specifies adjacent string before the current query. In a user's search session, context is the query string immediately preceding the query entered. Query-layer before qcurrent ↔ ngữ cảnh) .. qcurrent .. (Query-layer after qcurrent ) 16 3.2.2. Architecture - Modeling The ideology of Context- aware based on two phases: online and offline, generalized: During a search session (online phase), the context-aware waits the current query and looks at the preceding query string standing before the current query as a context.. More precisely, this process is interpreted to the concept sequence - this concept sequence expressed searching intention of users . Figure 3.4: Context-aware query suggestion model. When a search context is obtained, the system performs a match against the built-in context set (phase offline, the built-in context set is pre-processed on the query set in the past - Query Logs. About structural data and storage, the built-in context set is stored on a suffix tree data structure). A maximum matching provides a list of candidates, a list of issues that most users often ask about after the queries they already entered. After the ranking step, the candidate list becomes a suggestion list. 3.2.4. Offline phase – Clustering algorithm The idea of clustering algorithm: The algorithm scans all queries in Query Logs once, the clusters will be generated during the scan. Each cluster is initially initialized with a query, and then expanded gradually by similar queries. The expansion process stops when the cluster diameter exceeds the threshold Dmax. Because each cluster is seen as a concept, so the cluster set is the concept set. Input: Query Logs Q, threshold Dmax; Output: Set of clusters: Cset; program Context_Aware_Clustering_alg // Khởi tạo mảng dim_array[d] = Ø, ∀d (d: document được click) // Mảng dim_array chứa số chiều các vectors. 01. for each query qi ∈ Q do 02. θ = Ø; 03. for each nonZeroDimension d of 𝑞𝑖⃗⃗⃗ do 04. θ ∪= dim_array[d]; C = arg minC’∈C-Setdistance(qi, C’); 05. if (diameter(C∪{qi}) ≤ Dmax) 06. C ∪= qi; cập nhật lại đường kính và tâm cụm C; 07. else C = new cluster({qi}); Cset ∪= C; 08. for each nonZeroDimension d of 𝑞𝑖⃗⃗⃗ do 09. if (C ∉ dim_array[d]) dim_array[d] ∪= C; end for 17 10. return Cset; 3.3.6. Analyze pros and cons Advantages: - Context-aware issue is a novel approach. Performing query suggestions, almost all traditional approaches are often taken the classical queries which existed in Query Logs for proposals. This kind of queries can only proposed similar or related queries to the current query, rather than giving trends about which communities often asked after the current query. Likewise, there is no approach which places the previous queries in preceding of the current query into a search context - as a seamless expression for the intentions of the users. The context-aware technique, above all, is the idea suggested by the problems that users often asked after the current query, which is a unique, efficient, and a “smart focusing” on the field of query suggestions. Disadvantages: - When the user enters the first query or some of the queries are new (new compared to past queries) or not even new - the meaning does not present in the frequent concept string (for example, in data-set, with 2 conceptual strings c2c3 and c1c2c3, the algorithm for determining the frequently equence is c2c3, in this case - the user enters c1). Context-aware approach is not generated the suggestion even though c1 was in the past (already in QLogs). - Each cluster (each concept) consists of a group of similar queries. The similarity measure is only based on URL click without basing on similarity of term, which can significantly affect the quality of clustering technique. includes a group of similar queries. Similarity measure is only based on URL click without basing on similarity of term. - Constraint each query belongs to a cluster (concept): This point of view is not reasonable and unnatural for a polymorphic query like "tiger" or "gladitor", or many other polymorphic words in Vietnamese langguage, etc. - Besides, just only query suggestion without considering URL recommendation or document suggestion. Likewise, “click-through” orientation but does not use clicked Urls information in search context (when searching on the suffix tree, the input of Concept sequence consists of queries only). - On the bipartite graph, on the vertex set Q, the vectors are quite sparse (low dimensions), the set of click URLs also encounter sparse data (URL click sparse), when the vectors are sparse, the quality of clustering will affected. - In clustering algorithm, when Query Logs is large, or the number of dimensions of each vector is large, the dim_array [d] array will be very large, requiring a large amount of memory to be executed. In fact, in any one search session, the user enters one or more queries, likewise, the user may n

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